# Code Health

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

- Skill: `repowise-dev/code-health` (Agent Skill)
- Install (CLI): `npx skillmds@latest add repowise-dev/code-health`
- Raw SKILL.md: https://api.skillmd.com/api/skills/repowise-dev/code-health/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: repowise-dev (https://skillmd.com/u/repowise-dev)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/repowise-dev/code-health

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# 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 `/prompts: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.