# Ai Research Reproduction

> Rigor Reproduce compatible skill slug for README-first deep learning repository reproduction. Use when the user wants an end-to-end, minimal-trustworthy flow that reads the repository first, selects the smallest documented inference or evaluation target, coordinates intake, setup, trusted execution, optional trusted training, optional repository analysis, and optional paper-gap resolution, enforces conservative patch rules, records evidence assumptions deviations and human decision points, and writes the standardized `repro_outputs/` bundle. Do not use for paper summary, generic environment setup, isolated repo scanning, standalone command execution, silent protocol changes, score chasing, or broad research assistance outside repository-grounded reproduction.

Source: https://skillsagentes.com/skills/lllllllama/rigorpilot-skills/ai-research-reproduction
Repository: https://github.com/lllllllama/RigorPilot-Skills
Author: lllllllama
License: MIT
Updated: hace 19 días
Context cost: 192 tok installed, 1.7k tok once triggered, 20.6k tok with every bundled file
Bundle: 14 files, 80 KB
Permissions requested: none declared

## Install

```bash
npx -y skills add lllllllama/RigorPilot-Skills --skill ai-research-reproduction --agent claude-code
```

## What it does

- Coordina un flujo README-first para reproducir repositorios de deep learning: intake, setup, ejecución confiable, entrenamiento opcional, análisis opcional y resolución de brechas con el paper
- Selecciona el objetivo mínimo demostrable (inferencia, evaluación o arranque de entrenamiento documentados) en lugar de ejecutar todo
- Aplica reglas de parches conservadoras y exige registrar evidencia, supuestos, desviaciones y puntos de decisión humana
- Escribe el paquete estandarizado en `repro_outputs/` con SUMMARY.md, COMMANDS.md, LOG.md, SCIENTIFIC_CHANGELOG.md, COMPARABILITY_REPORT.md, status.json y ANNOTATED_README.md
- Pausa para revisión humana antes de afirmaciones de entrenamiento más completas o cambios que alteren dataset, split, checkpoint, métrica o semántica del modelo

## Use it when

- El objetivo es un repositorio de código de IA con README, scripts, configs o comandos documentados
- La solicitud abarca varias fases confiables: intake, setup, ejecución, verificación de entrenamiento, análisis, resolución de brechas con el paper y reporte
- El resultado deseado es un objetivo pequeño y reproducible, no experimentación amplia

## Don't bother when

- Resúmenes de papers
- Configuración genérica de entorno o escaneo aislado de un repo
- Ejecución de comandos independientes, diseño de investigación abierto o exploración solo de candidatos

## What triggers it

- "Reproduce el resultado de inferencia documentado en este repositorio siguiendo el README"
- "Ejecuta la evaluación mínima documentada de este repo de deep learning y registra la evidencia"
- "Necesito reproducir de forma confiable el arranque de entrenamiento de este proyecto y documentar las desviaciones"

## Before you install

- Requiere un repositorio con README, scripts o comandos documentados como base para la reproducción.

## Files

- SKILL.md — 6 KB
- agents/openai.yaml — 389 B
- assets/COMMANDS.template.md — 348 B
- assets/LOG.template.md — 669 B
- assets/PATCHES.template.md — 525 B
- assets/SUMMARY.template.md — 621 B
- assets/status.template.json — 1 KB
- references/architecture.md — 1 KB
- references/language-policy.md — 794 B
- references/output-spec.md — 3 KB
- references/patch-policy.md — 2 KB
- references/research-safety-principles.md — 2 KB
- scripts/annotate_readme.py — 19 KB
- scripts/orchestrate_repro.py — 43 KB

## SKILL.md

Reproduced verbatim from lllllllama/RigorPilot-Skills under MIT. This section is the upstream document and is in English.

# ai-research-reproduction

## Purpose

Use this as the Rigor Reproduce compatible skill slug for README-first deep
learning repository reproduction. The installed slug remains
`ai-research-reproduction` for compatibility. The skill guides the agent toward
a minimal trustworthy run with auditable evidence; it should not micromanage
implementation details that the model can infer from the repository.
Reproduction is not "make it run by changing anything"; it means faithfully
reading the README, environment, weights, datasets, and documented commands,
then recording results and deviations.

Start from the shared operating principles in
`../../references/agent-operating-principles.md`, then load
`../../references/research-rigor-principles.md` and
`../../references/deep-learning-experiment-principles.md` when scientific meaning, comparability, or experiment details are at stake.

