analyze-project
🎯SkillA skill that reads a deep learning research project and explains how it is built, without changing any code. It also points out code that looks suspicious so you can check it.
Installation
npx skills add lllllllama/rigorpilot-skills --skill analyze-projectRun it in a terminal; it asks which AI coding tools to install it for (Claude Code, Codex, Cursor and more).
Uses skills.sh · Node.js 22.20+
After installing
Ask for related work and the agent uses it on its own, or call it yourself.
/analyze-projectInstall by hand
Copy the whole skill folder from GitHub to the place below. Copying only SKILL.md can leave out files the skill uses.
Open the skill folder →- This project
.claude/skills/analyze-project/ - All projects
~/.claude/skills/analyze-project/
These paths are for Claude Code. For other tools, the command above puts it in the right place.
What this skill does
Overview
analyze-project is the read-only Rigor Analyze / Rigor Audit skill. It reads a deep learning repository (a project's code folder) and maps the model, the scripts that start training or inference (running a trained model), and the config files. It may run light code checks, but it never edits code or runs heavy jobs.
Key Features
- See how the model is structured and where training and inference start.
- Understand how config files relate to each other and where new code could fit.
- Get a list of code that looks risky, marked as a guess and not a confirmed bug.
- Read the results in
analysis_outputs/SUMMARY.md,analysis_outputs/RISKS.md, andanalysis_outputs/status.json.
Who is this for?
Use it when you get an unfamiliar research codebase and want to understand it before you change it. It is not for running failing commands, setting up environments, or fixing bugs automatically.
Summarized by AI from this skill's SKILL.md. Suggest a better one
SKILL.md
View 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.
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.
More from this repository10
A helper skill for rerunning a deep learning project from its README. When the README misses a key detail, it finds the answer in the original paper and notes where the two disagree.
A skill that reads a deep learning project's README and main files before you run anything and lists the commands they describe. It then suggests the smallest run you can trust to check the results.
A skill that plans the software setup and file locations you need before re-running a deep learning research project. It suggests install commands and where to put model and data files.
A skill that runs one short check command when you re-run a deep learning research project and saves the results as standard report files. It also records any edits made to the project's files.
This skill helps deep learning researchers test new ideas on top of an existing project, with fair comparisons against a fixed benchmark. It marks each result as a candidate to check and saves the reports in `explore_outputs/`.
A skill that helps your agent rerun a deep learning research project and check whether it works as its README describes. It starts with the smallest test and records every step and change.
A skill that finds the cause of errors in deep learning research code, such as running out of GPU memory, before changing anything. It suggests the smallest fix and waits for your approval.
A skill that helps you try small, low-risk code changes in a deep learning research project on a separate branch. It records why each change matters and how to undo it.
A skill that carefully runs a training command you already chose for a deep learning project. It saves the command, settings, logs, and results in one standard folder.
A skill that plans quick, small trial runs for deep learning research, such as testing on part of the data or trying many settings. It reports the results without claiming more than they show.