RigorPilot Skills

Run research repositories from their README, with bounded execution and auditable evidence. RigorPilot adds section-level results without rewriting the original README. Trusted reproduction is the default; candidate exploration requires explicit authorization.

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Real examples · Install & use · Skill index · Validation · Engineering roadmap

📄 Real repositories, inspectable results

Original commands, prose, badges, images, videos and HTML stay in the source file. RigorPilot splits that file into sections and inserts one evidence-linked card per section. Removing its insertion blocks restores the retained original README byte for byte.

Each card below opens a full annotated README beside the original README in a retained repository checkout. Supporting repository files are kept so relative links and media retain their original context.

🟢 selected checks passed · 🔵 not executed · ⚪ read only · 🟡 partial · 🔴 blocked · 🟣 decision needed. Green does not automatically mean paper-result reproduction; blue is not an execution failure.

micrograd · correctness checks 🟢 2 tests passed in 7.62 s 8 headings = 8 annotations · original bytes preserved Open full RigorPilot README →	minGPT · selection boundary 🔵 Test selected, not executed · no model download 11 headings = 11 annotations · original bytes preserved Open full RigorPilot README →
PyTorch MNIST · bounded startup 🟡 Partial training · observed loss 0.038893 1 heading = 1 annotation · original bytes preserved Open full RigorPilot README →	nanoGPT Shakespeare · bounded training 🟡 Partial · train loss 4.1676 · validation loss 4.1649 11 headings = 11 annotations · original bytes preserved Open full RigorPilot README →

All four cases and upstream links · Recorded suite · Case definitions · Methodology

These are historical, commit-pinned deterministic runs: 4/4 case protocols passed in 251.0 s, with a peak workspace of 98.67 MiB and 0 model API calls. The zero-API count applies only to that suite. Selection-only and partial cases are not completed evaluations, converged training or reproduced paper scores.

New: installed-skill micrograd trial, with before/after command reports, a retained failed attempt and independent checks—not a model-quality comparison.

🚀 Install and use

The installer needs Node.js/npm; check its Node version requirement if it reports EBADENGINE.

Install all skills:

npx skills add lllllllama/rigorpilot-skills --all

Or install only the self-contained reproduction skill:

npx skills add lllllllama/rigorpilot-skills --skill ai-research-reproduction

Open the target repository in a Skills-capable agent, then ask:

Use ai-research-reproduction: run the smallest README-documented evaluation, preserve the source and write evidence to repro_outputs/, plus an annotated copy beside the original README. Ask before large downloads or long training.

The main skill works alone; choose all skills for companion and leaf entrypoints. Your existing agent loads the skill. The standalone model runner is optional. Client compatibility

Start with the RIGORPILOT_README.md reported in source_adjacent_readme.path, then follow its command and log links. If a conflicting file blocks the extra copy, that file stays intact; inspect repro_outputs/SUMMARY.md for the outcome and next action.

What it does—and does not do

README → documented target → reviewed setup → bounded execution → verification → evidence.

• Preserves source meaning; records assumptions, deviations, failures and blockers.
• Records process state, logs and attempt lineage; supports explicit cancellation, recovery and retry through the persistent runtime.
• Separates trusted reproduction from explicitly authorized, candidate-only exploration.
• Checks execution criteria independently of the model's completion claim.

This is local execution, not an OS sandbox. Approved commands can access the host and network; use trusted repositories. Resource admission and between-action budget checks are not hard OS quotas or subscription-balance monitoring.

The optional model loop currently supports Anthropic Messages and reviewed command IDs, not unrestricted source repair. This standalone runner has no successful live-model acceptance recorded yet: three provider attempts returned HTTP 502. Other model profiles are metadata, not proof of working transports or equivalent model performance. Runner and recovery contract · Implementation evidence and limits

🎯 Skill index

Task	Skill
Reproduce from README commands	ai-research-reproduction
Read-only repository analysis	analyze-project
Prepare environment, data and weights	env-and-assets-bootstrap
Run documented inference or evaluation	minimal-run-and-audit
Start or verify training conservatively	run-train
Diagnose before proposing a patch	safe-debug
Coordinate authorized candidate exploration	ai-research-explore
Implement a candidate change on an isolated branch	explore-code
Execute a bounded candidate experiment	explore-run

Two helpers support orchestration: repo-intake-and-plan and paper-context-resolver. Exploration requires a durable current_research anchor and a frozen comparison contract. Candidate results never become trusted baseline results by declaration. Routing · Research loop · Campaign inputs

📦 Evidence bundle

Artifact	What to inspect
repro_outputs/ANNOTATED_README.md	Original README with inserted section verdicts
SUMMARY.md, COMMANDS.md, LOG.md, status.json	Outcome, exact commands, observations and machine-readable status
PATCHES.md, SCIENTIFIC_CHANGELOG.md, COMPARABILITY_REPORT.md	Changes, scientific meaning and comparison boundaries
_runtime/<run_id>/	Process state, events, resource samples and stdout/stderr
agent_state.json, trajectory.jsonl	Optional model runner's checkpoints, tool calls and reported usage

🟢 success · 🔵 not executed · ⚪ read only · 🟡 partial · 🔴 blocked · 🟣 decision required

Standard evidence stays under repro_outputs/. Both main runners accept --source-adjacent-readme to also write RIGORPILOT_README.md beside the original, preserving the context of its relative media/file links. Only inserted evidence links are rebased. The same output directory may refresh its unchanged owned copy, never an unrelated or manually edited file. Retain supporting repository files and the evidence directory's readme_delivery.json. Output contract · Rigor principles

✅ Offline validation

From a clone of this project, with Python 3.11+ and Git:

python scripts/run_harness_lab.py

This offline example uses scripted decisions and actual processes. It exercises failure → preparation → pause → controller restart → independent verification, without API calls, GPU use or model downloads. Inspect the printed REPORT.json path and its linked artifacts. Existing output is never overwritten; use --output tmp/check-2 to repeat. It is not evidence of live-model capability. Example source and checks

Run the repository regression suite:

python scripts/run_all_tests.py

Latest local record (2026-09-07): 69/69 scripts passed in 156.0 s. The CI badge links to the current Windows, Linux and macOS results. Local tests do not substitute for live-model or held-out evaluation.

For model comparisons, the small paired-evaluation kit provides frozen tasks and actual grader-calibration logs. The six planned model trials remain unrun; calibration is not evidence of skill uplift.

Controlled-trial checks add real failure/recovery logs, restricted tools and unknown-usage stops, with scripted model responses. The guide also provides a bounded A/B command-line entrypoint for model transport → reviewed commands → independent grading → sealed summary. Local HTTP integration is tested; real-provider effectiveness is not yet measured.

Engineering and contributions

Engineering roadmap · Contributing · Security and reporting · CI workflow · Reproduction feedback · MIT license

Keep acceptance checks independent, retain failed evidence and review traces before publication. Do not publish credentials or unreviewed private repository data. Agent guidance · Operating principles · Personalization policy

Historical interface illustration—not execution evidence

First attempt · After setup. This older MiniSeg preview illustrates error, metric and authorization displays. Its execution provenance is not independently verified; it is excluded from benchmarks.