Repowise computes a 1–10 health score for every file in your repo from twelve deterministic biomarkers — McCabe complexity, deep nesting, brain methods, clone detection, untested hotspots, organizational risk, and more. No LLM calls, no cloud requirement. Pure Python over tree-sitter + git data, designed to finish in under 30 seconds on a 3 000-file repo.

repowise init          # full index — populates health tables
repowise health        # KPIs + 20 worst-scoring files + top findings
repowise update        # re-score only changed files on each subsequent run

Open http://localhost:7777/repos/<id>/health for the dashboard once the local server is running (repowise serve).

Each file starts at 10.0. Biomarker findings deduct from the score; deductions are capped per category so any one category can drive the score down by at most:

The final score is clamped to [1.0, 10.0]. The three repo-level KPIs:

• Hotspot Health — NLOC-weighted average over the top-25 % hotspot files.
• Average Health — NLOC-weighted average over all files.
• Worst Performer — single lowest-scoring file.

brain_method — A single function that is simultaneously long, deeply nested, highly complex, and central to the dependency graph. The strongest single signal of fragile code.

nested_complexity — Functions with control-flow nesting ≥ 4 levels. Hard to read, hard to test, hard to refactor.

bumpy_road — Multiple branches stacked at the same depth — usually a sign the function is doing several jobs that should be split.

complex_method — Cyclomatic complexity ≥ 9. Each branch is a path the test suite has to cover.

large_method — Functions that exceed the NLOC threshold. Length on its own is not always a bug, so this is a milder signal.

primitive_obsession — Many primitive parameters in one signature. A dataclass or parameter object would name the inputs.

dry_violation — Cross-file code clones, detected by a native Rabin–Karp rolling hash over tree-sitter tokens (variable renames don't hide a clone). Pairs are ranked by co-change so dormant duplicates rank lower than active ones.

untested_hotspot — A hotspot file with low or zero coverage and many dependents. The textbook "write tests before refactoring" case.

coverage_gap — Non-test files with meaningful uncovered surface. Severity grades along coverage depth.

developer_congestion — Too many active authors touching the same file. Usually an ownership problem dressed up as a code problem.

knowledge_loss — The primary authors of the file are no longer active on the project. Refactor while someone still remembers why.

Pass coverage reports straight into the analyzer:

pytest --cov --cov-report=lcov:coverage.lcov
repowise health --coverage coverage.lcov

# Cobertura, Clover, or multiple sources also work:
repowise health \
  --coverage backend/coverage.xml --coverage-format cobertura \
  --coverage frontend/lcov.info

Coverage data feeds into untested_hotspot and coverage_gap, and shows up on the /repos/<id>/health/coverage dashboard.

repowise health --refactoring-targets

Ranks candidates by total_impact / effort_bucket so the biggest wins for the least work surface first. Each row carries a deterministic, rule-based suggestion ("Split this function. It carries high cyclomatic complexity...").

For agentic workflows, the same data is one MCP call away:

get_health(include=["refactoring"])           # dashboard + suggestions
get_health(targets=["src/api/server.py"])     # one file in detail
get_health(targets=["module:src.api"])        # everything in a module

Every health run writes a HealthSnapshot row (rolling 50 entries per repo). Two alerts run over the history:

• Declining Health — current hotspot_health is ≥ 0.5 below the snapshot 5 runs ago.
• Predicted Decline — the three most recent snapshots are each strictly below the one before.

Inspect from the CLI:

repowise health --trend

Or from MCP:

get_health(include=["trend"])

Per-file overrides live in .repowise/health-rules.json:

{
  "disabled_biomarkers": ["primitive_obsession"],
  "rules": [
    {
      "glob": "tests/**/*.py",
      "disabled_biomarkers": ["large_method", "complex_method"]
    },
    {
      "glob": "src/legacy/**",
      "disabled_biomarkers": ["dry_violation"]
    }
  ]
}

repowise update only re-scores the changed files. Findings and metrics for unchanged files stay put — no nightly full re-index needed.

repowise status includes a single-line health summary:

Health: 7.4 (avg) · 6.2 (hotspots) · 2.1 (worst: payments/processor.ts)

• packages/core/src/repowise/core/analysis/health/README.md — developer overview of the layer.
• Sub-package READMEs: complexity/, coverage/, duplication/, biomarkers/.