jparkerweb
b78b3cd6a3
Moves the README's deep material into .readme/ (walkthrough, autonomous loop, status line, metrics, test bot) and brings AGENTS.md, the .agents-docs set and QUICK-REFERENCE.md in line with Step 0 and the metrics ingestion flow. AI Assisted
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Metrics & Self-Improvement
plan2code-metrics closes the loop on the workflow itself: it collects data from your finished
specs, aggregates it across runs and prompt generations, then uses AI to diagnose which step is
underperforming and propose edits to the workflow prompts.
Aimed at contributors and heavy users — you don't need it to use Plan2Code.
The habit
One thing to remember: collect after every finished spec. Everything else is on demand.
# Install once, from the plan2code root
node install.js # A (everything + dev tools) — or C → M (metrics only)
# After finishing a spec (steps 1–4)
cd your-project
plan2code-metrics # → "Collect metrics" → pick the spec dir → done, ~5 seconds
Then, when you're curious or have a few runs banked:
plan2code-metrics # → "View metrics status" the dashboard
# → "Run analysis" AI diagnosis of weak steps
# → "Generate improvement proposal" concrete prompt edits
# → "Review and apply" patch src/plan2code-*.md
How much data you need
| Runs | What you get |
|---|---|
| 1 | Raw data and a basic dashboard. Start here. |
| 3+ | AI analysis unlocks. Pattern detection starts working. |
| 5–10+ | Averages stabilise; generation-over-generation comparisons become meaningful. |
You're looking for trends, not individual scores.
Sending feedback upstream
/plan2code-4-finalize can submit an anonymised metrics payload to the maintainers as a
community-feedback issue on the repo. Community runs are cohorted by the Plan2Code version that
produced them, so your data improves the prompts everyone installs — without displacing the
maintainer's own measurements.
Full documentation
Data model, aggregation, cohorts, analysis prompts, and the ingestion flow:
plan2code-metrics/README.md