agentrepocoach 0.3.1

Score your codebase on how ready it is for AI agents — and coach you through the fixes.

AgentRepoCoach

Score your codebase on how ready it is for AI agents — and coach you through the fixes.

AgentRepoCoach computes the Codebase Agent Health (CAH) score: a single 0-100 composite measuring how friendly a repository is for autonomous AI agents. It blends five statically-measurable components:

• Navigability (25%) — AGENTS.md, codebase map, CLI manifest, root cleanliness
• Error quality (25%) — fix-hint coverage, exception typing, generic-exception dominance
• Decision queryability (20%) — ADR catalog, inline reference resolution
• Test quality (15%) — naming convention, helper presence, fixture duplication
• Module hygiene (15%) — internal visibility, god files, doc coverage, architecture doc freshness

AgentRepoCoach ships with zero runtime dependencies — it uses the Python 3.11+ standard library only, including tomllib for config parsing.

Usage as a GitHub Action

Add this to any workflow to score your repo and fail the build if the composite score drops below a threshold:

name: codebase-health

on:
  push:
    branches: [main]
  pull_request:

jobs:
  agentrepocoach:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Run AgentRepoCoach
        id: agentrepocoach
        uses: WouterDeBot/agentrepocoach@v1
        with:
          repo-path: .
          output-format: json
          output-path: ./agentrepocoach-report.json
          fail-threshold: '70'

      - name: Show composite score
        run: echo "Score: ${{ steps.agentrepocoach.outputs.composite-score }}"

      - uses: actions/upload-artifact@v4
        with:
          name: agentrepocoach-report
          path: ./agentrepocoach-report.json

Action inputs:

Input	Default	Description
repo-path	.	Path to scan
config-path	.agentrepocoach.toml	TOML config file
output-format	json	json, markdown, or both
output-path	./agentrepocoach-report.json	Where to write the report
fail-threshold	(unset)	Exit 1 if score < threshold
python-version	3.11	Python version to install

Action outputs:

Output	Description
composite-score	CAH composite score (0-100)
report-path	Absolute path to written report

Usage as a CLI

pip install agentrepocoach

# Score the current directory (prints a summary table)
python -m agentrepocoach.cli --repo .

# Write a JSON report to disk
python -m agentrepocoach.cli --repo . --format json --output ./report.json

# Per-sub-component breakdown
python -m agentrepocoach.cli --repo . --verbose

# Compare against a baseline report (inline delta)
python -m agentrepocoach.cli --repo . --format json --output ./pr.json --compare ./baseline.json

# Compare two saved score files
python -m agentrepocoach.cli compare ./baseline.json ./pr.json

# Show the installed version
python -m agentrepocoach.cli --version

Configuration

AgentRepoCoach looks for .agentrepocoach.toml at the repo root. Every field is optional — the tool ships with sensible defaults and will score zero-config repos without complaint.

Minimal example:

# .agentrepocoach.toml
[weights]
navigability = 0.25
error_quality = 0.25
decision_queryability = 0.20
test_quality = 0.15
module_hygiene = 0.15

[paths]
adr_dir = "docs/adr/"
architecture_doc = "docs/architecture.md"

[error_quality]
domain_exception_types = ["DomainError", "ValidationError"]

[decision_queryability]
inline_ref_patterns = ["ADR-\\d+"]

See docs/METHODOLOGY.md for the full config schema and scoring formula.

Languages supported

Language	Status	Notes
C#	Full MVP	Throw-site scanner, XML doc detection, internal visibility, .sln/.csproj discovery
Python	Full MVP	Raise-site scanner, docstring detection, top-level visibility, src/ layout aware
TypeScript	Full MVP	Throw-site scanner with multi-line context, JSDoc detection, Jest/Vitest test extraction
Rust	Full MVP	panic!/Err(Custom) mapping, /// doc comment detection, #[test] attribute detection
Go	Full MVP	errors.New/fmt.Errorf/custom error mapping, Go doc comment detection, Test* function extraction

Adding a new adapter is a small, well-scoped task — see CONTRIBUTING.md.

Coaching recommendations

When you run AgentRepoCoach, it doesn't just score your repo — it tells you what to fix first. The coaching engine analyzes sub-component score gaps and surfaces the top-3 actionable tips ranked by weighted impact. Recommendations appear in:

• Terminal summary (default output)
• Verbose mode (--verbose)
• Markdown PR comments (--format markdown)
• JSON reports (--format json, new coaching array)

How it works

AgentRepoCoach detects the primary language of the repo, loads a language adapter, and runs five component scorers against the adapter's view of the codebase. Each component returns a 0-100 sub-score with a transparent breakdown. The weighted sum is the composite CAH score.

Every output field is a count, percentage, type name, or file path — AgentRepoCoach never emits code snippets or raw message bodies, so reports are safe to publish as CI artifacts.

License

Apache 2.0. See LICENSE.