Ai Research Reproduction
You point this at a deep learning repo and it tries to reproduce the smallest documented target without changing anything important. It reads the README first, picks inference or eval over training when possible, records every assumption and deviation in a structured output bundle, and pauses before doing anything that might alter scientific meaning. The workflow is opinionated: bootstrap only what you need, patch conservatively, write evidence to repro_outputs/, and treat the README as ground truth even when the paper says something different. If you want to chase SOTA scores or experiment freely, use something else. This is for auditable, minimal-trust reproduction runs where you need to know exactly what changed and why.
- install copies · 90 days. Times the install command was copied on this site in the last 90 days, bots filtered.
- 6
- skills.sh installs
- 353.4K
- GitHub stars
- 486
- last updated
- Aug 21, 2026
Install
$ npx -y skills add lllllllama/rigorpilot-skills --skill ai-research-reproduction --agent claude-codeInstalls into .claude/skills of the current project.
Files
ai-research-reproduction
Purpose
Guide README-first deep learning reproduction toward the smallest trustworthy run with auditable evidence. Preserve documented meaning; record assumptions, deviations and blockers instead of changing semantics to manufacture success. Load specialized references only for a concrete uncertainty.
Fast Path
For a routine bounded run, keep the control path short:
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- Repository
- github.com/lllllllama/rigorpilot-skills
- Categories
- Documentation, AI & Agent Building, Data Science & ML, Productivity & Planning
- First seen
- Jul 14, 2026
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