Initial commit: AI conversation impact methodology and toolkit

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.

tasks/07-positive-metrics.md

@@ -0,0 +1,31 @@
+# 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.