retro

name	retro
description	Run the measured loop-engineering pass: audit ledger/usage quality, evaluate executable incident cases and crystallized rules, mine recurrence and rework, then propose verified process changes for human approval.
license	MIT

retro

The loop that improves the loop. A historical problem becomes a cheap repeatable experiment; a rule becomes active only after that experiment proves it can kill the bad variant.

Load

• ${CLAUDE_PLUGIN_ROOT}/shared/core-contract.md
• ${CLAUDE_PLUGIN_ROOT}/shared/formats/ledger-format.md
• ${CLAUDE_PLUGIN_ROOT}/shared/formats/benchmark-case-format.md
• ${CLAUDE_PLUGIN_ROOT}/shared/formats/report-style.md

Process

1. Data-quality preflight: audit every active feature/error ledger and generated acceptance receipt. Count eligible phase/gate/dispatch entries, cost coverage, unmapped sessions, invalid gaps, command/provider capture, and benchmark linkage. Missing cost data creates or updates one stable observe gap with its label cap; never duplicate it.
2. Collect: feature summaries, exact-match error pre-groups, semantic clusters, normalized Codex/Claude usage, benchmark reports, and crystallized rules. Label script findings separately from semantic judgment.
3. Compute: measured/no-data cost by phase, time to first runnable core path, command and repeated-test counts, subtask/context modes, review/repair rounds, provider calls/cost, rework routing, user corrections, post-completion fixes, recurring clusters, open gaps, benchmark coverage, and false-green gates. Do not call missing capture “zero recurrence” or “efficient.”
4. Executable case check: every P0/P1, irreversible-risk, false-green, or recurring incident links a project case, or a human-approved waiver. Profiles reproduce one problem at different cost. Prefer the cheapest stable profile and report when real calibration is stale.
5. Crystallize: propose the lowest enforcement layer. Approval records approved; building records implemented; only a linked GREEN → RED → GREEN report promotes verified, then active-unmeasured until later observations exist.
6. Measure: after approval, count linked recurrences and rule fires. Zero with healthy capture becomes effective; recurrence becomes recurring and proposes a lower layer; 8 weeks unfired or a model major-version change becomes a retirement candidate.
7. Human report: write docs/opc/retro/<date>.json and .md, then render/lint docs/opc/retro/<date>.html. Lead with conclusion, user impact, evidence, and decision. Only human-approved rule changes are written to docs/opc/rules.md.

Fail-open

Missing telemetry or environments cap claims and create gaps; they never justify fabricated metrics. Destructive benchmark profiles and real external environments require explicit approval.

Output

Structured JSON and markdown truth, mandatory plain-language HTML, approved rule lifecycle updates, and linked benchmark evidence.