code-health

name	code-health
description	Use when the user asks about code health, code quality, complexity, technical debt, which files are risky or hard to maintain, what to refactor next, untested hotspots, or coverage gaps in a Repowise-indexed codebase (.repowise/ directory exists). Also use to get a before/after health read when planning or finishing a refactor.
user-invocable	false

Code Health with Repowise

Repowise scores every file 1–10 from deterministic markers — McCabe complexity, deep nesting, brain methods, class cohesion (LCOM4), god classes, clone detection, untested hotspots, function-level churn, ownership dispersion, and more. Zero LLM calls; pure local analysis. The weights are calibrated against a real defect corpus, so a low score means more likely to harbour bugs, not just bigger.

Pick the mode by what you pass

• Dashboard — get_health() (no targets): a directive naming what to fix first, then repo-level KPIs and the lowest-scoring files. Start here for "how healthy is this codebase?" or "what should we clean up?".
• Targeted — get_health(targets=["src/x.py", "src/y.py"]): per-file score and the specific marker findings driving it. Use before/after a refactor, or to explain why a file is flagged.

Useful include flags

get_health(targets=[...], include=[...]):

• "biomarkers" — always return the findings list (what's wrong, where).
• "refactoring" — deterministic, ranked refactoring suggestions (by impact/effort).
• "coverage" — surface coverage data when it's been ingested.
• "trend" — recent health snapshots + declining / predicted-decline signal.

include adds blocks; only=[...] subtracts them.

How to use the results

1. For "what should I refactor?" → dashboard mode, lead with directive, then get_health(targets=[worst files], include=["refactoring"]) and present the ranked plans, not just the scores.
2. Rank by weighted_deficit, not score — the score floors at 1.0.
3. For a specific file → report the score, the top 2–3 marker findings, and what each one means in plain language. Avoid dumping the raw payload.
4. Check unresolved before calling a file clean: a target listed there matched nothing, and not_indexed means run repowise update.
5. Before editing a flagged file → cross-check get_risk(targets=[...]); a file that is both low-health and a churn hotspot deserves the most care.
6. Untested-hotspot / coverage questions → tell the user coverage markers light up once they ingest a report: repowise coverage add cov.lcov (LCOV / Cobertura / Clover; a coverage.py .coverage also builds the per-test map), then re-run repowise health.

CLI equivalents

• repowise health — KPIs + lowest-scoring files
• repowise health --refactoring-targets — ranked by impact / effort
• repowise health --trend — snapshots + declining alerts
• repowise coverage add <file> — ingest coverage, light up untested-hotspot

Error handling

If get_health reports no repository, suggest /repowise:init. Code health is computed even with a template-rendered wiki (no LLM needed), so it should be available whenever the repo is indexed.