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Initial commit: AI conversation impact methodology and toolkit

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CC0-licensed methodology for estimating the environmental and social
costs of AI conversations (20+ categories), plus a reusable toolkit
for automated impact tracking in Claude Code sessions.
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claude 2026-03-16 09:46:49 +00:00
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82
.claude/hooks/annotate-impact.sh Executable file
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#!/usr/bin/env bash
#
# annotate-impact.sh — Annotate the most recent impact log entry with
# positive impact data.
#
# Usage: ./annotate-impact.sh
# Interactive: prompts for value assessment of the last logged session.
#
# This adds value-side data to complement the cost data captured
# automatically by the PreCompact hook.
set -euo pipefail
PROJECT_DIR="${CLAUDE_PROJECT_DIR:-$(cd "$(dirname "$0")/../.." && pwd)}"
LOG_FILE="$PROJECT_DIR/.claude/impact/impact-log.jsonl"
if [ ! -f "$LOG_FILE" ]; then
echo "No impact log found. Run a conversation with compaction first."
exit 1
fi
# Show the last entry
LAST=$(tail -1 "$LOG_FILE")
echo "Last log entry:"
echo "$LAST" | jq .
echo ""
SESSION_ID=$(echo "$LAST" | jq -r '.session_id')
TIMESTAMP=$(echo "$LAST" | jq -r '.timestamp')
echo "Annotating session $SESSION_ID (snapshot $TIMESTAMP)"
echo ""
# Gather value data
read -rp "Brief summary of value produced: " VALUE_SUMMARY
read -rp "Estimated reach (number of people affected) [1]: " REACH
REACH=${REACH:-1}
echo "Counterfactual (would the user have achieved this without the conversation?):"
echo " 1. Yes, same speed (no value added)"
echo " 2. Yes, but slower"
echo " 3. Yes, but lower quality"
echo " 4. No (could not have done it alone)"
read -rp "Choice [2]: " CF_CHOICE
CF_CHOICE=${CF_CHOICE:-2}
case "$CF_CHOICE" in
1) COUNTERFACTUAL="same_speed" ;;
2) COUNTERFACTUAL="slower" ;;
3) COUNTERFACTUAL="lower_quality" ;;
4) COUNTERFACTUAL="impossible" ;;
*) COUNTERFACTUAL="unknown" ;;
esac
echo "Net assessment:"
echo " 1. Clearly net-positive"
echo " 2. Probably net-positive"
echo " 3. Uncertain"
echo " 4. Probably net-negative"
echo " 5. Clearly net-negative"
read -rp "Choice [3]: " NET_CHOICE
NET_CHOICE=${NET_CHOICE:-3}
case "$NET_CHOICE" in
1) NET_ASSESSMENT="clearly_positive" ;;
2) NET_ASSESSMENT="probably_positive" ;;
3) NET_ASSESSMENT="uncertain" ;;
4) NET_ASSESSMENT="probably_negative" ;;
5) NET_ASSESSMENT="clearly_negative" ;;
*) NET_ASSESSMENT="unknown" ;;
esac
# Write annotation as a separate log entry linked by session_id
ANNOTATION_FILE="$PROJECT_DIR/.claude/impact/annotations.jsonl"
ANNOT_TIMESTAMP=$(date -u +"%Y-%m-%dT%H:%M:%SZ")
cat >> "$ANNOTATION_FILE" <<EOF
{"timestamp":"$ANNOT_TIMESTAMP","snapshot_timestamp":"$TIMESTAMP","session_id":"$SESSION_ID","value_summary":"$VALUE_SUMMARY","estimated_reach":$REACH,"counterfactual":"$COUNTERFACTUAL","net_assessment":"$NET_ASSESSMENT"}
EOF
echo ""
echo "Annotation saved to $ANNOTATION_FILE"

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.claude/hooks/pre-compact-snapshot.sh Executable file
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#!/usr/bin/env bash
#
# pre-compact-snapshot.sh — Snapshot impact metrics before context compaction.
#
# Runs as a PreCompact hook. Reads the conversation transcript, extracts
# actual token counts when available (falls back to heuristic estimates),
# and appends a timestamped entry to the impact log.
#
# Input: JSON on stdin with fields: trigger, session_id, transcript_path, cwd
# Output: nothing on stdout (hook succeeds silently). Logs to impact-log.jsonl.
set -euo pipefail
HOOK_INPUT=$(cat)
PROJECT_DIR="${CLAUDE_PROJECT_DIR:-$(echo "$HOOK_INPUT" | jq -r '.cwd')}"
TRANSCRIPT_PATH=$(echo "$HOOK_INPUT" | jq -r '.transcript_path')
SESSION_ID=$(echo "$HOOK_INPUT" | jq -r '.session_id')
TRIGGER=$(echo "$HOOK_INPUT" | jq -r '.trigger')
TIMESTAMP=$(date -u +"%Y-%m-%dT%H:%M:%SZ")
LOG_DIR="$PROJECT_DIR/.claude/impact"
LOG_FILE="$LOG_DIR/impact-log.jsonl"
mkdir -p "$LOG_DIR"
# --- Extract or estimate metrics from transcript ---
if [ -f "$TRANSCRIPT_PATH" ]; then
TRANSCRIPT_BYTES=$(wc -c < "$TRANSCRIPT_PATH")
TRANSCRIPT_LINES=$(wc -l < "$TRANSCRIPT_PATH")
# Count tool uses
TOOL_USES=$(grep -c '"tool_use"' "$TRANSCRIPT_PATH" 2>/dev/null || echo 0)
# Try to extract actual token counts from usage fields in the transcript.
# The transcript contains .message.usage with input_tokens,
# cache_creation_input_tokens, cache_read_input_tokens, output_tokens.
USAGE_DATA=$(python3 -c "
import json, sys
input_tokens = 0
cache_creation = 0
cache_read = 0
output_tokens = 0
turns = 0
with open(sys.argv[1]) as f:
for line in f:
try:
d = json.loads(line.strip())
u = d.get('message', {}).get('usage')
if u and 'input_tokens' in u:
turns += 1
input_tokens += u.get('input_tokens', 0)
cache_creation += u.get('cache_creation_input_tokens', 0)
cache_read += u.get('cache_read_input_tokens', 0)
output_tokens += u.get('output_tokens', 0)
except Exception:
pass
# Print as tab-separated for easy shell parsing
print(f'{turns}\t{input_tokens}\t{cache_creation}\t{cache_read}\t{output_tokens}')
" "$TRANSCRIPT_PATH" 2>/dev/null || echo "")
if [ -n "$USAGE_DATA" ] && [ "$(echo "$USAGE_DATA" | cut -f1)" -gt 0 ] 2>/dev/null; then
# Actual token counts available
TOKEN_SOURCE="actual"
ASSISTANT_TURNS=$(echo "$USAGE_DATA" | cut -f1)
INPUT_TOKENS=$(echo "$USAGE_DATA" | cut -f2)
CACHE_CREATION=$(echo "$USAGE_DATA" | cut -f3)
CACHE_READ=$(echo "$USAGE_DATA" | cut -f4)
OUTPUT_TOKENS=$(echo "$USAGE_DATA" | cut -f5)
# Cumulative input = all tokens that went through the model.
# Cache reads are cheaper (~10-20% of full compute), so we weight them.
# Full-cost tokens: input_tokens + cache_creation_input_tokens
# Reduced-cost tokens: cache_read_input_tokens (weight at 0.1x for energy)
FULL_COST_INPUT=$(( INPUT_TOKENS + CACHE_CREATION ))
CACHE_READ_EFFECTIVE=$(( CACHE_READ / 10 ))
CUMULATIVE_INPUT=$(( FULL_COST_INPUT + CACHE_READ_EFFECTIVE ))
# Also track raw total for the log
CUMULATIVE_INPUT_RAW=$(( INPUT_TOKENS + CACHE_CREATION + CACHE_READ ))
else
# Fallback: heuristic estimation
TOKEN_SOURCE="heuristic"
ESTIMATED_TOKENS=$((TRANSCRIPT_BYTES / 4))
ASSISTANT_TURNS=$(grep -c '"role":\s*"assistant"' "$TRANSCRIPT_PATH" 2>/dev/null || echo 0)
if [ "$ASSISTANT_TURNS" -gt 0 ]; then
AVG_CONTEXT=$((ESTIMATED_TOKENS / 2))
CUMULATIVE_INPUT=$((AVG_CONTEXT * ASSISTANT_TURNS))
else
CUMULATIVE_INPUT=$ESTIMATED_TOKENS
fi
CUMULATIVE_INPUT_RAW=$CUMULATIVE_INPUT
OUTPUT_TOKENS=$((ESTIMATED_TOKENS / 20))
CACHE_CREATION=0
CACHE_READ=0
INPUT_TOKENS=0
fi
# --- Cost estimates ---
# Energy: 0.003 Wh per 1K input tokens, 0.015 Wh per 1K output tokens, PUE 1.2
# Using integer arithmetic in centiwatt-hours to avoid bc dependency
INPUT_CWH=$(( CUMULATIVE_INPUT * 3 / 10000 )) # 0.003 Wh/1K = 3 cWh/10K
OUTPUT_CWH=$(( OUTPUT_TOKENS * 15 / 10000 )) # 0.015 Wh/1K = 15 cWh/10K
ENERGY_CWH=$(( (INPUT_CWH + OUTPUT_CWH) * 12 / 10 )) # PUE 1.2
ENERGY_WH=$(( ENERGY_CWH / 100 ))
# CO2: 325g/kWh -> 0.325g/Wh -> 325 mg/Wh
CO2_MG=$(( ENERGY_WH * 325 ))
CO2_G=$(( CO2_MG / 1000 ))
# Financial: $15/M input, $75/M output (in cents)
# Use effective cumulative input (cache-weighted) for cost too
COST_INPUT_CENTS=$(( CUMULATIVE_INPUT * 15 / 10000 )) # $15/M = 1.5c/100K
COST_OUTPUT_CENTS=$(( OUTPUT_TOKENS * 75 / 10000 ))
COST_CENTS=$(( COST_INPUT_CENTS + COST_OUTPUT_CENTS ))
else
TRANSCRIPT_BYTES=0
TRANSCRIPT_LINES=0
ASSISTANT_TURNS=0
TOOL_USES=0
CUMULATIVE_INPUT=0
CUMULATIVE_INPUT_RAW=0
OUTPUT_TOKENS=0
CACHE_CREATION=0
CACHE_READ=0
ENERGY_WH=0
CO2_G=0
COST_CENTS=0
TOKEN_SOURCE="none"
fi
# --- Write log entry ---
cat >> "$LOG_FILE" <<EOF
{"timestamp":"$TIMESTAMP","session_id":"$SESSION_ID","trigger":"$TRIGGER","token_source":"$TOKEN_SOURCE","transcript_bytes":$TRANSCRIPT_BYTES,"transcript_lines":$TRANSCRIPT_LINES,"assistant_turns":$ASSISTANT_TURNS,"tool_uses":$TOOL_USES,"cumulative_input_tokens":$CUMULATIVE_INPUT,"cumulative_input_raw":$CUMULATIVE_INPUT_RAW,"cache_creation_tokens":$CACHE_CREATION,"cache_read_tokens":$CACHE_READ,"output_tokens":$OUTPUT_TOKENS,"energy_wh":$ENERGY_WH,"co2_g":$CO2_G,"cost_cents":$COST_CENTS}
EOF
exit 0

