audit-feedback-loop
Skill15 starsChanged 5 months agoMITView on GitHub
Scan the current repo and score its feedback loop maturity for AI-assisted development
In this repository24 other files it gives its agents.
- CLAUDE.mdCLAUDE.md
- examples/CLAUDE.mdCLAUDE.md
- src/CLAUDE.mdCLAUDE.md
- add-a-linterSkill
- audience-checkSkill
- code-qualitySkill
- deep-researchSkill
- dogfood-cliSkill
- enforce-rules-formatSkill
- generate-logoSkill
- hexagonal-architectureSkill
- landing-siteSkill
- pr-to-lint-ruleSkill
- review-docsSkill
- screenshotSkill
- ship-a-featureSkill
- verify-docs-findableSkill
- pr-to-lint-ruleSkill
- adopt-specSkill
- debug-my-harnessSkill
- edit-specSkill
- linter-docsSkill
- strengthenSkill
- test-harnessSkill
The file
We split this skill into the rules it gives an agent
10 rules. About 0 are common, 3 are distinctive, and 7 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈3
Distinctive · estimate
This author's own choices. Worth a look.
7
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 16
No CI config, no linter rules, no CLAUDE.md.
- Very specific: line 31
CI Configuration: Look for .github/workflows/, .circleci/, Jenkinsfile, .gitlab-ci.yml, bitbucket-pipelines.yml, .travis.yml, etc.
- Distinctive: line 39
Custom Lint Rules: Look for custom plugins, rule directories, or inline rule definitions in linter configs
- Distinctive: line 44
CLAUDE.md: Check if CLAUDE.md exists at the repo root
- Very specific: line 45
CLAUDE.md Enforcement: Check if using vigiles v2 specs (CLAUDE.md.spec.ts exists) or v1 annotations (Enforced by: in CLAUDE.md).
- Very specific: line 46
Type-Safe Specs: Check for CLAUDE.md.spec.ts or *.spec.ts files — indicates typed spec compilation via vigiles v2
- Very specific: line 47
Generated Types: Check for .vigiles/generated.d.ts — indicates linter rules are type-checked at authoring time
- Very specific: line 48
Screenshot/Visual Tests: Look for Playwright (playwright.config.*), Cypress (cypress.config.*), Chromatic, Percy, BackstopJS configs
- Very specific: line 49
Observability: Search for imports/usage of @sentry/, dd-trace, @datadog/, newrelic, @opentelemetry/, sentry_sdk, structlog, tracing (Rust), …
- Very specific: line 50
Scheduled Agent Tasks: Look for cron patterns in CI configs, .github/workflows/ with schedule: triggers, or references to scheduled Claude …
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
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:
- CI Configuration: Look for
.github/workflows/,.circleci/,Jenkinsfile,.gitlab-ci.yml,bitbucket-pipelines.yml,.travis.yml, etc. - 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
- JS/TS:
- 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
- JS/TS:
- CLAUDE.md: Check if
CLAUDE.mdexists at the repo root - CLAUDE.md Enforcement: Check if using vigiles v2 specs (
CLAUDE.md.spec.tsexists) or v1 annotations (**Enforced by:**in CLAUDE.md). v2 specs = higher maturity. - Type-Safe Specs: Check for
CLAUDE.md.spec.tsor*.spec.tsfiles — indicates typed spec compilation via vigiles v2 - Generated Types: Check for
.vigiles/generated.d.ts— indicates linter rules are type-checked at authoring time - Screenshot/Visual Tests: Look for Playwright (
playwright.config.*), Cypress (cypress.config.*), Chromatic, Percy, BackstopJS configs - Observability: Search for imports/usage of
@sentry/,dd-trace,@datadog/,newrelic,@opentelemetry/,sentry_sdk,structlog,tracing(Rust),opentelemetryin source files - Scheduled Agent Tasks: Look for cron patterns in CI configs,
.github/workflows/withschedule: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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People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
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Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

