AI Readiness Analyzer

Deterministic AI-readiness scoring for GitHub Copilot and Claude Code repository configuration.

Point it at a repository. Get back a reproducible score, a letter grade, and a ranked list of concrete fixes — computed entirely by static analysis, with zero model calls in the scoring path. Same commit in, byte-identical report out, every time.

Status: v0.2.0 "Fable" — pre-1.0. All eight scoring pillars are live with a catalog of 94 rules, platform sub-scores, waivers, and terminal/JSON/Markdown/SARIF reports. The original design document is plan.md; the v0.2.0 refactor plan is plan-v2-fable.md; the full generated rule catalog is docs/RULES.md. See CHANGELOG.md for exactly what's built.

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Why this exists

Repositories increasingly ship configuration meant for AI coding agents — copilot-instructions.md, CLAUDE.md, AGENTS.md, SKILL.md files, custom agents, path-scoped instructions, hooks, MCP servers. Whether any of it actually works is usually invisible until an agent silently fails to load a skill, ignores a 900-line instructions file, or never triggers a skill because its description is too vague.

AI Readiness Analyzer answers "is this repo actually ready for an AI agent?" the same way a linter answers "does this code compile" — deterministically, offline, and with a specific file and line number for every issue.

What it checks

Eight pillars, 94 rules (full catalog):

Pillar	Weight	What it covers
Foundation	20	Entry points exist and parse; AGENTS.md↔CLAUDE.md bridge; length and structure; section coverage; @import resolution
Instruction quality	15	Specific, justified, exemplified directives; no boilerplate, stale markers, or credential-shaped strings; commands and links resolve
Context scoping	12	Path-scoped *.instructions.md; the missing-applyTo silent no-op; dead globs; monolith detection
Skills	15	The full Agent Skills validation set: frontmatter, the dirname-match silent-drop trap, description quality (0–100), token budgets, CWE-59 reference escapes, progressive disclosure
Agents & prompts	10	Custom-agent frontmatter, description quality, least-privilege tools, prompt→agent reference resolution
Verification	12	Documented and resolvable test/build/lint commands; CI; iterate-until-green and show-evidence instructions; hooks schema
Tooling	8	MCP config validity and secret indirection; setup scripts; devcontainer; version pins
Safety	8	Committed personal files; permission-bypass settings; secrets in settings/MCP; curl | sh injection surface

Quickstart

git clone https://github.com/YoavLax/AI-Repo-Analyzer.git
cd AI-Repo-Analyzer
python -m venv .venv
# Windows: .venv\Scripts\activate   |   macOS/Linux: source .venv/bin/activate
pip install -e .

airx analyze /path/to/some/repo

AI Readiness Analyzer — /path/to/some/repo

Overall score: 61.1/100   Grade: D
Platforms:     copilot 62.5   claude 61.1   parity delta 1.4

Pillars:
  foundation      86.4%   (presence 100.0%, quality  77.3%, weight 20, 9 rules)
  skills          99.8%   (presence 100.0%, quality  99.6%, weight 15, 37 rules)
  ...

Findings (20):
  [error  ] skills.name.dirname-match   .github/skills/deploy/SKILL.md
            Name 'deployer' does not match parent directory 'deploy'. VS Code/Copilot silently fails to load this skill.

Top fixes (estimated score gain):
  1. +4.8  [additive      ] verify.test-command.documented
     Document the repository's test command in an entry point so agents can verify their work.

CI usage

airx analyze . --format json -o report.json     # canonical machine output
airx analyze . --format sarif -o airx.sarif     # GitHub code scanning
airx analyze . --format md                      # PR comment / job summary
airx analyze . --min-score 70 --fail-on error   # quality gate
airx compare baseline.json report.json          # exit 1 on regression

Exit codes: 0 passed · 1 gate failed · 2 input/config error · 3 internal error.

Configuration (.airx.yml)

airx init scaffolds it:

profile: standard        # or: minimal, enterprise (weight profiles)
min_score: 70
fail_on: error
ignore:
  - skills.compat.unverified
waivers:
  - rule: skills.present
    reason: "Domain knowledge lives in an internal plugin marketplace."
    expires: "2027-01-01"
    approved_by: platform-team

Waived rules score as satisfied but stay visible in the report. Waiver expiry is only evaluated against an explicit date (--today 2026-07-29 or AIRX_TODAY) — the scoring path never reads the clock, so output stays reproducible.

How it works

path → fs.scan            deterministic, symlink-free traversal
     → discovery          declarative artifact patterns (skills, agents, prompts,
                          instructions, hooks, MCP, settings — see src/airx/patterns.py)
     → probe              repo facts: test/build/lint evidence, CI, hygiene
     → rules/*            94 pure functions, one per check, in a versioned registry
     → scoring            presence/quality split per pillar, platform sub-scores,
                          profiles, waivers, grade banding
     → report/*           terminal | json | markdown | sarif + ranked remediation plan

Every rule is a pure function of its input. There are no model calls, no network access, and no wall-clock or environment dependence anywhere in the scoring path — see plan.md §3 for the determinism contract and tests/test_determinism.py for its enforcement.

The scoring model, briefly

Each pillar splits into a presence score (does the relevant artifact exist at all?) and a quality score (how good is it?), combined as 0.4 × presence + 0.6 × quality. This makes the score resistant to gaming in both directions: deleting every skill scores worse than having one flawed skill, and duplicating a mediocre skill doesn't inflate the score (it's an average, not a sum). Rules that don't apply are removed from both numerator and denominator; a pillar with nothing applicable at all is excluded from the weighted overall rather than scoring a vacuous 100%.

Any error-severity finding caps the overall grade at C, regardless of the arithmetic score — and error severity is reserved for objective, spec-verifiable failures (a skill that silently fails to load, a committed credential), never for style heuristics. The cap never upgrades an already-worse grade.

Score	Grade	Meaning
90–100	A	Agent-native
80–89	B	Agent-ready
70–79	C	Agent-capable
55–69	D	Partially configured
35–54	E	Minimal
0–34	F	Not agent-ready

Every rule is tagged by platform, so the report also carries separate copilot and claude scores and their parity delta — a rich AGENTS.md with no CLAUDE.md bridge shows up as a Copilot/Claude gap, not just a buried warning.

Commands

airx analyze PATH [--format terminal|json|md|sarif] [-o FILE]
                  [--profile minimal|standard|enterprise]
                  [--platform copilot|claude|all]
                  [--min-score N] [--fail-on error|warning|never]
                  [--ignore PREFIX]... [--no-waivers] [--today YYYY-MM-DD]
airx rules        [--format terminal|json|md]     # the catalog; generates docs/RULES.md
airx compare      OLD.json NEW.json               # regression diff for CI
airx init         [--force]                       # scaffold .airx.yml

Not yet implemented

HTML report, the composite GitHub Action, airx fix, duplication detection, and nested-monorepo aggregation — see plan.md §12 and plan-v2-fable.md §1 for sequencing.

Contributing

See CONTRIBUTING.md — in particular, the section on the determinism contract, which every rule must preserve.

Security

See SECURITY.md for the threat model and how to report a vulnerability.

Credits

The SKILL.md validation rules and their thresholds are vendored from AgentEval (MIT licensed). The rule catalog is derived from the published Agent Skills specification, the Claude Code documentation, and GitHub Copilot's custom-instructions guidance — see plan.md §15 for the full bibliography.

License

MIT © 2026 Yoav Lax