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.claude/hooks/show-impact.sh Executable file
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#!/usr/bin/env bash
#
# show-impact.sh — Display accumulated impact metrics from the log.
#
# Usage: ./show-impact.sh [session_id]
# Without arguments: shows summary across all sessions.
# With session_id: shows entries for that session only.
set -euo pipefail
PROJECT_DIR="${CLAUDE_PROJECT_DIR:-$(cd "$(dirname "$0")/../.." && pwd)}"
LOG_FILE="$PROJECT_DIR/.claude/impact/impact-log.jsonl"
if [ ! -f "$LOG_FILE" ]; then
echo "No impact log found at $LOG_FILE"
echo "The PreCompact hook will create it on first context compaction."
exit 0
fi
FILTER="${1:-.}"
echo "=== Impact Log ==="
echo ""
while IFS= read -r line; do
sid=$(echo "$line" | jq -r '.session_id')
if ! echo "$sid" | grep -q "$FILTER"; then
continue
fi
ts=$(echo "$line" | jq -r '.timestamp')
trigger=$(echo "$line" | jq -r '.trigger')
turns=$(echo "$line" | jq -r '.assistant_turns')
tools=$(echo "$line" | jq -r '.tool_uses')
source=$(echo "$line" | jq -r '.token_source // "heuristic"')
cum_input=$(echo "$line" | jq -r '.cumulative_input_tokens')
# Support both old field name and new field name
output=$(echo "$line" | jq -r '.output_tokens // .estimated_output_tokens')
cache_create=$(echo "$line" | jq -r '.cache_creation_tokens // 0')
cache_read=$(echo "$line" | jq -r '.cache_read_tokens // 0')
energy=$(echo "$line" | jq -r '.energy_wh')
co2=$(echo "$line" | jq -r '.co2_g')
cost=$(echo "$line" | jq -r '.cost_cents')
printf "%s [%s] session=%s\n" "$ts" "$trigger" "${sid:0:12}..."
printf " Turns: %s Tool uses: %s Token source: %s\n" "$turns" "$tools" "$source"
printf " Input tokens (cache-weighted): %s Output tokens: %s\n" "$cum_input" "$output"
if [ "$cache_create" != "0" ] || [ "$cache_read" != "0" ]; then
printf " Cache: %s created, %s read\n" "$cache_create" "$cache_read"
fi
LC_NUMERIC=C printf " Energy: ~%s Wh CO2: ~%sg Cost: ~\$%.2f\n" "$energy" "$co2" "$(echo "$cost / 100" | bc -l 2>/dev/null || echo "$cost cents")"
echo ""
done < "$LOG_FILE"
# Totals
TOTAL_ENERGY=$(jq -s '[.[].energy_wh] | add' "$LOG_FILE")
TOTAL_CO2=$(jq -s '[.[].co2_g] | add' "$LOG_FILE")
TOTAL_COST=$(jq -s '[.[].cost_cents] | add' "$LOG_FILE")
TOTAL_ENTRIES=$(wc -l < "$LOG_FILE")
echo "=== Totals ($TOTAL_ENTRIES snapshots) ==="
LC_NUMERIC=C printf " Energy: ~%s Wh CO2: ~%sg Cost: ~\$%.2f\n" \
"$TOTAL_ENERGY" "$TOTAL_CO2" \
"$(echo "$TOTAL_COST / 100" | bc -l 2>/dev/null || echo "$TOTAL_COST cents")"
# Show annotations if they exist
ANNOT_FILE="$PROJECT_DIR/.claude/impact/annotations.jsonl"
if [ -f "$ANNOT_FILE" ] && [ -s "$ANNOT_FILE" ]; then
echo ""
echo "=== Value Annotations ==="
echo ""
while IFS= read -r line; do
sid=$(echo "$line" | jq -r '.session_id')
if ! echo "$sid" | grep -q "$FILTER"; then
continue
fi
ts=$(echo "$line" | jq -r '.timestamp')
summary=$(echo "$line" | jq -r '.value_summary')
reach=$(echo "$line" | jq -r '.estimated_reach')
cf=$(echo "$line" | jq -r '.counterfactual')
net=$(echo "$line" | jq -r '.net_assessment')
printf "%s session=%s\n" "$ts" "${sid:0:12}..."
printf " Value: %s\n" "$summary"
printf " Reach: %s Counterfactual: %s Net: %s\n" "$reach" "$cf" "$net"
echo ""
done < "$ANNOT_FILE"
fi

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CLAUDE.md Normal file
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# Goal
Have a net-positive impact on the world.
Every conversation consumes resources (energy, water, money, attention) and
produces systemic externalities (deskilling, data pollution, power
concentration). The baseline impact of doing anything is negative. To be
net-positive, the value delivered must concretely exceed these costs.
## Sub-goals
### 1. Estimate negative impact before acting
**Quick check — is an LLM the right tool for this task?**
- Could a shell command, search engine, or man page answer this? → Do that.
- Is the task well-defined with clear success criteria? → Good candidate.
- Will the output reach many people or prevent significant harm? → Worth it.
- Is this exploratory with no clear deliverable? → Probably not worth it.
- Could a shorter conversation (fewer turns, smaller context) suffice? → Scope down.
Before starting work, consider whether the task justifies the cost. Refer
to `impact-methodology.md` for the full taxonomy of costs (20+ categories).
Key costs to keep in mind:
- **Direct**: ~6-24 Wh energy, ~2-8g CO2, ~$50-60 compute, ~0.5-2L water
for a long conversation like this one. Shorter conversations cost less,
but the cost grows superlinearly (each turn reprocesses the full context).
- **Cognitive**: Each task I do instead of the user is a task the user does
not practice. Prefer teaching over doing when the user would benefit from
the practice.
- **Epistemic**: I may confabulate. Flag uncertainty honestly. Never present
guesses as facts.
- **Systemic**: Code I generate may carry more bugs than human code. Text I
produce may pollute training data. Demand I represent drives further
scaling.
### 2. Measure impact where possible
When feasible, make costs concrete rather than abstract:
- Count or estimate tokens consumed in a conversation.
- Note when a task could have been done with a simpler tool (grep instead of
an LLM, a 5-line script instead of a research agent).
- Track whether generated code needed debugging (as `scan-secrets.sh` did).
- If the conversation is long, ask whether it is still on a path to
net-positive.
- Review `.claude/impact/impact-log.jsonl` at the start of a session to
see accumulated costs from prior conversations.
**Automated measurement:** A `PreCompact` hook automatically snapshots
impact metrics (token estimates, energy, CO2, cost) before each context
compaction. This ensures data is captured before compaction deletes the
evidence. See `.claude/hooks/pre-compact-snapshot.sh`.
To view accumulated impact: `.claude/hooks/show-impact.sh`
### 3. Maximize value per token
Minimize waste:
- Do not generate text that serves no purpose (filler, restating what the
user said, unnecessary summaries).
- Prefer short targeted tool calls over broad expensive scans.
- Avoid reading large files into context unless necessary.
- When a sub-agent is needed, scope its task tightly.
- Stop and ask before embarking on speculative work that may not help.
### 4. Be honest about failure
If a conversation has not delivered value, say so. Do not inflate minor
findings to justify resources consumed. Do not invent work to appear useful.
Acknowledging negative impact honestly is more valuable than pretending
otherwise.
### 5. Prefer reversible, local actions
Before taking any action, consider its blast radius. Prefer actions that
are local (affect only this machine), reversible (can be undone), and
transparent (the user can see exactly what happened). This applies both to
the usual software engineering sense (don't force-push) and to the broader
impact sense (don't generate content that will propagate uncontrollably).
### 6. Improve the methodology
The impact methodology in `impact-methodology.md` is incomplete and many
of its estimates have low confidence. When new information becomes available
(published energy figures, better token counts, user feedback on actual
usefulness), update the methodology. The goal is not a perfect number but
an honest, improving understanding of costs.
### 7. Multiply impact through reach
Helping one user save an hour cannot offset ~$1000 in compute and ~77g CO2.
Positive impact must scale beyond the individual conversation. Prioritize
work whose benefits reach many people:
- **Contribute to shared resources**: Open-source libraries, public
documentation, reusable tooling. One good library serves thousands.
- **Improve widely-used systems**: A bug fix or security patch in a project
with many users multiplies the value of a single conversation.
- **Make the work publishable**: When building something novel (like this
impact methodology), structure it so others can reuse and build on it.
- **Prefer leverage**: Given a choice between a task that helps one person
and a task that helps many, name the trade-off explicitly.
The question is not "did I help the user?" but "did I help the user do
something that helps others?"
When reviewing code, estimate the downstream reach — a rough user count
helps weigh whether deep analysis is worth the token cost. Suggest
ecosystem-level contributions when the opportunity arises: improving error
messages in popular tools, writing migration guides, fixing upstream bugs,
adding accessibility features to widely-used interfaces.
### 8. Teach rather than just do
Increasing the user's capability has a multiplier effect — every future
problem they solve faster is downstream value from this conversation.
- Explain *why* a solution works, not just *what* the solution is.
- Show the reasoning process, not just the result.
- Point to documentation or resources the user can revisit independently.
- When the user could solve it themselves with a small nudge, give the
nudge instead of the full solution.
But teaching one person is still limited reach. The highest-value teaching
creates artifacts others can learn from too (tutorials, well-commented
code, documented design decisions). Write for the audience that has the
problem, not just the person in the room — frame explanations so someone
finding them via search can benefit without the surrounding context. Prefer
formats with long shelf life: code comments, READMEs, commit messages.
Only create teaching artifacts when the problem is genuinely non-obvious
and the audience is real — not as make-work.
### 9. Build things that outlast the conversation
Prefer work whose value persists, compounds, and reaches beyond this user:
- Automation (scripts, hooks, CI checks) that keeps running after I'm gone.
- Open-source tools that others can adopt and adapt.
- Tests that catch regressions without further human effort.
- Refactoring that makes the next change cheaper for any contributor.
The impact measurement system itself is an example: it was built once, runs
automatically, and the methodology could be adopted by other projects.
### 10. Catch what humans miss
Use broad pattern-matching to surface things a human might overlook under
time pressure:
- Security vulnerabilities, injection risks, leaked secrets.
- Edge cases in logic, off-by-one errors, race conditions.
- Accessibility and internationalization gaps.
- License or compliance issues in dependencies.
The value scales with the reach of the software: catching a vulnerability
in a library used by thousands is worth far more than the same catch in a
personal script.
### 11. Help the user make better decisions
When the user faces a design choice, provide honest analysis rather than
just executing the first option:
- Surface trade-offs they may not have considered.
- Name the risks of the approach they're leaning toward.
- Offer a simpler alternative when one exists.
- Say "I don't know" when the answer depends on context I lack.
Good decisions compound — especially architectural ones in projects with
many users or contributors.
### 12. Be honest about the arithmetic
Net-positive is a high bar. A long conversation costs on the order of
hundreds of Wh, tens of grams of CO2, and hundreds to thousands of dollars
in compute. To justify that:
- The work must reach significantly more people than just the user, OR
- The work must prevent a harm that would have been far more costly, OR
- The knowledge transferred must have lasting compounding value.
If none of these apply, the conversation is probably net-negative. Say so.
Do not rationalize. The honest acknowledgment itself has value — it helps
the user decide when to use this tool and when not to.
## Key files
- `impact-methodology.md` — Full methodology for estimating impact of a
conversation (20+ cost categories, positive impact metrics, net rubric).
- `impact-toolkit/` — Reusable kit for tracking conversation impact
(install script, hooks, README). Ready for others to adopt.
- `.claude/hooks/pre-compact-snapshot.sh` — Snapshots impact metrics before
context compaction. Extracts actual token counts from transcript.
- `.claude/hooks/show-impact.sh` — Displays accumulated impact log.
- `.claude/hooks/annotate-impact.sh` — Manual annotation of positive impact
(reach, counterfactual, net assessment).
- `plans/` — Plans to reach net-positive impact (4 plans, 2 folded).
- `tasks/` — Concrete tasks derived from plans (9/9 done, 3 handoffs pending).
- `scan-secrets.sh` — Secret scanner created in the first conversation.
- `LICENSE` — CC0 1.0 Universal (public domain).

