When you run a deep learning command, you need proof that it worked. This Rigor Run skill helps you capture that evidence. It takes a smoke test or inference command and writes standardized output files so anyone can verify the results.
The skill is for when you already know which command to run. It does not choose the goal or set up the environment. It only handles the execution and reporting. It creates a SCIENTIFIC_CHANGELOG.md and a COMPARABILITY_REPORT.md to track any changes in scientific meaning.
If repository files are changed, it also writes a PATCHES.md file. The skill keeps everything auditable and clear. It is perfect for reproducing deep learning results from a research paper or a cookbook.
Global
mkdir -p ~/.claude/skills/minimal-run-and-auditProject
mkdir -p .claude/skills/minimal-run-and-auditSource 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
Paper Context Resolverlllllllama/ai-paper-reproduction-skill
Resolve deep learning paper reproduction gaps with precise primary source evidence
Repo Intake And Planlllllllama/ai-paper-reproduction-skill
Scan a repo, extract commands, and get a minimal reproduction plan
Minimal Run And Auditlllllllama/ai-paper-reproduction-skill
Run commands and capture normalized evidence for reproducible deep learning research
Firecrawl Searchfirecrawl/cli
Search the web and extract full page content as markdown for deeper research
Ai Research Reproductionlllllllama/rigorpilot-skills
Faithful reproduction of deep learning research with auditable steps and minimal changes
Analyze Projectlllllllama/rigorpilot-skills
Safe read-only analysis of deep learning repos for understanding and risk detection