data-effectiveness-auditlisted

<!-- prettier-ignore-start --> **Document Version:** 1.0 **Last Updated:** 2026-03-13 **Status:** ACTIVE <!-- prettier-ignore-end --> # Data Effectiveness Audit Systematic audit of all data systems to identify write-only data, broken feedback loops, and enforcement gaps. Uses lifecycle scoring (Capture/Storage/Recall/Action, 0-3 each, 0-12 total) to quantify effectiveness and route gaps through the learning-to-automation pipeline. **Invocation:** `/data-effectiveness-audit` ## Critical Rules (MUST follow) 1. **READ lifecycle-scores.jsonl first** (MUST) — never generate findings without current data. 2. **Present findings interactively** (MUST) — one system at a time, worst first, with user decisions. 3. **Use conversational Q&A for decisions** (MUST) — NEVER use AskUserQuestion. 4. **Update scores after decisions** (MUST) — write to lifecycle-scores.jsonl immediately. 5. **Regenerate dashboard after updates** (MUST) — run `node scripts/generate-lifecycle-scores-md.js`. 6. **Route gaps through pipeline** (MUST) — Action<2 gaps routed via `scripts/route-lifecycle-gaps.js`. 7. **Create TDMS entries** (MUST) — for deferred findings via `/add-debt`. --- ## When to Use - User explicitly invokes `/data-effectiveness-audit` - Periodic health check on data pipeline effectiveness - After adding new JSONL files or data systems - When health score Data Effectiveness dimension drops - When `/alerts` surfaces data staleness warnings ## When NOT to Use - For individ