analyze-project
Skill495 starsChanged 6 months agoMITView on GitHub
Rigor Analyze / Rigor Audit read-only skill for deep learning research repositories. Use when the user wants to read and understand a repository, inspect model structure and training or inference entrypoints, review configs and insertion points, or flag suspicious implementation patterns without modifying code or running heavy jobs. Do not use for active command execution, broad refactoring, speculative code adaptation, or automatic bug fixing.
6 other projects use a version of this.
Adopted by
Most recently taken up by
- dmlguq456/heartingtheir own version, first seen Nov 8, 2026
- CsHeng/agent-skillstheir own version, first seen Nov 4, 2026
- jrjsmrtn/project-orchestration-skillstheir own version, first seen Nov 3, 2026
- lgtm-hq/ai-skillstheir own version, first seen Oct 13, 2026
- hidekitux/skillstheir own version, first seen Sep 8, 2026
- Minitour/agentic-ontology-development-kittheir own version, first seen Jul 24, 2026
Other versions6 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 6
- jrjsmrtn/project-orchestration-skills★ 17same name
- dmlguq456/hearting★ 9same name
- CsHeng/agent-skills★ 3same name
- Minitour/agentic-ontology-development-kit★ 2same name
- lgtm-hq/ai-skills★ 1same name
- hidekitux/skills★ 0same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository11 other files it gives its agents.
- AGENTS.mdAGENTS.md
- ai-research-exploreSkill
- ai-research-reproductionSkill
- env-and-assets-bootstrapSkill
- explore-codeSkill
- explore-runSkill
- minimal-run-and-auditSkill
- paper-context-resolverSkill
- repo-intake-and-planSkill
- run-trainSkill
- safe-debugSkill
The file
We split this skill into the rules it gives an agent
7 rules. 0 are common, 4 are distinctive, and 3 are very specific to this project.
0
Common
Most of the 6 edited versions we read keep these.
4
Distinctive
This author's own choices. Worth a look.
3
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 8
Use this as the Rigor Analyze / Rigor Audit read-only skill.
- Very specific: line 11
Use the shared operating principles in ../ai-research-reproduction/references/agent-operating-principles.md.
- Distinctive: line 24
When the main task is to execute a failing command or debug a traceback.
- Distinctive: line 25
When the user wants environment setup or asset download only.
- Distinctive: line 26
When the user wants speculative adaptation or broad exploratory patching.
- Distinctive: line 27
When the task is a general literature summary without repository analysis.
- Very specific: line 45
Use references/analysis-policy.md and the shared ../ai-research-reproduction/references/research-pitfall-checklist.md.
“Common” is measured: kept by at least 6 in 10 of the 6 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
analyze-project
Use this as the Rigor Analyze / Rigor Audit read-only skill. The installed slug
remains analyze-project for compatibility.
Use the shared operating principles in
../ai-research-reproduction/references/agent-operating-principles.md; this skill should guide
read-only analysis without constraining the model's project-specific reasoning.
When to apply
- The user wants to understand a deep learning repository before changing it.
- The user needs a map of model structure, training entrypoints, inference entrypoints, and config relationships.
- The user wants conservative suggestions about likely insertion points or suspicious implementation patterns.
- The user explicitly wants read-only analysis and not heavy execution.
When not to apply
- When the main task is to execute a failing command or debug a traceback.
- When the user wants environment setup or asset download only.
- When the user wants speculative adaptation or broad exploratory patching.
- When the task is a general literature summary without repository analysis.
Clear boundaries
- This skill is read-mostly.
- It may run lightweight static inspection helpers.
- It does not patch repository code.
- It does not own final reproduction outputs.
- It should mark suspicious patterns as heuristics, not confirmed bugs.
Output expectations
analysis_outputs/SUMMARY.mdanalysis_outputs/RISKS.mdanalysis_outputs/status.json
Notes
Use references/analysis-policy.md and the shared ../ai-research-reproduction/references/research-pitfall-checklist.md.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
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- It worked
- Partly
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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.