aif-analyze
Skill0 starsChanged 7 months agoMITView on GitHub
Deep code analysis using the code index. Finds missing tests, circular deps, god modules, and architectural issues.
In this repository22 other files it gives its agents.
- AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- .github/copilot-instructions.mdCopilot instructions
- aif-enhanceSkill
- aif-evolveSkill
- aif-feedbackSkill
- aif-ingestSkill
- aif-learnSkill
- aif-pulseSkill
- aiframework-learnSkill
- aiframework-reviewSkill
- aiframework-shipSkill
- aif-readySkill
- aif-researchSkill
- aif-reviewSkill
- aif-shipSkill
- aif-statusSkill
- retroSkill
- sheal-askSkill
- sheal-checkSkill
- sheal-driftSkill
- sheal-retroSkill
The file
We split this skill into the rules it gives an agent
2 rules. About 0 are common, 1 is distinctive, and 1 is very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈1
Distinctive · estimate
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 26
Use Glob/Grep to verify findings against actual code
- Very specific: line 27
Write structured findings to .aiframework/enhance-findings.json
≈ “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
Code Analysis
Telemetry (run first, silent):
aiframework-telemetry skill_invoked skill=aif-analyze 2>/dev/null || trueAnalyze the codebase using the pre-computed code index for architectural insights.
Process
- Read
.aiframework/code-index.json - Analyze modules for:
- Missing tests: source files with no corresponding test file
- Circular dependencies: modules that import each other
- God modules: high fan_in (>3) AND high total_symbols (>20)
- Orphan files: files with no imports in or out
- Hot spots: files with highest PageRank importance
- Use Glob/Grep to verify findings against actual code
- Write structured findings to
.aiframework/enhance-findings.json
Output Format
{
"findings": [
{"type": "missing-test", "file": "src/utils.py", "suggestion": "Add tests/test_utils.py"},
{"type": "circular-dep", "modules": ["lib/a", "lib/b"], "severity": "medium"},
{"type": "god-module", "module": "lib/core", "fan_in": 8, "symbols": 45}
]
}Does it work?
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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.

