From debt-ops
Audits a debt registry, ranks entries by churn and Fowler quadrant, surfaces top candidates, and walks paydown on user follow-up.
How this skill is triggered — by the user, by Claude, or both
Slash command
/debt-ops:reviewThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
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.
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.
python3 ${CLAUDE_PLUGIN_ROOT}/skills/review/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. Claude Code collapses long bash outputs behind a +N lines, ctrl+o to expand placeholder — if you don't print it yourself, the user never sees 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 → invoke /code-review on 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.Guides creation, editing, and verification of skills for AI coding agents using test-driven development with subagent scenarios. Use when authoring or debugging skills.
npx claudepluginhub bcanfield/agentic-tech-debt --plugin debt-ops