claude
eaf0a6cbeb
New show-review-delta.sh compares AI-edited files (from impact log) against git commits to show overlap percentage. High overlap means most committed code was AI-generated with minimal human review. Completes Phase 2 of the quantify-social-costs plan. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
105 lines
3.7 KiB
Markdown
105 lines
3.7 KiB
Markdown
# Claude Code Impact Toolkit
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Track the environmental and financial cost of your Claude Code
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conversations.
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## What it does
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A PreCompact hook that runs before each context compaction, capturing:
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- Token counts (actual from transcript or heuristic estimate)
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- Cache usage breakdown (creation vs. read)
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- Energy consumption estimate (Wh)
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- CO2 emissions estimate (grams)
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- Financial cost estimate (USD)
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- Model ID
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- Automation ratio (AI output vs. user input — deskilling proxy)
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- File churn (edits per file — code quality proxy)
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- Test pass/fail counts
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- Public push detection (data pollution risk flag)
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Data is logged to a JSONL file for analysis over time.
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## Install
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```bash
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# Project-level (recommended)
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cd your-project
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./path/to/impact-toolkit/install.sh
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# Or user-level (applies to all projects)
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./path/to/impact-toolkit/install.sh --user
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```
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Requirements: `bash`, `jq`, `python3`.
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## View results
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```bash
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.claude/hooks/show-impact.sh # per-session details
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.claude/hooks/show-impact.sh <session_id> # specific session
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.claude/hooks/show-aggregate.sh # portfolio-level dashboard
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.claude/hooks/show-review-delta.sh # AI vs human code overlap
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.claude/hooks/show-review-delta.sh 20 # analyze last 20 commits
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```
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## How it works
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The hook fires before Claude Code compacts your conversation context.
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It reads the conversation transcript, extracts token usage data from
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API response metadata, and calculates cost estimates using:
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- **Energy**: 0.1 Wh/1K input tokens, 0.5 Wh/1K output tokens
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(midpoint of range calibrated against Google and Jegham et al., 2025)
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- **PUE**: 1.2 (data center overhead)
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- **CO2**: 325g/kWh (US grid average for cloud regions)
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- **Cost**: $15/M input tokens, $75/M output tokens
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Cache-read tokens are weighted at 10% of full cost (they skip most
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computation).
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## Related tools
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This toolkit measures a subset of the costs covered by
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`impact-methodology.md`. For more precise environmental measurement,
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consider these complementary tools:
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- **[EcoLogits](https://ecologits.ai/)** — Python library that tracks
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per-query energy and CO2 for API calls to OpenAI, Anthropic, Mistral,
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and others. More precise than our estimates for environmental metrics.
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- **[CodeCarbon](https://codecarbon.io/)** — Measures GPU/CPU energy for
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local training and inference workloads.
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- **[Hugging Face AI Energy Score](https://huggingface.github.io/AIEnergyScore/)** —
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Benchmarks model energy efficiency. Useful for choosing between models.
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These tools focus on environmental metrics only. This toolkit also
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tracks financial cost and proxy metrics for social costs (automation
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ratio, file churn, test outcomes, public push detection). The
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accompanying methodology covers additional dimensions in depth.
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## Limitations
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- All numbers are estimates with low to medium confidence.
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- Energy-per-token figures are calibrated against published research
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(Google, Aug 2025; Jegham et al., May 2025), not official Anthropic data.
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- The hook only runs on context compaction, not at conversation end.
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Short conversations that never compact will not be logged.
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- This toolkit only works with Claude Code. The methodology itself is
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tool-agnostic.
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- See `impact-methodology.md` for the full methodology, uncertainty
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analysis, and non-quantifiable costs.
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## Files
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```
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impact-toolkit/
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install.sh # installer
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hooks/pre-compact-snapshot.sh # PreCompact hook
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hooks/show-impact.sh # per-session log viewer
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hooks/show-aggregate.sh # portfolio-level dashboard
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hooks/show-review-delta.sh # AI vs human code overlap
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README.md # this file
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```
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## License
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CC0 1.0 Universal (public domain). See LICENSE in the repository root.
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