RigorPilot Skills — Setup Guide¶
Source: lllllllama/rigorpilot-skills (2.6M combined installs) GitHub: github.com/lllllllama/rigorpilot-skills Category: Research / ML Engineering First Seen: August 12, 2026 Quality Tier: 🟡 Beta (high install volume, young publisher)
RigorPilot packages the full AI-research loop as twelve agent skills: explore a research area, resolve paper context, reproduce experiments, run training, and audit the result — all with safety-first debugging. Every skill carries ~234K installs, making this one of the highest-volume research clusters on skills.sh.
Installation¶
# Install the full repo
npx skills add llllllllama/rigorpilot-skills
# Or install individually
npx skills add llllllllama/rigorpilot-skills --skill ai-research-explore
npx skills add llllllllama/rigorpilot-skills --skill paper-context-resolver
npx skills add llllllllama/rigorpilot-skills --skill safe-debug
Core Skills¶
| Skill | Installs | Use For |
|---|---|---|
ai-research-explore |
235.2K | Systematic exploration of a research area |
analyze-project |
234.8K | Project structure and goals analysis |
ai-research-reproduction |
234.6K | Reproducing published AI research |
explore-code |
234.5K | Codebase exploration for research repos |
paper-context-resolver |
234.5K | Resolving paper references and context |
safe-debug |
234.5K | Debugging without breaking experiments |
repo-intake-and-plan |
234.5K | Intake a repo and produce an execution plan |
minimal-run-and-audit |
234.4K | Minimal run + result audit loop |
run-train |
234.4K | Training run execution |
env-and-assets-bootstrap |
234.4K | Environment and dataset/asset setup |
explore-run |
234.4K | Exploratory run management |
ai-paper-reproduction |
0 | End-to-end paper reproduction (newest) |
Prerequisites¶
| Requirement | Details |
|---|---|
| Node.js + npx | For the skills.sh CLI install path |
| Python ML stack | PyTorch/HF ecosystem for the training-oriented skills |
| GPU access | For run-train and reproduction workloads (local or cloud) |
| Git | Repos under study are cloned locally |
CorpusIQ Use Cases¶
| Use Case | How |
|---|---|
| Model evaluation research | ai-research-explore + paper-context-resolver for surveying techniques before evals |
| Agent skill upgrades | repo-intake-and-plan + explore-code when assessing new tooling repos for the stack |
| Safe experimentation | minimal-run-and-audit + safe-debug pattern for local AI infrastructure work |
Limitations / Verification¶
- Newest skill (
ai-paper-reproduction) has zero installs — treat as alpha - Verify install:
npx skills list | grep -E "research|rigor"shows installed entries - Training skills assume a working GPU environment; they orchestrate, they don't provision hardware
Related¶
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