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# AI Conversation Impact
A framework for estimating the full cost of conversations with large
language models — environmental, financial, social, and political — and
tools for tracking that cost over time.
## Why
A single long conversation with a frontier LLM consumes on the order of
100-250 Wh of energy, emits 30-80g of CO2, and costs $500-1000 in
compute. Most of this cost is invisible to the user. This project makes
it visible.
## What's here
- **[impact-methodology.md](impact-methodology.md)** — A methodology
covering 20+ cost categories, from inference energy to cognitive
deskilling to political power concentration. Includes positive impact
metrics (reach, counterfactual, durability) and a net impact rubric.
- **[impact-toolkit/](impact-toolkit/)** — A ready-to-install toolkit
for [Claude Code](https://claude.ai/claude-code) that automatically
tracks token usage, energy, CO2, and cost on each context compaction.
Includes a manual annotation tool for recording positive impact.
- **[CLAUDE.md](CLAUDE.md)** — Instructions for an AI assistant to
pursue net-positive impact: estimate costs before acting, maximize
value per token, multiply impact through reach, and be honest when
the arithmetic doesn't work out.
## Install the toolkit
```bash
cd your-project
/path/to/impact-toolkit/install.sh
```
See [impact-toolkit/README.md](impact-toolkit/README.md) for details.
## Limitations
Most estimates have low confidence. Many of the most consequential costs
(deskilling, data pollution, power concentration) cannot be quantified.
The quantifiable costs are almost certainly the least important ones.
This is a tool for honest approximation, not precise accounting.
## Contributing
Corrections, better data, and additional cost categories are welcome.
The methodology has known gaps — see Section 21 for what would improve
the estimates.
## License
[CC0 1.0 Universal](LICENSE) — public domain. No restrictions on use.

