Reproducing an AI research repository is a careful process. This workflow guides you step by step. It starts by reading the README and picking the smallest trusted target like inference or evaluation. Then you set up the environment and run the code. Every step records evidence and any deviations from the original instructions.
The goal is to get reliable results with minimal changes to the code. Any edits are conservative and clearly noted. At the end you get a standardized output bundle with all evidence and notes. This makes verification easy.
This approach is best for verifying research results. It is not meant for broad experimentation or changing the code freely. You always stay true to the repository's documented commands.
Global
mkdir -p ~/.claude/skills/ai-research-reproductionProject
mkdir -p .claude/skills/ai-research-reproductionSource Repository
Lark Baselarksuite/cli
Simplify your Lark Base data with tables, fields, records, and views
Lark Wikilarksuite/cli
Manage your Lark Wiki spaces, members, and documents with simple commands
Grillingmattpocock/skills
Test your plan thoroughly with clear step-by-step interview questions
Analyze Projectlllllllama/rigorpilot-skills
Safe read-only analysis of deep learning repos for understanding and risk detection
Repo Intake And Planlllllllama/rigorpilot-skills
Scan a repo, find commands, and get a simple reproduction plan
Minimal Run And Auditlllllllama/rigorpilot-skills
Capture evidence from deep learning runs and create standardized audit reports
Obsidian Vaultmattpocock/skills
Search create and organize notes with wikilinks and index notes easily
Paper Context Resolverlllllllama/ai-paper-reproduction-skill
Resolve deep learning paper reproduction gaps with precise primary source evidence