zernie/vigiles

audit-feedback-loop

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

First seen Apr 2, 2026

Installation

$ npx skills add zernie/vigiles --skill audit-feedback-loop

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Repository health

Stars 15
License LICENSE
Default branch main
Open issues 17
Status Active

Skill metadata

Parsed from SKILL.md frontmatter.

Declared agents claude-code

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 3,623 B
  • docs SUMMARY.md 110 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 360 installs

SKILL.md

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

  1. 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

  1. CLAUDE.md: Check if CLAUDE.md exists at the repo root
  2. 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.
  3. Type-Safe Specs: Check for CLAUDE.md.spec.ts or *.spec.ts files — indicates typed spec compilation via vigiles v2
  4. Generated Types: Check for .vigiles/generated.d.ts — indicates linter rules are type-checked at authoring time
  5. Screenshot/Visual Tests: Look for Playwright (playwright.config.), Cypress (cypress.config.), Chromatic, Percy, BackstopJS configs
  6. Observability: Search for imports/usage of @sentry/, dd-trace, @datadog/, newrelic, @opentelemetry/, sentry_sdk, structlog, tracing (Rust), opentelemetry in source files
  7. 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.