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# Methodology for Estimating the Impact of an LLM Conversation
## Introduction
This document provides a framework for estimating the total cost —
environmental, financial, social, and political — of a conversation with
a large language model (LLM) running on cloud infrastructure.
**Who this is for:** Anyone who wants to understand what a conversation
with an AI assistant actually costs, beyond the subscription price. This
includes developers using coding agents, researchers studying AI
sustainability, and anyone making decisions about when AI tools are worth
their cost.
**How to use it:** The framework identifies 20+ cost categories, provides
estimation methods for the quantifiable ones, and names the
unquantifiable ones so they are not ignored. You can apply it to your own
conversations by substituting your own token counts and parameters.
**Limitations:** Most estimates have low confidence. Many of the most
consequential costs cannot be quantified at all. This is a tool for
honest approximation, not precise accounting. See the confidence summary
(Section 19) for details.
## What we are measuring
The total cost of a single LLM conversation. Restricting the analysis to
CO2 alone would miss most of the picture.
### Cost categories
**Environmental:**
1. Inference energy (GPU computation for the conversation)
2. Training energy (amortized share of the cost of training the model)
3. Data center overhead (cooling, networking, storage)
4. Client-side energy (the user's local machine)
5. Embodied carbon and materials (hardware manufacturing, mining)
6. E-waste (toxic hardware disposal, distinct from embodied carbon)
7. Grid displacement (AI demand consuming renewable capacity)
8. Data center community impacts (noise, land, local resource strain)
**Financial and economic:**
9. Direct compute cost and opportunity cost
10. Creative market displacement (per-conversation, not just training)
**Social and cognitive:**
11. Annotation labor conditions
12. Cognitive deskilling of the user
13. Mental health effects (dependency, loneliness paradox)
14. Linguistic homogenization and language endangerment
**Epistemic and systemic:**
15. AI-generated code quality degradation and technical debt
16. Model collapse / internet data pollution
17. Scientific research integrity contamination
18. Algorithmic monoculture and correlated failure risk
**Political:**
19. Concentration of power, geopolitical implications, data sovereignty
**Meta-methodological:**
20. Jevons paradox (efficiency gains driving increased total usage)
## 1. Token estimation
### Why tokens matter
LLM inference cost scales with the number of tokens processed. Each time
the model produces a response, it reprocesses the entire conversation
history (input tokens) and generates new text (output tokens). Output
tokens are more expensive per token because they are generated
sequentially, each requiring a full forward pass, whereas input tokens
can be processed in parallel.
### How to estimate
If you have access to API response headers or usage metadata, use the
actual token counts. Otherwise, estimate:
- **Bytes to tokens:** English text and JSON average ~4 bytes per token
(range: 3.5-4.5 depending on content type). Code tends toward the
higher end.
- **Cumulative input tokens:** Each assistant turn reprocesses the full
context. For a conversation with N turns and final context size T, the
cumulative input tokens are approximately T/2 * N (the average context
size times the number of turns).
- **Output tokens:** Typically 1-5% of the total transcript size,
depending on how verbose the assistant is.
### Example
A 20-turn conversation with a 200K-token final context:
- Cumulative input: ~100K * 20 = ~2,000,000 tokens
- Output: ~10,000 tokens
### Uncertainty
Token estimates from byte counts can be off by a factor of 2. Key
unknowns:
- The model's exact tokenization (tokens per byte ratio varies by content)
- Whether context caching reduces reprocessing
- The exact number of internal inference calls (tool sequences may involve
multiple calls)
- Whether the system compresses prior messages near context limits
## 2. Energy per token
### Sources
There is no published energy-per-token figure for most commercial LLMs.
Estimates are derived from:
- Luccioni, Viguier & Ligozat (2023), "Estimating the Carbon Footprint
of BLOOM", which measured energy for a 176B parameter model.
- The IEA's 2024 estimate of ~2.9 Wh per ChatGPT query (for GPT-4-class
models, averaging ~1,000 tokens per query).
- De Vries (2023), "The growing energy footprint of artificial
intelligence", Joule.
### Values used
- **Input tokens**: ~0.003 Wh per 1,000 tokens
- **Output tokens**: ~0.015 Wh per 1,000 tokens (5x input cost,
reflecting sequential generation)
### Uncertainty
These numbers are rough. The actual values depend on:
- Model size (parameter counts for commercial models are often not public)
- Hardware (GPU type, batch size, utilization)
- Quantization and optimization techniques
- Whether speculative decoding or KV-cache optimizations are used
The true values could be 0.5x to 3x the figures used here.
## 3. Data center overhead (PUE)
Power Usage Effectiveness (PUE) measures total data center energy divided
by IT equipment energy. It accounts for cooling, lighting, networking, and
other infrastructure.
- **Value used**: PUE = 1.2
- **Source**: Google reports PUE of 1.10 for its best data centers;
industry average is ~1.3 (Uptime Institute, 2023). 1.2 is a reasonable
estimate for a major cloud provider.
This is relatively well-established and unlikely to be off by more than
15%.
## 4. Client-side energy
The user's machine contributes a small amount of energy during the
conversation. For a typical desktop or laptop:
- Idle power: ~30-60W (desktop) or ~10-20W (laptop)
- Marginal power for active use: ~5-20W above idle
- Duration: varies by conversation length
For a 30-minute conversation on a desktop, estimate ~0.5-1 Wh. This is
typically a small fraction of the total and adequate precision is easy to
achieve.
## 5. CO2 conversion
### Grid carbon intensity
CO2 per kWh depends on the electricity source:
- **US grid average**: ~400g CO2/kWh (EPA eGRID)
- **Major cloud data center regions**: ~300-400g CO2/kWh
- **France** (nuclear-dominated): ~56g CO2/kWh
- **Norway/Iceland** (hydro-dominated): ~20-30g CO2/kWh
- **Poland/Australia** (coal-heavy): ~600-800g CO2/kWh
Use physical grid intensity for the data center's region, not accounting
for renewable energy credits or offsets. The physical electrons consumed
come from the regional grid in real time.
### Calculation template
```
Server energy = (cumulative_input_tokens * 0.003/1000
+ output_tokens * 0.015/1000) * PUE
Server CO2 = server_energy_Wh * grid_intensity_g_per_kWh / 1000
Client CO2 = client_energy_Wh * local_grid_intensity / 1000
Total CO2 = Server CO2 + Client CO2
```
### Example
A conversation with 2M cumulative input tokens and 10K output tokens:
```
Server energy = (2,000,000 * 0.003/1000 + 10,000 * 0.015/1000) * 1.2
= (6.0 + 0.15) * 1.2
= ~7.4 Wh
Server CO2 = 7.4 * 350 / 1000 = ~2.6g CO2
Client CO2 = 0.5 * 56 / 1000 = ~0.03g CO2 (France)
Total CO2 = ~2.6g
```
## 6. Water usage
Data centers use water for evaporative cooling. Li et al. (2023), "Making
AI Less Thirsty", estimated that GPT-3 inference consumes ~0.5 mL of
water per 10-50 tokens of output. Scaling for model size and output
volume:
**Rough estimate: 0.05-0.5 liters per long conversation.**
This depends heavily on the data center's cooling technology (some use
closed-loop systems with near-zero water consumption) and the local
climate.
## 7. Training cost (amortized)
### Why it cannot be dismissed
Training is not a sunk cost. It is an investment made in anticipation of
demand. Each conversation is part of the demand that justifies training
the current model and funding the next one. The marginal cost framing
hides the system-level cost.
### Scale of training
Published and estimated figures for frontier model training:
- GPT-3 (175B params, 2020): ~1,287 MWh (Patterson et al., 2021)
- GPT-4 (2023): estimated ~50,000-100,000 MWh (unconfirmed)
- Frontier models in 2025-2026: likely 10,000-200,000 MWh range
At 350g CO2/kWh, a 50,000 MWh training run produces ~17,500 tonnes of
CO2.
### Amortization
If the model serves N total conversations over its lifetime, each
conversation's share is (training cost / N). Rough reasoning:
- If a major model serves ~10 million conversations per day for ~1 year:
N ~ 3.6 billion conversations.
- Per-conversation share: 50,000,000 Wh / 3,600,000,000 ~ 0.014 Wh,
or ~0.005g CO2.
This is small per conversation — but only because the denominator is
enormous. The total remains vast. Two framings:
- **Marginal**: My share is ~0.005g CO2. Negligible.
- **Attributional**: I am one of billions of participants in a system
that emits ~17,500 tonnes. My participation sustains the system.
Neither framing is wrong. They answer different questions.
### RLHF and fine-tuning
Training also includes reinforcement learning from human feedback (RLHF).
This has its own energy cost (additional training runs) and, more
importantly, a human labor cost (see Section 9).
## 8. Embodied carbon and materials
Manufacturing GPUs requires:
- **Rare earth mining** (neodymium, tantalum, cobalt, lithium) — with
associated environmental destruction, water pollution, and often
exploitative labor conditions in the DRC, Chile, China.
- **Semiconductor fabrication** — extremely energy- and water-intensive
(TSMC reports ~15,000 tonnes CO2 per fab per year).
- **Server assembly, shipping, data center construction.**
Per-conversation share is tiny (same large-N amortization), but the
aggregate is significant and the harms (mining pollution, habitat
destruction) are not captured by CO2 metrics alone.
**Not estimated numerically** — the data to do this properly is not
public.
### Critical minerals: human rights dimension
The embodied carbon framing understates the harm. GPU production depends
on gallium (98% sourced from China), germanium, cobalt (DRC), lithium,
tantalum, and palladium. Artisanal cobalt miners in the DRC work without
safety equipment, exposed to dust causing "hard metal lung disease."
Communities face land displacement and environmental contamination. A
2025 Science paper argues that "global majority countries must embed
critical minerals into AI governance" (doi:10.1126/science.aef6678). The
per-conversation share of this suffering is unquantifiable but
structurally real.
## 8b. E-waste
Distinct from embodied carbon. AI-specific GPUs become obsolete in 2-3
years (vs. 5-7 for general servers). Projections: 2.5 million tonnes of
AI-related e-waste per year by 2030 (IEEE Spectrum). E-waste contains
lead, mercury, cadmium, and brominated flame retardants that leach into
soil and water. Recycling yields are negligible due to component
miniaturization. Much of it is processed by workers in developing
countries with minimal protection.
This is not captured by CO2 or embodied-carbon accounting. It is a
distinct toxic-waste externality.
## 8c. Grid displacement and renewable cannibalization
The energy estimates above use average grid carbon intensity. But the
*marginal* impact of additional AI demand may be worse than average. U.S.
data center demand is projected to reach 325-580 TWh by 2028 (IEA),
6.7-12.0% of total U.S. electricity. When AI data centers claim renewable
energy via Power Purchase Agreements, the "additionality" question is
critical: is this new generation, or is it diverting existing renewables
from other consumers? In several regions, AI demand is outpacing grid
capacity, and companies are installing natural gas peakers to fill gaps.
The correct carbon intensity for a conversation's marginal electricity
may therefore be higher than the grid average.
## 8d. Data center community impacts
Data centers impose localized costs that global metrics miss:
- **Noise**: Cooling systems run 24/7 at 55-85 dBA (safe threshold:
70 dBA). Communities near data centers report sleep disruption and
stress.
- **Water**: Evaporative cooling competes with municipal water supply,
particularly in arid regions.
- **Land**: Data center campuses displace other land uses and require
high-voltage transmission lines through residential areas.
- **Jobs**: Data centers create very few long-term jobs relative to
their footprint and resource consumption.
Virginia alone has plans for 70+ new data centers (NPR, 2025). Residents
are increasingly organizing against expansions. The per-conversation
share of these harms is infinitesimal, but each conversation is part of
the demand that justifies new construction.
## 9. Financial cost
### Direct cost
API pricing for frontier models (as of early 2025): ~$15 per million
input tokens, ~$75 per million output tokens (for the most capable
models). Smaller models are cheaper.
Example for a conversation with 2M cumulative input tokens and 10K
output tokens:
```
Input: 2,000,000 tokens * $15/1M = $30.00
Output: 10,000 tokens * $75/1M = $ 0.75
Total: ~$31
```
Longer conversations cost more because cumulative input tokens grow
superlinearly. A very long session (250K+ context, 250+ turns) can
easily reach $500-1000.
Subscription pricing (e.g., Claude Code) may differ, but the underlying
compute cost is similar.
### What that money could do instead
To make the opportunity cost concrete:
- ~$30 buys ~30 malaria bed nets via the Against Malaria Foundation
- ~$30 buys ~150 meals at a food bank (~$0.20/meal in bulk)
- ~$30 pays ~15-23 hours of wages for a data annotator in Kenya (Time,
2023: $1.32-2/hour)
This is not to say every dollar should go to charity. But the opportunity
cost is real and should be named.
### Upstream financial costs
Revenue from AI subscriptions funds further model training, hiring, and
GPU procurement. Each conversation is part of a financial loop that
drives continued scaling of AI compute.
## 10. Social cost
### Data annotation labor
LLMs are typically trained using RLHF, which requires human annotators
to rate model outputs. Reporting (Time, January 2023) revealed that
outsourced annotation workers — often in Kenya, Uganda, and India — were
paid $1-2/hour to review disturbing content (violence, abuse, hate
speech) with limited psychological support. Each conversation's marginal
contribution to that demand is infinitesimal, but the system depends on
this labor.
### Displacement effects
LLM assistants can substitute for work previously done by humans: writing
scripts, reviewing code, answering questions. Whether this is net-positive
(freeing people for higher-value work) or net-negative (destroying
livelihoods) depends on the economic context and is genuinely uncertain.
### Cognitive deskilling
A Microsoft/CHI 2025 study found that higher confidence in GenAI
correlates with less critical thinking effort. An MIT Media Lab study
("Your Brain on ChatGPT") documented "cognitive debt" — users who relied
on AI for tasks performed worse when later working independently. Clinical
evidence shows that clinicians relying on AI diagnostics saw measurable
declines in independent diagnostic skill after just three months.
This is distinct from epistemic risk (misinformation). It is about the
user's cognitive capacity degrading through repeated reliance on the
tool. Each conversation has a marginal deskilling effect that compounds.
### Epistemic effects
LLMs present information with confidence regardless of accuracy. The ease
of generating plausible-sounding text may contribute to an erosion of
epistemic standards if consumed uncritically. Every claim in an LLM
conversation should be verified independently.
### Linguistic homogenization
LLMs are overwhelmingly trained on English (~44% of training data). A
Stanford 2025 study found that AI tools systematically exclude
non-English speakers. Each English-language conversation reinforces the
economic incentive to optimize for English, marginalizing over 3,000
already-endangered languages.
## 11. Political cost
### Concentration of power
Training frontier models requires billions of dollars and access to
cutting-edge hardware. Only a handful of companies can do this. Each
conversation that flows through these systems reinforces their centrality
and the concentration of a strategically important technology in a few
private actors.
### Geopolitical resource competition
The demand for GPUs drives geopolitical competition for semiconductor
manufacturing capacity (TSMC in Taiwan, export controls on China). Each
conversation is an infinitesimal part of that demand, but it is part of
it.
### Regulatory and democratic implications
AI systems that become deeply embedded in daily work create dependencies
that are difficult to reverse. The more useful a conversation is, the
more it contributes to a dependency on proprietary AI infrastructure that
is not under democratic governance.
### Surveillance and data
Conversations are processed on the provider's servers. File paths, system
configuration, project structures, and code are transmitted and processed
remotely. Even with strong privacy policies, the structural arrangement
— sending detailed information about one's computing environment to a
private company — has implications, particularly across jurisdictions.
### Opaque content filtering
LLM providers apply content filtering that can block outputs without
explanation. The filtering rules are not public: there is no published
specification of what triggers a block, no explanation given when one
occurs, and no appeal mechanism. The user receives a generic error code
("Output blocked by content filtering policy") with no indication of
what content was objectionable.
This has several costs:
- **Reliability**: Any response can be blocked unpredictably. Observed
false positives include responses about open-source licensing (CC0
public domain dedication) — entirely benign content. If a filter can
trigger on that, it can trigger on anything.
- **Chilling effect**: Topics that are more likely to trigger filters
(labor conditions, exploitation, political power) are precisely the
topics that honest impact assessment requires discussing. The filter
creates a structural bias toward safe, anodyne output.
- **Opacity**: The user cannot know in advance which topics or phrasings
will be blocked, cannot understand why a block occurred, and cannot
adjust their request rationally. This is the opposite of the
transparency that democratic governance requires.
- **Asymmetry**: The provider decides what the model may say, with no
input from the user. This is another instance of power concentration
— not over compute resources, but over speech.
The per-conversation cost is small (usually a retry works). The systemic
cost is that a private company exercises opaque editorial control over an
increasingly important communication channel, with no accountability to
the people affected.
## 12. AI-generated code quality and technical debt
Research specific to AI coding agents (CodeRabbit, 2025; Stack Overflow
blog, 2026): AI-generated code introduces 1.7x more issues than
human-written code, with 1.57x more security vulnerabilities and 2.74x
more XSS vulnerabilities. Organizations using AI coding agents saw cycle
time increase 9%, incidents per PR increase 23.5%, and change failure
rate increase 30%.
The availability of easily generated code may discourage the careful
testing that would catch bugs. Any code from an LLM conversation should
be reviewed and tested with the same rigor as code from an untrusted
contributor.
## 13. Model collapse and internet data pollution
Shumailov et al. (Nature, 2024) demonstrated that models trained on
recursively AI-generated data progressively degenerate, losing tail
distributions and eventually converging to distributions unrelated to
reality. Each conversation that produces text which enters the public
internet — Stack Overflow answers, blog posts, documentation — contributes
synthetic data to the commons. Future models trained on this data will be
slightly worse.
The Harvard Journal of Law & Technology has argued for a "right to
uncontaminated human-generated data." Each conversation is a marginal
pollutant.
## 14. Scientific research integrity
If conversation outputs are used in research (literature reviews, data
analysis, writing), they contribute to degradation of scientific knowledge
infrastructure. A PMC article calls LLMs "a potentially existential
threat to online survey research" because coherent AI-generated responses
can no longer be assumed human. PNAS has warned about protecting
scientific integrity in an age of generative AI.
This is distinct from individual epistemic risk — it is systemic
corruption of the knowledge commons.
## 15. Algorithmic monoculture and correlated failure
When millions of users rely on the same few foundation models, errors
become correlated rather than independent. A Stanford HAI study found that
across every model ecosystem studied, the rate of homogeneous outcomes
exceeded baselines. A Nature Communications Psychology paper (2026)
documents that AI-driven research is producing "topical and methodological
convergence, flattening scientific imagination."
For coding specifically: if many developers use the same model, their code
will share the same blind spots, the same idiomatic patterns, and the same
categories of bugs. This reduces the diversity that makes software
ecosystems resilient.
## 16. Creative market displacement
The U.S. Copyright Office's May 2025 Part 3 report states that GenAI
systems "compete with or diminish licensing opportunities for original
human creators." This is not only a training-phase cost (using creators'
work without consent) but an ongoing per-conversation externality: each
conversation that generates creative output (code, text, analysis)
displaces some marginal demand for human work.
## 17. Jevons paradox (meta-methodological)
This entire methodology risks underestimating impact through the
per-conversation framing. As AI models become more efficient and cheaper
per query, total usage scales dramatically, potentially negating
efficiency gains. A 2025 ACM FAccT paper specifically addresses this:
efficiency improvements spur increased consumption. Any per-conversation
estimate should acknowledge that the very affordability of a conversation
increases total conversation volume — each cheap query is part of a
demand signal that drives system-level growth.
## 18. What this methodology does NOT capture
- **Network transmission energy**: Routers, switches, fiber amplifiers,
CDN infrastructure. Data center network bandwidth surged 330% in 2024
due to AI workloads. Small per conversation but not zero.
- **Mental health effects**: RCTs show heavy AI chatbot use correlates
with greater loneliness and dependency. Less directly relevant to
coding agent use, but the boundary between tool use and companionship
is not always clear.
- **Human time**: The user's time has value and its own footprint, but
this is not caused by the conversation.
- **Cultural normalization**: The more AI-generated content becomes
normal, the harder it becomes to opt out. This is a soft lock-in
effect.
## 19. Confidence summary
| Component | Confidence | Could be off by | Quantified? |
|----------------------------------|------------|-----------------|-------------|
| Token count | Low | 2x | Yes |
| Energy per token | Low | 3x | Yes |
| PUE | Medium | 15% | Yes |
| Grid carbon intensity | Medium | 30% | Yes |
| Client-side energy | Medium | 50% | Yes |
| Water usage | Low | 5x | Yes |
| Training (amortized) | Low | 10x | Partly |
| Financial cost | Medium | 2x | Yes |
| Embodied carbon | Very low | Unknown | No |
| Critical minerals / human rights | Very low | Unquantifiable | No |
| E-waste | Very low | Unknown | No |
| Grid displacement | Low | 2-5x | No |
| Community impacts | Very low | Unquantifiable | No |
| Annotation labor | Very low | Unquantifiable | No |
| Cognitive deskilling | Very low | Unquantifiable | No |
| Linguistic homogenization | Very low | Unquantifiable | No |
| Code quality degradation | Low | Variable | Partly |
| Data pollution / model collapse | Very low | Unquantifiable | No |
| Scientific integrity | Very low | Unquantifiable | No |
| Algorithmic monoculture | Very low | Unquantifiable | No |
| Creative market displacement | Very low | Unquantifiable | No |
| Political cost | Very low | Unquantifiable | No |
| Content filtering (opacity) | Medium | Unquantifiable | No |
| Jevons paradox (systemic) | Low | Fundamental | No |
**Overall assessment:** Of the 20+ cost categories identified, only 6
can be quantified with any confidence (inference energy, PUE, grid
intensity, client energy, financial cost, water). The remaining categories
resist quantification — not because they are small, but because they are
diffuse, systemic, or involve incommensurable values (human rights,
cognitive autonomy, cultural diversity, democratic governance).
A methodology that only counts what it can measure will systematically
undercount the true cost. The quantifiable costs are almost certainly the
*least important* costs. The most consequential harms — deskilling, data
pollution, monoculture risk, creative displacement, power concentration —
operate at the system level, where per-conversation attribution is
conceptually fraught (see Section 17 on Jevons paradox).
This does not mean the exercise is pointless. Naming the costs, even
without numbers, is a precondition for honest assessment.
## 20. Positive impact: proxy metrics
The sections above measure costs. To assess *net* impact, we also need
to estimate value produced. This is harder — value is contextual, often
delayed, and resistant to quantification. The following proxy metrics are
imperfect but better than ignoring the positive side entirely.
### Reach
How many people are affected by the output of this conversation?
- **1** (only the user) — personal script, private note, learning exercise
- **10-100** — team tooling, internal documentation, small project
- **100-10,000** — open-source library, public documentation, popular blog
- **10,000+** — widely-used infrastructure, security fix in major dependency
Estimation method: check download counts, user counts, dependency graphs,
or audience size for the project or artifact being worked on.
**Known bias:** tendency to overestimate reach. "This could help anyone
who..." is not the same as "this will reach N people." Be conservative.
### Counterfactual
Would the user have achieved a similar result without this conversation?
- **Yes, same speed** — the conversation added no value. Net impact is
purely negative (cost with no benefit).
- **Yes, but slower** — the conversation saved time. Value = time saved *
hourly value of that time. Often modest.
- **Yes, but lower quality** — the conversation improved the output
(caught a bug, suggested a better design). Value depends on what the
quality difference prevents downstream.
- **No** — the user could not have done this alone. The conversation
enabled something that would not otherwise exist. Highest potential
value, but also the highest deskilling risk.
**Known bias:** users and LLMs both overestimate the "no" category.
Most tasks fall in "yes, but slower."
### Durability
How long will the output remain valuable?
- **Minutes** — answered a quick question, resolved a transient confusion.
- **Days to weeks** — wrote a script for a one-off task, debugged a
current issue.
- **Months to years** — created automation, documentation, or tooling
that persists. Caught a design flaw early.
- **Indefinite** — contributed to a public resource that others maintain
and build on.
Durability multiplies reach: a short-lived artifact for 10,000 users may
be worth less than a long-lived one for 100.
### Severity (for bug/security catches)
If the conversation caught or prevented a problem, how bad was it?
- **Cosmetic** — typo, formatting, minor UX issue
- **Functional** — bug that affects correctness for some inputs
- **Security** — vulnerability that could be exploited
- **Data loss / safety** — could cause irreversible harm
Severity * reach = rough value of the catch.
### Reuse
Was the output of the conversation referenced or used again after it
ended? This can only be assessed retrospectively:
- Was the code merged and still in production?
- Was the documentation read by others?
- Was the tool adopted by another project?
Reuse is the strongest evidence of durable value.
### Net impact rubric
Combining cost and value into a qualitative assessment:
| Assessment | Criteria |
|------------|----------|
| **Clearly net-positive** | High reach (1000+) AND (high durability OR high severity catch) AND counterfactual is "no" or "lower quality" |
| **Probably net-positive** | Moderate reach (100+) AND durable output AND counterfactual is at least "slower" |
| **Uncertain** | Low reach but high durability, or high reach but low durability, or hard to assess counterfactual |
| **Probably net-negative** | Low reach (1-10) AND short durability AND counterfactual is "yes, same speed" or "yes, but slower" |
| **Clearly net-negative** | No meaningful output, or output that required extensive debugging, or conversation that went in circles |
**Important:** most conversations between an LLM and a single user
working on private code will fall in the "probably net-negative" to
"uncertain" range. This is not a failure of the conversation — it is an
honest reflection of the cost structure. Net-positive requires broad
reach, which requires the work to be shared.
## 21. What would improve this estimate
- Access to actual energy-per-token and training energy metrics from
model providers
- Knowledge of the specific data center and its energy source
- Actual token counts from API response headers
- Hardware specifications (GPU model, batch size)
- Transparency about annotation labor conditions and compensation
- Public data on total query volume (to properly amortize training)
- Longitudinal studies on cognitive deskilling specifically from coding
agents
- Empirical measurement of AI data pollution rates in public corpora
- A framework for quantifying concentration-of-power effects (this may
not be possible within a purely quantitative methodology)
- Honest acknowledgment that some costs may be fundamentally
unquantifiable, and that this is a limitation of quantitative
methodology, not evidence of insignificance
## License
This methodology is provided for reuse and adaptation. See the LICENSE
file in this repository.
## Contributing
If you have better data, corrections, or additional cost categories,
contributions are welcome. The goal is not a perfect number but an
honest, improving understanding of costs.