## Fit

Use this skill when all are true:

- The target is an AI code repository with a README, scripts, configs, or
  documented commands.
- The request spans multiple trusted phases such as intake, setup, execution,
  training verification, analysis, paper-gap resolution, and reporting.
- The desired result is a small reproducible target, not broad experimentation.

Do not use this skill for paper summaries, generic environment setup, isolated
repo scanning, standalone command execution, open-ended research design, or
explicit candidate-only exploration.

## Trusted Target Selection

Choose the smallest target that can honestly demonstrate repository-grounded
reproduction:

1. documented inference
2. documented evaluation
3. documented training startup or partial verification
4. full training only after explicit user confirmation

Treat README guidance as the primary reproduction intent. Use repository files
to clarify the README, not to silently replace it. When the README and paper
conflict, record the conflict and use `paper-context-resolver` only for the
narrow reproduction-critical gap.

## Workflow

1. Read the README and nearby repo signals.
2. Use `repo-intake-and-plan` to extract documented commands and candidate
   targets.
3. Select and justify the minimum trustworthy target.
4. Use `env-and-assets-bootstrap` only for target-specific environment,
   checkpoint, dataset, and cache assumptions.
5. Use `analyze-project` only when structure, insertion points, or suspicious
   implementation patterns need read-only clarification.
6. Use `minimal-run-and-audit` for documented inference, evaluation, smoke, or
   sanity execution.
7. Use `run-train` instead when the selected trusted target is training startup,
   short-run verification, full kickoff, or resume.
8. Pause for human review before fuller training claims or any change that could
   alter dataset, split, checkpoint, preprocessing, metric, loss, model
   semantics, or result interpretation.
9. Write the standardized outputs and give a concise final note in the user's
   language when practical.

## Patch Boundary

Prefer no repository edits. If edits are needed, keep them conservative and
auditable:

- Try command-line arguments, environment variables, path fixes, dependency
  version fixes, or dependency-file fixes before code changes.
- Reproduction fixes are allowed when needed, but they must not be hidden. State
  what changed, why it was necessary, whether it changes scientific meaning,
  and whether it affects comparability with the paper, README, or baseline.
- Avoid changing model architecture, core inference semantics, training logic,
  loss functions, or experiment meaning.
- If repository files must change, create a branch named
  `repro/YYYY-MM-DD-short-task`, keep verified patch commits sparse, and record
  README-fidelity impact in `PATCHES.md`.

See `references/patch-policy.md`.

## Outputs

Always target `repro_outputs/`:

```text
SUMMARY.md
COMMANDS.md
LOG.md
SCIENTIFIC_CHANGELOG.md
COMPARABILITY_REPORT.md
status.json
ANNOTATED_README.md   # original README + colored per-section agent-action annotations
PATCHES.md   # only if patches were applied
```

Use the templates under `assets/` and the field rules in `references/output-spec.md`.

- Put the shortest high-value summary in `SUMMARY.md`.
- Put copyable commands in `COMMANDS.md`.
- Put process evidence, assumptions, failures, and decisions in `LOG.md`.
- Put scientific meaning and change effects in `SCIENTIFIC_CHANGELOG.md`.
- Put comparison anchors and protocol deviations in `COMPARABILITY_REPORT.md`.
- Put durable machine-readable state in `status.json`.
- Put branch, commit, validation, and README-fidelity impact in `PATCHES.md` when needed.
- Put the researcher's at-a-glance view in `ANNOTATED_README.md`: the README replayed verbatim, each section annotated in color with what the agent did there, linked to the evidence files above.
- Distinguish verified facts from inferred guesses.

## Reference Loading

- Load `references/language-policy.md` when writing human-readable outputs.
- Load `../../references/research-rigor-principles.md` before making comparability, contribution, or research-result claims.
- Load `../../references/deep-learning-experiment-principles.md` when dataset, split, metric, checkpoint, training, or evaluation details matter.
- Consult `~/.rigorpilot/PERSONAL_RIGOR.md` if present, under `../../references/continuous-learning-policy.md` (advisory only; core wins).
- Failed and later-resolved runs are auto-recorded as lessons via `shared/scripts/lessons_store.py` (`RIGORPILOT_LESSONS=0` disables).
- Load `references/research-safety-principles.md` before protocol-sensitive
  decisions.
- Load `references/patch-policy.md` before modifying repository files.
- Keep specialized logic in sub-skills, scripts, templates, or references rather
  than expanding this entrypoint.

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Skills Agentes — https://skillsagentes.com/skills/lllllllama/rigorpilot-skills/ai-research-reproduction