brainstorming
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, modifying behavior, or planning anything new, in software or out of it (a talk, a business, a renovation).
reviewAudit the debt registry, rank survivors by churn × Fowler quadrant, surface a top-N list, then walk paydown on user follow-up. Use when the user asks to review debt, see what to pay down, work through entries, or invokes $review. Stale entries drop with "drop A,B,C".
Independent GitHub snapshot · Automated classification · Original documentation governs
Two modes. First turn: print the audit and stop. On a user follow-up ("fix the top one," "walk these," "do A," "pay some down"), apply the rubric below.
Run the bundled review.py (it lives in this skill's scripts/ directory — reference it with the relative path; Codex resolves it against the skill root):
python3 scripts/review.py
Optional: --top N to surface more than the default 3 candidates.
Re-emit the helper's stdout verbatim in a fenced code block. Codex may collapse long bash outputs — if you don't print it yourself, the user might not see it. Copy exactly: no preamble, no summary, no "want me to fix the top one?" The fenced block preserves column alignment.
Then stop. The user picks the next move.
Work through requested entries one at a time. Confirm before each fix. Never auto-batch. Never auto-commit.
For each entry, read the registry file, the hotspot, and adjacent tests. Apply this rubric:
created: and age >90d → propose deferring. ~20% of files generate ~80% of debt-related rework; don't pay down vanity refactors.payoff_trigger or body — don't commit code you can't explain.ai_authored: true → run a fresh-context review of the diff before suggesting commit. Fresh-context review catches what the writer's motivated reasoning misses.drop A, and commits.Aim for 3–10 entries per session — continuous paydown outperforms stop-the-world batches. If the user says "do them all," push back once: unsupervised AI cleanup measurably increases duplicate blocks and short-term churn. If they insist, still one-at-a-time with diffs surfaced.
The frontmatter uses a research taxonomy (quadrant, category) for ranking and grounding — it is not user-facing vocabulary. When you talk about an entry, describe it in plain words; never say "prudent-inadvertent", "reckless-deliberate", "code_rot", etc. to the user. Use the entry's body and a plain phrase (e.g. "a planned tradeoff", "a shortcut you knew about", "came up later") instead. The review.py output is already translated — match its tone.
review.py.