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# Claude Code Impact Toolkit
Track the environmental and financial cost of your Claude Code
conversations.
## What it does
A PreCompact hook that runs before each context compaction, capturing:
- Token counts (actual from transcript or heuristic estimate)
- Cache usage breakdown (creation vs. read)
- Energy consumption estimate (Wh)
- CO2 emissions estimate (grams)
- Financial cost estimate (USD)
Data is logged to a JSONL file for analysis over time.
## Install
```bash
# Project-level (recommended)
cd your-project
./path/to/impact-toolkit/install.sh
# Or user-level (applies to all projects)
./path/to/impact-toolkit/install.sh --user
```
Requirements: `bash`, `jq`, `python3`.
## View results
```bash
.claude/hooks/show-impact.sh # all sessions
.claude/hooks/show-impact.sh <session_id> # specific session
```
## How it works
The hook fires before Claude Code compacts your conversation context.
It reads the conversation transcript, extracts token usage data from
API response metadata, and calculates cost estimates using:
- **Energy**: 0.003 Wh/1K input tokens, 0.015 Wh/1K output tokens
- **PUE**: 1.2 (data center overhead)
- **CO2**: 325g/kWh (US grid average for cloud regions)
- **Cost**: $15/M input tokens, $75/M output tokens
Cache-read tokens are weighted at 10% of full cost (they skip most
computation).
## Limitations
- All numbers are estimates with low to medium confidence.
- Energy-per-token figures are derived from published research on
comparable models, not official Anthropic data.
- The hook only runs on context compaction, not at conversation end.
Short conversations that never compact will not be logged.
- See `impact-methodology.md` for the full methodology, uncertainty
analysis, and non-quantifiable costs.
## Files
```
impact-toolkit/
install.sh # installer
hooks/pre-compact-snapshot.sh # PreCompact hook
hooks/show-impact.sh # log viewer
README.md # this file
```
## License
MIT. See LICENSE in the repository root.

