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): fix_first, the ranked list of 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 fix_first; open an item with get_health(fix_id=...) for its steps and the tests to run, and present the plan, 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 repowise health --recompute to fold it into the scores.

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.