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audit-feedback-loop

audit-feedback-loop

github.com/zernieTesting & quality15435 installsUpdated 2d ago
Repository

About audit-feedback-loop

A repository-audit skill for teams using AI-assisted development. It scores feedback-loop maturity from repository signals and produces a report with findings and tailored recommendations.

Key features

  1. 01Scores feedback-loop maturity across four levels
  2. 02Checks CI and language-specific linters
  3. 03Inspects custom rules and CLAUDE.md enforcement
  4. 04Checks visual tests, observability, and scheduled tasks
  5. 05Reports findings with paths and recommendations

Use cases

  1. 01Assess how much feedback an AI coding agent receives
  2. 02Find gaps in repository checks and specifications
  3. 03Plan improvements to development feedback loops

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/zernie/vigiles --skill audit-feedback-loop

After installing, type/audit-feedback-loopin Claude Code to confirm it loaded.

Details

From the project README

Scan the current repository and score its feedback loop maturity for AI-assisted development.

Instructions

Analyze this repository and score its feedback loop maturity using the levels below. Check for each signal, then output a summary report.

Maturity Levels

Level 0 — Vibes No CI config, no linter rules, no CLAUDE.md. The AI agent is flying blind.

Level 1 — Guardrails Has CI + standard linters, but no custom rules. The agent gets basic feedback but can't learn project-specific conventions.

Level 2 — Architecture as Code Has custom lint rules, CLAUDE.md rules have enforcement annotations. The agent gets rich, project-specific feedback.

Level 3 — The Organism Has CI + custom rules + screenshot/visual tests + observability + scheduled agent tasks. The entire development loop is instrumented.

Signals to Check

Scan the repository for the following and note which exist:

  1. CI Configuration: Look for .github/workflows/, .circleci/, Jenkinsfile, .gitlab-ci.yml, bitbucket-pipelines.yml, .travis.yml, etc.
  2. Linter Config (language-aware):
    • JS/TS: eslint.config.*, .eslintrc*, biome.json, .prettierrc*, deno.json
    • Python: pyproject.toml (look for [tool.ruff], [tool.pylint], [tool.flake8]), setup.cfg, .flake8, ruff.toml
    • Rust: clippy.toml, .clippy.toml, rustfmt.toml
    • Go: .golangci.yml, .golangci.yaml
    • Ruby: .rubocop.yml
    • Java/Kotlin: checkstyle.xml, pmd.xml, detekt.yml
  3. Custom Lint Rules: Look for custom plugins, rule directories, or inline rule definitions in linter configs
    • JS/TS: eslint-plugin-*, eslint-rules/ directories
    • Python: custom Ruff/Pylint plugins, AST-based checks
    • Rust: custom Clippy lints
    • Go: custom analyzers
  4. CLAUDE.md: Check if CLAUDE.md exists at the repo root
  5. CLAUDE.md Enforcement: Check if using vigiles v2 specs (CLAUDE.md.spec.ts exists) or v1 annotations (**Enforced by:** in CLAUDE.md). v2 specs = higher maturity.
  6. Type-Safe Specs: Check for CLAUDE.md.spec.ts or *.spec.ts files — indicates typed spec compilation via vigiles v2
  7. Generated Types: Check for .vigiles/generated.d.ts — indicates linter rules are type-checked at authoring time
  8. Screenshot/Visual Tests: Look for Playwright (playwright.config.*), Cypress (cypress.config.*), Chromatic, Percy, BackstopJS configs
  9. Observability: Search for imports/usage of @sentry/, dd-trace, @datadog/, newrelic, @opentelemetry/, sentry_sdk, structlog, tracing (Rust), opentelemetry in source files
  10. Scheduled Agent Tasks: Look for cron patterns in CI configs, .github/workflows/ with schedule: triggers, or references to scheduled Claude Code tasks
Output Format
## Feedback Loop Audit

**Repository:** <repo name>
**Primary language(s):** <detected languages>
**Score: Level X — <Name>**

### Signals Found
- [x] CI Configuration: <details>
- [ ] Custom Lint Rules: not found
- [x] CLAUDE.md: found, 5 enforced / 2 guidance / 1 missing
...

### Recommendations
1. <Most impactful next step to level up>
2. <Second recommendation>
3. <Third recommendation>

### How to Level Up
<Specific, actionable advice for reaching the next maturity level>

Be specific about file paths and what you found. Give actionable recommendations tailored to the project's language and toolchain.

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