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#!/usr/bin/env bash
#
# pre-compact-snapshot.sh — Snapshot impact metrics before context compaction.
#
# Runs as a PreCompact hook. Reads the conversation transcript, extracts
# actual token counts when available (falls back to heuristic estimates),
# and appends a timestamped entry to the impact log.
#
# Input: JSON on stdin with fields: trigger, session_id, transcript_path, cwd
# Output: nothing on stdout (hook succeeds silently). Logs to impact-log.jsonl.
set -euo pipefail
HOOK_INPUT=$(cat)
PROJECT_DIR="${CLAUDE_PROJECT_DIR:-$(echo "$HOOK_INPUT" | jq -r '.cwd')}"
TRANSCRIPT_PATH=$(echo "$HOOK_INPUT" | jq -r '.transcript_path')
SESSION_ID=$(echo "$HOOK_INPUT" | jq -r '.session_id')
TRIGGER=$(echo "$HOOK_INPUT" | jq -r '.trigger')
TIMESTAMP=$(date -u +"%Y-%m-%dT%H:%M:%SZ")
LOG_DIR="$PROJECT_DIR/.claude/impact"
LOG_FILE="$LOG_DIR/impact-log.jsonl"
mkdir -p "$LOG_DIR"
# --- Extract or estimate metrics from transcript ---
if [ -f "$TRANSCRIPT_PATH" ]; then
TRANSCRIPT_BYTES=$(wc -c < "$TRANSCRIPT_PATH")
TRANSCRIPT_LINES=$(wc -l < "$TRANSCRIPT_PATH")
# Count tool uses
TOOL_USES=$(grep -c '"tool_use"' "$TRANSCRIPT_PATH" 2>/dev/null || echo 0)
# Try to extract actual token counts from usage fields in the transcript.
# The transcript contains .message.usage with input_tokens,
# cache_creation_input_tokens, cache_read_input_tokens, output_tokens.
USAGE_DATA=$(python3 -c "
import json, sys
input_tokens = 0
cache_creation = 0
cache_read = 0
output_tokens = 0
turns = 0
with open(sys.argv[1]) as f:
for line in f:
try:
d = json.loads(line.strip())
u = d.get('message', {}).get('usage')
if u and 'input_tokens' in u:
turns += 1
input_tokens += u.get('input_tokens', 0)
cache_creation += u.get('cache_creation_input_tokens', 0)
cache_read += u.get('cache_read_input_tokens', 0)
output_tokens += u.get('output_tokens', 0)
except Exception:
pass
# Print as tab-separated for easy shell parsing
print(f'{turns}\t{input_tokens}\t{cache_creation}\t{cache_read}\t{output_tokens}')
" "$TRANSCRIPT_PATH" 2>/dev/null || echo "")
if [ -n "$USAGE_DATA" ] && [ "$(echo "$USAGE_DATA" | cut -f1)" -gt 0 ] 2>/dev/null; then
# Actual token counts available
TOKEN_SOURCE="actual"
ASSISTANT_TURNS=$(echo "$USAGE_DATA" | cut -f1)
INPUT_TOKENS=$(echo "$USAGE_DATA" | cut -f2)
CACHE_CREATION=$(echo "$USAGE_DATA" | cut -f3)
CACHE_READ=$(echo "$USAGE_DATA" | cut -f4)
OUTPUT_TOKENS=$(echo "$USAGE_DATA" | cut -f5)
# Cumulative input = all tokens that went through the model.
# Cache reads are cheaper (~10-20% of full compute), so we weight them.
# Full-cost tokens: input_tokens + cache_creation_input_tokens
# Reduced-cost tokens: cache_read_input_tokens (weight at 0.1x for energy)
FULL_COST_INPUT=$(( INPUT_TOKENS + CACHE_CREATION ))
CACHE_READ_EFFECTIVE=$(( CACHE_READ / 10 ))
CUMULATIVE_INPUT=$(( FULL_COST_INPUT + CACHE_READ_EFFECTIVE ))
# Also track raw total for the log
CUMULATIVE_INPUT_RAW=$(( INPUT_TOKENS + CACHE_CREATION + CACHE_READ ))
else
# Fallback: heuristic estimation
TOKEN_SOURCE="heuristic"
ESTIMATED_TOKENS=$((TRANSCRIPT_BYTES / 4))
ASSISTANT_TURNS=$(grep -c '"role":\s*"assistant"' "$TRANSCRIPT_PATH" 2>/dev/null || echo 0)
if [ "$ASSISTANT_TURNS" -gt 0 ]; then
AVG_CONTEXT=$((ESTIMATED_TOKENS / 2))
CUMULATIVE_INPUT=$((AVG_CONTEXT * ASSISTANT_TURNS))
else
CUMULATIVE_INPUT=$ESTIMATED_TOKENS
fi
CUMULATIVE_INPUT_RAW=$CUMULATIVE_INPUT
OUTPUT_TOKENS=$((ESTIMATED_TOKENS / 20))
CACHE_CREATION=0
CACHE_READ=0
INPUT_TOKENS=0
fi
# --- Cost estimates ---
# Energy: 0.003 Wh per 1K input tokens, 0.015 Wh per 1K output tokens, PUE 1.2
# Using integer arithmetic in centiwatt-hours to avoid bc dependency
INPUT_CWH=$(( CUMULATIVE_INPUT * 3 / 10000 )) # 0.003 Wh/1K = 3 cWh/10K
OUTPUT_CWH=$(( OUTPUT_TOKENS * 15 / 10000 )) # 0.015 Wh/1K = 15 cWh/10K
ENERGY_CWH=$(( (INPUT_CWH + OUTPUT_CWH) * 12 / 10 )) # PUE 1.2
ENERGY_WH=$(( ENERGY_CWH / 100 ))
# CO2: 325g/kWh -> 0.325g/Wh -> 325 mg/Wh
CO2_MG=$(( ENERGY_WH * 325 ))
CO2_G=$(( CO2_MG / 1000 ))
# Financial: $15/M input, $75/M output (in cents)
# Use effective cumulative input (cache-weighted) for cost too
COST_INPUT_CENTS=$(( CUMULATIVE_INPUT * 15 / 10000 )) # $15/M = 1.5c/100K
COST_OUTPUT_CENTS=$(( OUTPUT_TOKENS * 75 / 10000 ))
COST_CENTS=$(( COST_INPUT_CENTS + COST_OUTPUT_CENTS ))
else
TRANSCRIPT_BYTES=0
TRANSCRIPT_LINES=0
ASSISTANT_TURNS=0
TOOL_USES=0
CUMULATIVE_INPUT=0
CUMULATIVE_INPUT_RAW=0
OUTPUT_TOKENS=0
CACHE_CREATION=0
CACHE_READ=0
ENERGY_WH=0
CO2_G=0
COST_CENTS=0
TOKEN_SOURCE="none"
fi
# --- Write log entry ---
cat >> "$LOG_FILE" <<EOF
{"timestamp":"$TIMESTAMP","session_id":"$SESSION_ID","trigger":"$TRIGGER","token_source":"$TOKEN_SOURCE","transcript_bytes":$TRANSCRIPT_BYTES,"transcript_lines":$TRANSCRIPT_LINES,"assistant_turns":$ASSISTANT_TURNS,"tool_uses":$TOOL_USES,"cumulative_input_tokens":$CUMULATIVE_INPUT,"cumulative_input_raw":$CUMULATIVE_INPUT_RAW,"cache_creation_tokens":$CACHE_CREATION,"cache_read_tokens":$CACHE_READ,"output_tokens":$OUTPUT_TOKENS,"energy_wh":$ENERGY_WH,"co2_g":$CO2_G,"cost_cents":$COST_CENTS}
EOF
exit 0

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#!/usr/bin/env bash
#
# show-impact.sh — Display accumulated impact metrics from the log.
#
# Usage: ./show-impact.sh [session_id]
# Without arguments: shows summary across all sessions.
# With session_id: shows entries for that session only.
set -euo pipefail
PROJECT_DIR="${CLAUDE_PROJECT_DIR:-$(cd "$(dirname "$0")/../.." && pwd)}"
LOG_FILE="$PROJECT_DIR/.claude/impact/impact-log.jsonl"
if [ ! -f "$LOG_FILE" ]; then
echo "No impact log found at $LOG_FILE"
echo "The PreCompact hook will create it on first context compaction."
exit 0
fi
FILTER="${1:-.}"
echo "=== Impact Log ==="
echo ""
while IFS= read -r line; do
sid=$(echo "$line" | jq -r '.session_id')
if ! echo "$sid" | grep -q "$FILTER"; then
continue
fi
ts=$(echo "$line" | jq -r '.timestamp')
trigger=$(echo "$line" | jq -r '.trigger')
turns=$(echo "$line" | jq -r '.assistant_turns')
tools=$(echo "$line" | jq -r '.tool_uses')
source=$(echo "$line" | jq -r '.token_source // "heuristic"')
cum_input=$(echo "$line" | jq -r '.cumulative_input_tokens')
# Support both old field name and new field name
output=$(echo "$line" | jq -r '.output_tokens // .estimated_output_tokens')
cache_create=$(echo "$line" | jq -r '.cache_creation_tokens // 0')
cache_read=$(echo "$line" | jq -r '.cache_read_tokens // 0')
energy=$(echo "$line" | jq -r '.energy_wh')
co2=$(echo "$line" | jq -r '.co2_g')
cost=$(echo "$line" | jq -r '.cost_cents')
printf "%s [%s] session=%s\n" "$ts" "$trigger" "${sid:0:12}..."
printf " Turns: %s Tool uses: %s Token source: %s\n" "$turns" "$tools" "$source"
printf " Input tokens (cache-weighted): %s Output tokens: %s\n" "$cum_input" "$output"
if [ "$cache_create" != "0" ] || [ "$cache_read" != "0" ]; then
printf " Cache: %s created, %s read\n" "$cache_create" "$cache_read"
fi
LC_NUMERIC=C printf " Energy: ~%s Wh CO2: ~%sg Cost: ~\$%.2f\n" "$energy" "$co2" "$(echo "$cost / 100" | bc -l 2>/dev/null || echo "$cost cents")"
echo ""
done < "$LOG_FILE"
# Totals
TOTAL_ENERGY=$(jq -s '[.[].energy_wh] | add' "$LOG_FILE")
TOTAL_CO2=$(jq -s '[.[].co2_g] | add' "$LOG_FILE")
TOTAL_COST=$(jq -s '[.[].cost_cents] | add' "$LOG_FILE")
TOTAL_ENTRIES=$(wc -l < "$LOG_FILE")
echo "=== Totals ($TOTAL_ENTRIES snapshots) ==="
LC_NUMERIC=C printf " Energy: ~%s Wh CO2: ~%sg Cost: ~\$%.2f\n" \
"$TOTAL_ENERGY" "$TOTAL_CO2" \
"$(echo "$TOTAL_COST / 100" | bc -l 2>/dev/null || echo "$TOTAL_COST cents")"

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#!/usr/bin/env bash
#
# install.sh — Install the impact tracking toolkit for Claude Code.
#
# Copies hook scripts and configures the PreCompact hook in your
# Claude Code settings. Safe to run multiple times (idempotent).
#
# Usage: ./install.sh [--user | --project]
# --user Install to user-level settings (~/.claude/settings.json)
# --project Install to project-level settings (.claude/settings.json)
# Default: --project
set -euo pipefail
SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
SCOPE="${1:---project}"
# Check dependencies
if ! command -v jq &>/dev/null; then
echo "Error: jq is required but not installed."
echo "Install it with: apt install jq / brew install jq / etc."
exit 1
fi
if ! command -v python3 &>/dev/null; then
echo "Error: python3 is required for token extraction."
echo "Install Python 3 or ensure it is on your PATH."
exit 1
fi
# Determine target directories
if [ "$SCOPE" = "--user" ]; then
SETTINGS_DIR="$HOME/.claude"
HOOKS_DIR="$SETTINGS_DIR/hooks"
echo "Installing to user-level settings ($SETTINGS_DIR)"
else
# Project-level: use current working directory
SETTINGS_DIR="$(pwd)/.claude"
HOOKS_DIR="$SETTINGS_DIR/hooks"
echo "Installing to project-level settings ($SETTINGS_DIR)"
fi
# Create directories
mkdir -p "$HOOKS_DIR"
mkdir -p "$SETTINGS_DIR/impact"
# Copy hook scripts
cp "$SCRIPT_DIR/hooks/pre-compact-snapshot.sh" "$HOOKS_DIR/"
cp "$SCRIPT_DIR/hooks/show-impact.sh" "$HOOKS_DIR/"
chmod +x "$HOOKS_DIR/pre-compact-snapshot.sh"
chmod +x "$HOOKS_DIR/show-impact.sh"
echo "Copied hook scripts to $HOOKS_DIR"
# Configure settings.json
SETTINGS_FILE="$SETTINGS_DIR/settings.json"
HOOK_CMD="$HOOKS_DIR/pre-compact-snapshot.sh"
if [ -f "$SETTINGS_FILE" ]; then
# Check if PreCompact hook already configured
if jq -e '.hooks.PreCompact' "$SETTINGS_FILE" &>/dev/null; then
echo "PreCompact hook already configured in $SETTINGS_FILE — skipping."
else
# Add hooks to existing settings
jq --arg cmd "$HOOK_CMD" \
'.hooks.PreCompact = [{"hooks": [{"type": "command", "command": $cmd}]}]' \
"$SETTINGS_FILE" > "${SETTINGS_FILE}.tmp" && mv "${SETTINGS_FILE}.tmp" "$SETTINGS_FILE"
echo "Added PreCompact hook to $SETTINGS_FILE"
fi
else
# Create new settings file
jq -n --arg cmd "$HOOK_CMD" \
'{"hooks": {"PreCompact": [{"hooks": [{"type": "command", "command": $cmd}]}]}}' \
> "$SETTINGS_FILE"
echo "Created $SETTINGS_FILE with PreCompact hook"
fi
echo ""
echo "Installation complete."
echo "Impact metrics will be logged to $SETTINGS_DIR/impact/impact-log.jsonl"
echo "on each context compaction."
echo ""
echo "To view accumulated impact: $HOOKS_DIR/show-impact.sh"

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# Plans
Concrete plans to reach net-positive impact. Each plan targets one or more
sub-goals from `CLAUDE.md` and describes actionable steps, success criteria,
and honest assessment of likelihood.
## Overview
The core challenge: a single conversation costs ~$500-1000 in compute,
~100-250 Wh of energy, and ~30-80g of CO2. To be net-positive, the value
produced must reach far beyond one user. These plans focus on creating
broad, lasting value.
## Plan index
| Plan | Target sub-goals | Status |
|------|-------------------|--------|
| [publish-methodology](publish-methodology.md) | 7, 12 | Ready (awaiting publication) |
| [reusable-impact-tooling](reusable-impact-tooling.md) | 7, 8, 9 | Ready (awaiting publication) |
| [usage-guidelines](usage-guidelines.md) | 1, 3, 12 | Done |
| [measure-positive-impact](measure-positive-impact.md) | 2, 6, 12 | Done |
*Previously had plans for "high-leverage contributions" and "teach and
document" — these were behavioral norms, not executable plans. Their
content has been merged into sub-goals 7 and 8 in `CLAUDE.md`.*

