Run a recursive-learning retrospective on an app/agent/plugin/build-loop project — analyze build history, behavior, and current state to extract reusable learning objects and encode them into memory, agents, skills, plugins, evals, preflights, and approval gates. Use after a substantial build, when reviewing a project's trajectory, when deciding preserve/refine/redirect/reset, or when converting a session into durable system improvement. Discovery-first (observe behavior → cluster patterns → encode learning); RCA is one module, not the whole job.
التثبيت
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
Run a recursive-learning retrospective on an app/agent/plugin/build-loop project — analyze build history, behavior, and current state to extract reusable learning objects and encode them into memory, agents, skills, plugins, evals, preflights, and approval gates. Use after a substantial build, when reviewing a project's trajectory, when deciding preserve/refine/redirect/reset, or when converting a session into durable system improvement. Discovery-first (observe behavior → cluster patterns → encode learning); RCA is one module, not the whole job.
user-invocable
false
Recursive Learning Retrospective
A three-stage pipeline that turns a project's history into durable, encoded system improvement. Discovery-first: observe revealed behavior, cluster patterns, then encode learning — categories are seed scaffolds, never closed taxonomies (preserve emergent findings).
v2 (2026-06-18) — test-validated. This revision was hardened against a live run: the full pipeline was executed on a real project (ross-labs-astro) and scored by an independent judge at 4.27/5, "Accept with revisions." The judge's penalties are fixed here (see Changelog). The v1 structure (peer draft feat/recursive-retrospective@1c24d4a) is preserved; only the validated fixes are layered on.
When to use
After a substantial build/session, to extract what the system should learn.
When deciding whether to preserve / refine / redirect / reset a mid-build project.
To convert a retrospective into concrete learning objects (memory, agent rules, skills, plugin/app behavior, evals, preflights, approval gates).
NOT only for failures — RCA is one diagnostic module used when there are failures, regressions, steering loops, brittle decisions, or outcome gaps.
The pipeline
Run — references/01-retrospective.md (Prompt 1): the recursive-learning retrospective. Produces maturity classification, spec→current→desired comparison, behavior/workflow discovery, steering mining, learning objects, a selective RCA module, preflight/hard-gate analysis, a counterfactual simulation, a prioritized (ordinal) roadmap, layered recommendations, a from-scratch option, emergent patterns, and an executive summary.
Capture — references/02-learning-capture.md (Prompt 2): a PACKAGER/DEDUPER of Prompt 1's §7 + §12. It does not re-extract from scratch; it dedupes the learning objects Prompt 1 already produced, adds only net-new ones it missed (flagged), and emits the copy-ready encoding package.
Judge — references/03-judge.md (Prompt 3): an INDEPENDENT evaluator scores the retrospective on 11 criteria and returns accept / accept-with-revisions / reject. It MUST verify headline claims against source before scoring evidence grounding.
Operating rules (carried through all three stages)
Discovery flexibility: start from evidence before classification; add/rename/split/merge categories when evidence requires; use Other/Emergent; preserve unexpected findings.
Density governor: reward density, not completeness. Collapse a low-signal section to a single line; omit a conditional section entirely when it yields no non-obvious finding. A shorter retrospective that drops empty scaffolding scores HIGHER than an exhaustive one. Do not pad a section to look complete.
Cross-reference, don't restate: assign each finding a stable id (LO-1, RCA-1, …) once, then reference the id in later sections instead of repeating the finding.
Evidence discipline: separate explicit statements from revealed behavior; mark unknowns UNKNOWN — evidence not available and unverified claims TAG:INFERRED.
Memory discipline: do not encode one-off comments as durable memory unless explicitly framed durable or recurring.
Appropriate autonomy + approval authority: the dispatching (main) agent holds approval for this pipeline's own outputs — SAFE, reversible encodes (memory / note / skill-source / agent-instruction) auto-persist under that authority and are never bounced to the human. Reserve human gates for the hard-gate taxonomy only: security, privacy, cost, live/production deploy, irreversible/destructive actions, or promoting an experimental artifact into a globally runtime-active skill/agent.
No false precision: rank by ordinal priority (P0/P1/P2) with qualitative justification; do not emit multiplied point-scores that imply a calibration the inputs cannot support.
Model tiering
Stage 1 (retrospective = assessment) and Stage 3 (judge = verification verdict) are Frontier-tier work (Fable) per the model org. Stage 3 MUST run in a context independent of Stage 1's author (external evaluator). Frontier-unavailable → Thinking-tier (Opus) fallback, never Code tier.
Stage 2 (capture) can run at the executor tier.
Output homes
Retrospective + learning-object package → the project's retrospective lane and (for cross-project learning) build-loop-memory via the canonical memory_writer.py.
Learning objects with encoding target = memory / project-note / skill-source / agent-instruction are SAFE + reversible: the dispatching (main) agent approves them and they persist automatically — do not bounce a medium-confidence memory note to the human. Human approval is reserved for the hard-gate taxonomy only (security / privacy / cost / live-deploy / irreversible-destructive / promoting an experimental artifact into a globally runtime-active skill or agent).
Changelog (v1 → v2; each fix traces to the live judge run)
Density governor + conditional sections — judge flagged §11 (counterfactual) and the near-1:1 §5 behavior→LO mapping as forced-completeness padding. §11 and parts of §5/§15 are now conditional; the density rule is explicit in the operating rules and acceptance criteria.
Ordinal priority replaces the multiply formula — judge flagged (F×I×R×C)/Difficulty point-scores (250, 200, 160) as false precision. §12 now ranks P0/P1/P2 with F/I/R/C/Difficulty kept as qualitative justification columns only.
Cross-reference rule — judge flagged §5/§6/§7/§15 reformatting the same findings 3–4×. Findings now carry ids and are referenced, not restated.
Prompt 2 reframed as packager/deduper — Prompt 1 §7 already emits a full learning-object table; v1 Prompt 2 re-extracted from scratch (double work, divergence risk). v2 Prompt 2 ingests §7+§12 and dedupes.
Prompt 3 mandates verification — in the live test the judge only verified facts because it was told to. v2 §1 (evidence grounding) requires independent tool-verification of ≥3 headline claims when source is available, and caps the grounding score at 3 if verification was possible but skipped.
