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