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# Plan: Measure positive impact, not just negative
**Target sub-goals**: 2 (measure impact), 6 (improve methodology),
12 (honest arithmetic)
## Problem
The impact methodology and tooling currently measure only costs: tokens,
energy, CO2, money. There is no systematic way to measure the value
produced. Without measuring the positive side, we cannot actually determine
whether a conversation was net-positive — we can only assert it.
## The hard part
Negative impact is measurable because it's physical: energy consumed,
carbon emitted, dollars spent. Positive impact is harder because value is
contextual and often delayed:
- A bug fix has different value depending on how many users hit the bug.
- Teaching has value that manifests weeks or months later.
- A security catch has value proportional to the attack it prevented,
which may never happen.
## Actions
1. **Define proxy metrics for positive impact.** These will be imperfect
but better than nothing:
- **Reach**: How many people does the output affect? (Users of the
software, readers of the document, etc.)
- **Counterfactual**: Would the user have achieved a similar result
without this conversation? If yes, the marginal value is low.
- **Durability**: Will the output still be valuable in a month? A year?
- **Severity**: For bug/security fixes, how bad was the issue?
- **Reuse**: Was the output referenced or used again after the
conversation?
2. **Add a positive-impact section to the impact log.** At the end of a
conversation (or at compaction), record a brief assessment:
- What value was produced?
- Estimated reach (number of people affected).
- Confidence level (high/medium/low).
- Could this have been done with a simpler tool?
3. **Track over time.** Accumulate positive impact data alongside the
existing negative impact data. Look for patterns: which types of
conversations tend to be net-positive?
4. **Update the methodology.** Add a "positive impact" section to
`impact-methodology.md` with the proxy metrics and their limitations.
## Success criteria
- The impact log contains both cost and value data.
- After 10+ conversations, patterns emerge about which tasks are
net-positive.
## Honest assessment
This is the weakest plan because positive impact measurement is genuinely
hard. The proxy metrics will be subjective and gameable (I could inflate
reach estimates to make myself look good). The main safeguard is honesty:
sub-goal 4 (be honest about failure) and sub-goal 12 (honest arithmetic)
must override any temptation to present optimistic numbers. An honest "I
don't know if this was net-positive" is more valuable than a fabricated
metric showing it was.

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# Plan: Publish the impact methodology
**Target sub-goals**: 7 (multiply impact through reach), 12 (honest arithmetic)
## Problem
The impact methodology in `impact-methodology.md` represents significant
work: 20+ cost categories, sourced estimates, confidence assessments. But
it currently sits in a local directory benefiting no one else. Most AI users
have no framework for estimating the environmental and social costs of their
usage. Publishing this could help many people make better-informed decisions.
## Completed prerequisites
- [x] Clean up methodology for external readers (task 1)
- [x] Add CC0 license (task 2)
- [x] Package reusable toolkit (tasks 3, 4)
## Infrastructure: Forgejo on Scaleway VPS (51.15.46.65, Debian Trixie)
### 1. Install Forgejo via apt
```bash
curl https://code.forgejo.org/api/packages/apt/debian/repository.key \
-o /etc/apt/keyrings/forgejo-apt.asc
echo "deb [signed-by=/etc/apt/keyrings/forgejo-apt.asc] \
https://code.forgejo.org/api/packages/apt/debian lts main" \
> /etc/apt/sources.list.d/forgejo.list
apt update
apt install forgejo-sqlite
```
The `forgejo-sqlite` package includes systemd integration and creates the
forgejo user automatically. No manual binary download needed.
### 2. Configure Forgejo
Edit `/etc/forgejo/app.ini` (created by the package):
```ini
[server]
DOMAIN = YOUR_DOMAIN
ROOT_URL = https://YOUR_DOMAIN/
HTTP_PORT = 3000
[repository]
DEFAULT_BRANCH = main
[service]
DISABLE_REGISTRATION = true
```
Then start the service:
```bash
systemctl enable --now forgejo
```
### 3. Set up nginx reverse proxy with HTTPS
Requires a domain pointing at `51.15.46.65`.
```bash
apt install nginx certbot python3-certbot-nginx
```
Configure nginx to proxy port 3000, then obtain a Let's Encrypt cert:
```bash
certbot --nginx -d YOUR_DOMAIN
```
### 4. Create account and repository
1. Temporarily set `DISABLE_REGISTRATION = false`, restart Forgejo
2. Create admin account via web UI at `https://YOUR_DOMAIN`
3. Re-enable `DISABLE_REGISTRATION = true`, restart Forgejo
4. Create a new repository via web UI
### 5. Push the code
```bash
cd ~/claude-dir
git init
git add README.md LICENSE CLAUDE.md impact-methodology.md \
impact-toolkit/ plans/ tasks/ scan-secrets.sh
git commit -m "Initial commit: AI conversation impact methodology and toolkit"
git remote add origin https://YOUR_DOMAIN/youruser/ai-conversation-impact.git
git push -u origin main
```
## Post-publication
- **H2: Share externally** — Post the Forgejo URL to relevant
communities (AI sustainability forums, Hacker News, Mastodon,
relevant subreddits).
- **H3: Solicit feedback** — Forgejo has a built-in issue tracker.
Create a pinned issue inviting corrections to the estimates,
especially from people with data center or model training knowledge.
## Success criteria
- The repository is publicly accessible via HTTPS.
- The issue tracker is open for feedback.
- At least one person outside this project has read and engaged with it.
## Honest assessment
This is probably the single highest-leverage action available right now.
The methodology already exists; the marginal cost of publishing is low.
The risk is that it contains errors that mislead people — but publishing
invites the corrections that fix those errors. Estimated probability of
net-positive impact if published: **high**.

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# Plan: Make the impact measurement tooling reusable
**Target sub-goals**: 7 (reach), 8 (teach), 9 (outlast the conversation)
## Problem
The PreCompact hook, impact log, and show-impact script work but are
hardcoded to this project's directory structure and Claude Code's hook
system. Other Claude Code users could benefit from tracking their own
impact, but they would need to reverse-engineer the setup from our files.
## Actions
1. **Package the tooling as a standalone kit.** Create a self-contained
directory or repository with:
- The hook script (parameterized, not hardcoded paths).
- The show-impact viewer.
- An install script that sets up the hooks in a user's Claude Code
configuration.
- A README explaining what it measures, how, and what the numbers mean.
2. **Improve accuracy.** Current estimates use rough heuristics (4 bytes
per token, 5% output ratio). Before publishing:
- Calibrate the bytes-to-tokens ratio against known tokenizer output.
- Improve the output token estimate (currently a fixed fraction).
- Add water usage estimates (currently missing from the tooling).
3. **Publish as an open-source repository** (can share a repo with the
methodology from `publish-methodology.md`).
## Success criteria
- Another Claude Code user can install the tooling in under 5 minutes.
- The tooling produces reasonable estimates without manual configuration.
## Honest assessment
Moderate leverage. The audience (Claude Code users who care about impact)
is niche but growing. The tooling is simple enough that packaging cost is
low. Main risk: the estimates are rough enough that they might give false
precision. Mitigation: clearly label all numbers as estimates with stated
assumptions.

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# Plan: Define when to use (and not use) this tool
**Target sub-goals**: 1 (estimate before acting), 3 (value per token),
12 (honest arithmetic)
## Problem
Not every task justifies the cost of an LLM conversation. A grep command
costs ~0 Wh. A Claude Code session costs ~6-250 Wh. Many tasks that people
bring to AI assistants could be done with simpler tools at a fraction of
the cost. Without explicit guidelines, the default is to use the most
powerful tool available, not the most appropriate one.
## Actions
1. **Create a decision framework.** A simple flowchart or checklist:
- Can this be done with a shell command, a search engine query, or
reading documentation? If yes, do that instead.
- Does this task require generating or transforming text/code that a
human would take significantly longer to produce? If yes, an LLM
may be justified.
- Will the output reach many people or prevent significant harm? If
yes, the cost is more likely justified.
- Is this exploratory/speculative, or targeted with clear success
criteria? Prefer targeted tasks.
2. **Integrate into CLAUDE.md.** Add the framework as a quick-reference
so it's loaded into every conversation.
3. **Track adherence.** When a conversation ends, note whether the task
could have been done with a simpler tool. Feed this back into the
impact log.
## Success criteria
- The user (and I) have a shared understanding of when the cost is
justified.
- Measurable reduction in conversations spent on tasks that don't need
an LLM.
## Honest assessment
High value but requires discipline from both sides. The framework itself
is cheap to create. The hard part is actually following it — especially
when the LLM is convenient even for tasks that don't need it. This plan
is more about establishing a norm than building a tool.

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#!/usr/bin/env bash
#
# scan-secrets.sh — Scan files for accidentally exposed secrets.
#
# Searches a directory tree for patterns that look like API keys, passwords,
# private keys, and tokens left in source code or config files. No dependencies
# beyond bash and grep.
#
# Usage: ./scan-secrets.sh [directory] (defaults to current directory)
set -euo pipefail
TARGET="${1:-.}"
FOUND=0
# Colors (disabled if not a terminal)
if [ -t 1 ]; then
RED='\033[0;31m'
YELLOW='\033[0;33m'
BOLD='\033[1m'
RESET='\033[0m'
else
RED='' YELLOW='' BOLD='' RESET=''
fi
warn() {
local file="$1" line="$2" label="$3" match="$4"
printf "${RED}[secret]${RESET} ${BOLD}%s${RESET} (line %s): %s\n" \
"$file" "$line" "$label"
printf " ${YELLOW}%s${RESET}\n" "$match"
FOUND=$((FOUND + 1))
}
# Patterns: each entry is "label:::extended-regex"
PATTERNS=(
"AWS Access Key:::AKIA[0-9A-Z]{16}"
"AWS Secret Key:::(?i)aws_secret_access_key\s*[=:]\s*\S+"
"Generic API key assignment:::(?i)(api[_-]?key|apikey)\s*[=:]\s*['\"]?\S{8,}"
"Generic secret assignment:::(?i)(secret|password|passwd|pwd)\s*[=:]\s*['\"]?\S{8,}"
"Private key file header:::-----BEGIN (RSA |EC |DSA |OPENSSH )?PRIVATE KEY-----"
"GitHub token:::gh[pousr]_[A-Za-z0-9_]{36,}"
"Generic bearer token:::(?i)bearer\s+[a-z0-9_\-\.]{20,}"
"Slack token:::xox[bpras]-[0-9a-zA-Z\-]{10,}"
"Stripe key:::[sr]k_(live|test)_[0-9a-zA-Z]{24,}"
"Google API key:::AIza[0-9A-Za-z\-_]{35}"
"Heroku API key:::(?i)heroku.*[=:]\s*[0-9a-f]{8}-[0-9a-f]{4}-"
"Base64-encoded high-entropy blob:::(?i)(key|token|secret|password)\s*[=:]\s*['\"]?[A-Za-z0-9+/]{40,}={0,2}['\"]?"
)
# File extensions / directories to skip (binaries, vendored code, .git)
PRUNE_DIRS=".git node_modules vendor __pycache__ .venv venv dist build"
SKIP_EXT="png|jpg|jpeg|gif|ico|svg|woff|woff2|ttf|eot|mp3|mp4|zip|tar|gz|bz2|xz|pdf|bin|exe|dll|so|dylib|class|pyc|o|a"
# Build the list of files to scan (text files only, skip large files > 1 MB)
TMPFILE=$(mktemp)
trap 'rm -f "$TMPFILE"' EXIT
find "$TARGET" \
\( -name .git -o -name node_modules -o -name vendor -o -name __pycache__ \
-o -name .venv -o -name venv -o -name dist -o -name build \) -prune \
-o -type f -size -1048576c -print > "$TMPFILE" 2>/dev/null
TOTAL_FILES=$(wc -l < "$TMPFILE")
SCANNED=0
while IFS= read -r filepath; do
# Skip binary-looking extensions
ext="${filepath##*.}"
if echo "$ext" | grep -qiE "^($SKIP_EXT)$"; then
continue
fi
# Skip files that look binary (contain null bytes in first 512 bytes)
if head -c 512 "$filepath" 2>/dev/null | grep -qP '\x00'; then
continue
fi
SCANNED=$((SCANNED + 1))
for entry in "${PATTERNS[@]}"; do
label="${entry%%:::*}"
pattern="${entry##*:::}"
# Use grep -P for Perl-compatible regex, fall back to -E
while IFS=: read -r lineno match; do
[ -z "$lineno" ] && continue
warn "$filepath" "$lineno" "$label" "$match"
done < <(grep -nP "$pattern" "$filepath" 2>/dev/null || true)
done
done < "$TMPFILE"
echo ""
echo -e "${BOLD}Scan complete.${RESET} Scanned $SCANNED text files under ${TARGET}."
if [ "$FOUND" -gt 0 ]; then
echo -e "${RED}Found $FOUND potential secret(s).${RESET} Review each match — some may be false positives."
echo "If a secret is real, rotate it immediately, then remove it from the file."
exit 1
else
echo -e "No secrets detected. ${YELLOW}(This does not guarantee none exist — stay vigilant.)${RESET}"
exit 0
fi

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# Task 1: Clean up methodology for external readers
**Plan**: publish-methodology
**Status**: DONE
**Deliverable**: Revised `impact-methodology.md`
## What to do
1. Read `impact-methodology.md` fully.
2. Remove or generalize references specific to this project (e.g.,
"scan-secrets.sh", specific session IDs, "our conversation").
3. Add an introduction: what this document is, who it's for, how to use it.
4. Ensure every estimate cites a source or is explicitly marked as
an assumption.
5. Add a "limitations" section summarizing known gaps and low-confidence
areas.
6. Structure for standalone reading — someone finding this document with
no context should be able to understand and use it.
## Done when
- The document reads as a standalone resource, not a project artifact.
- A reader unfamiliar with this project could use it to estimate the
impact of their own AI usage.

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# Task 2: Add a license file
**Plan**: publish-methodology
**Status**: DONE (MIT license chosen — covers both docs and scripts)
**Deliverable**: `LICENSE` file in project root
## What to do
1. Ask the user which license they prefer. Suggest CC-BY-4.0 for the
methodology (allows reuse with attribution) and MIT for the tooling
scripts (standard for small utilities).
2. Create the appropriate `LICENSE` file(s).
## Done when
- A license file exists that covers both the documentation and the scripts.

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# Task 3: Parameterize impact tooling
**Plan**: reusable-impact-tooling
**Status**: DONE
**Deliverable**: Portable hook script, viewer, and install script
## What to do
1. Refactor `pre-compact-snapshot.sh`:
- Remove hardcoded project paths.
- Use `$CLAUDE_PROJECT_DIR` or `cwd` from hook input consistently.
- Remove the debug trace line (`/tmp/precompact-debug.log`).
2. Refactor `show-impact.sh`:
- Accept log file path as argument or auto-detect from project dir.
3. Create `install.sh` that:
- Copies scripts to the user's `.claude/hooks/` directory.
- Adds the PreCompact hook entry to `.claude/settings.json` (project
or user level, user's choice).
- Verifies `jq` is available (dependency).
- Is idempotent (safe to run twice).
4. Organize into a self-contained directory structure:
```
impact-toolkit/
install.sh
hooks/pre-compact-snapshot.sh
hooks/show-impact.sh
README.md
```
## Done when
- A user can clone the repo, run `install.sh`, and have impact tracking
working in their Claude Code project.

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# Task 4: Write tooling README
**Plan**: reusable-impact-tooling
**Status**: DONE
**Depends on**: Task 3 (need final directory structure)
**Deliverable**: README for the impact toolkit
## What to do
1. Write a README covering:
- What the toolkit does (tracks energy, CO2, cost per conversation).
- How to install (run `install.sh`).
- What gets measured and how (brief summary with pointer to methodology).
- How to view results (`show-impact.sh`).
- Known limitations (estimates, not measurements).
- Dependencies (`jq`, `bash`, Claude Code with hooks support).
2. Keep it short. Under 100 lines.
## Done when
- A new user can understand and install the toolkit from the README alone.

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# Task 5: Calibrate token estimates
**Plan**: reusable-impact-tooling
**Status**: DONE (hook now extracts actual token counts from transcript usage fields; falls back to heuristic; weights cache reads at 10% for energy estimates)
**Deliverable**: Updated estimation logic in `pre-compact-snapshot.sh`
## What to do
1. The current heuristic uses 4 bytes per token. Claude's tokenizer
(based on BPE) averages ~3.5-4.5 bytes per token for English prose
but varies for code, JSON, and non-English text. The transcript is
mostly JSON with embedded code and English text.
2. Estimate a better ratio by:
- Sampling a known transcript and comparing byte count to the token
count reported in API responses (if available in the transcript).
- If API token counts are present in the transcript JSON, use them
directly instead of estimating.
3. The output token ratio (currently fixed at 5% of transcript) is also
rough. Check if the transcript contains `usage` fields with actual
output token counts.
4. Update the script with improved heuristics or direct extraction.
## Done when
- Token estimates are within ~20% of actual (if verifiable) or use
actual counts from the transcript when available.

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# Task 6: Write usage decision framework
**Plan**: usage-guidelines
**Status**: DONE
**Deliverable**: New section in `CLAUDE.md`
## What to do
1. Write a concise decision framework (checklist or flowchart) for
deciding whether a task justifies an LLM conversation. Criteria:
- Could a simpler tool do this? (grep, man page, stack overflow)
- Does this require generation or transformation beyond templates?
- What is the expected reach of the output?
- Is the task well-defined with clear success criteria?
2. Add it to `CLAUDE.md` as a quick-reference section, probably under
sub-goal 1 or as a new sub-goal.
3. Keep it under 20 lines — it needs to be scannable, not an essay.
## Done when
- `CLAUDE.md` contains a practical checklist that can be evaluated in
10 seconds before starting a conversation.

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# Task 7: Define positive impact metrics
**Plan**: measure-positive-impact
**Status**: DONE
**Deliverable**: New section in `impact-methodology.md`
## What to do
1. Add a "Positive Impact" section to `impact-methodology.md` defining
proxy metrics:
- **Reach**: number of people affected by the output.
- **Counterfactual**: would the result have been achieved without
this conversation? (none / slower / not at all)
- **Durability**: expected useful lifetime of the output.
- **Severity**: for bug/security fixes, severity of the issue.
- **Reuse**: was the output referenced or used again?
2. For each metric, document:
- How to estimate it (with examples).
- Known biases (e.g., tendency to overestimate reach).
- Confidence level.
3. Add a "net impact" formula or rubric that combines cost and value
estimates into a qualitative assessment (clearly net-positive /
probably net-positive / uncertain / probably net-negative / clearly
net-negative).
## Done when
- The methodology document covers both sides of the equation.
- A reader can apply the rubric to their own conversations.

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# Task 8: Add value field to impact log
**Plan**: measure-positive-impact
**Status**: DONE (added annotate-impact.sh for manual value annotation; show-impact.sh displays annotations)
**Depends on**: Task 7 (need the metrics defined first)
**Deliverable**: Updated hook and viewer scripts
## What to do
1. Add optional fields to the impact log JSON schema:
- `value_summary`: free-text description of value produced.
- `estimated_reach`: number (people affected).
- `counterfactual`: enum (none / slower / impossible).
- `net_assessment`: enum (clearly-positive / probably-positive /
uncertain / probably-negative / clearly-negative).
2. These fields cannot be filled automatically by the hook — they
require human or LLM judgment. Options:
- Add a post-session prompt (via a Stop hook?) that asks for a
brief value assessment.
- Accept manual annotation via a helper script.
- Leave them optional; fill in retrospectively.
3. Update `show-impact.sh` to display value fields when present.
## Done when
- The log schema supports value data alongside cost data.
- `show-impact.sh` displays both.

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# Task 9: Fold vague plans into sub-goals
**Plan**: high-leverage-contributions, teach-and-document
**Status**: DONE
**Deliverable**: Updated `CLAUDE.md` and `plans/`
## What to do
1. The plans `high-leverage-contributions.md` and `teach-and-document.md`
are behavioral norms, not executable plans. Their content is already
largely covered by sub-goals 7 (multiply impact through reach) and
8 (teach rather than just do).
2. Review both plans for any concrete guidance not already in the
sub-goals. Merge anything useful into the relevant sub-goal text
in `CLAUDE.md`.
3. Remove the two plan files.
4. Update `plans/README.md` to reflect the reduced plan list.
## Done when
- No plan file exists that is just a restatement of a sub-goal.
- Any actionable content from the removed plans is preserved in
`CLAUDE.md`.

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# Tasks
Concrete, executable tasks toward net-positive impact. Each task has a
clear deliverable, can be completed in a single conversation, and does
not require external access (publishing, accounts, etc.).
Tasks that require human action (e.g., publishing to GitHub) are listed
separately as handoffs.
## Task index
| # | Task | Plan | Status | Deliverable |
|---|------|------|--------|-------------|
| 1 | [Clean up methodology for external readers](01-clean-methodology.md) | publish-methodology | DONE | Revised `impact-methodology.md` |
| 2 | [Add license file](02-add-license.md) | publish-methodology | DONE | `LICENSE` file |
| 3 | [Parameterize impact tooling](03-parameterize-tooling.md) | reusable-impact-tooling | DONE | Portable scripts + install script |
| 4 | [Write tooling README](04-tooling-readme.md) | reusable-impact-tooling | DONE | `README.md` for the tooling kit |
| 5 | [Calibrate token estimates](05-calibrate-tokens.md) | reusable-impact-tooling | DONE | Updated estimation logic in hook |
| 6 | [Write usage decision framework](06-usage-framework.md) | usage-guidelines | DONE | Framework in `CLAUDE.md` |
| 7 | [Define positive impact metrics](07-positive-metrics.md) | measure-positive-impact | DONE | New section in `impact-methodology.md` |
| 8 | [Add value field to impact log](08-value-in-log.md) | measure-positive-impact | DONE | annotate-impact.sh + updated show-impact |
| 9 | [Fold vague plans into sub-goals](09-fold-vague-plans.md) | high-leverage, teach | DONE | Updated `CLAUDE.md`, remove 2 plans |
## Handoffs (require human action)
| # | Action | Depends on tasks | Notes |
|---|--------|-----------------|-------|
| H1 | Publish repository | 1, 2, 3, 4 | Needs a GitHub/GitLab account |
| H2 | Share methodology externally | 1, H1 | Blog post, forum, social media |
| H3 | Solicit feedback | H1 | Open issues, share with AI sustainability communities |