repo-intake-and-plan
작성자: lllllllama
Rigor Intake helper for README-first deep learning repo reproduction. Use when the task is specifically to scan a repository, read the README and common project files, extract documented commands, classify inference, evaluation, and training candidates, and return the smallest trustworthy reproduction plan to the main orchestrator. Do not use for environment setup, asset download, command execution, final reporting, paper lookup, or end-to-end orchestration.
npx skills add https://github.com/lllllllama/rigorpilot-skills --skill repo-intake-and-planrepo-intake-and-plan
Use this as the Rigor Intake helper. The installed slug remains
repo-intake-and-plan for compatibility.
When to apply
- At the beginning of README-first reproduction work.
- When the main skill needs a fast map of repo structure and documented commands.
- When inference, evaluation, and training candidates must be classified conservatively.
- When the user explicitly wants to inspect the repo first and not run anything yet.
When not to apply
- When execution has already started and the task is now about running commands or writing outputs.
- When the target is not a repository-backed reproduction task.
- When the user only wants paper interpretation without repo inspection.
- When the user already has a selected documented command and only needs setup or execution.
Clear boundaries
- This skill scans and plans.
- This skill is helper-tier and should usually be orchestrator-invoked.
- It does not install environments.
- It does not prepare large assets.
- It does not execute substantive reproduction commands.
- It does not decide high-risk patching.
Input expectations
- Target repository path.
- Access to README and common project files if present.
- Optional user hints about desired priority, such as inference-first.
Output expectations
- concise repo structure summary
- documented command inventory
- inferred candidate categories: inference, evaluation, training, other
- minimum trustworthy reproduction recommendation
- notable ambiguity or risk list
Notes
Use references/repo-scan-rules.md and helper scripts under scripts/.
lllllllama의 다른 스킬
ai-research-explore
lllllllama
Rigor Explore compatible skill slug for meaningful and potentially novel deep learning research candidates. Use when the researcher has chosen the task family, dataset, benchmark, evaluation method, provided SOTA references, and wants candidate-only exploration on top of `current_research` with auditable repo understanding, idea gating, fair comparison, and governed experiments written to `explore_outputs/`. Do not use for README-first trusted reproduction, open-ended direction finding,...
researchdata-analysisapi
analyze-project
lllllllama
딥러닝 연구 저장소를 위한 Rigor Analyze / Rigor Audit 읽기 전용 스킬입니다. 사용자가 저장소를 읽고 이해하거나, 모델 구조 및 학습/추론 진입점을 검사하거나, 설정 및 삽입 지점을 검토하거나, 코드 수정이나 무거운 작업 실행 없이 의심스러운 구현 패턴을 식별하려 할 때 사용하세요. 활성 명령 실행, 광범위한 리팩토링, 추측성 코드 적용, 자동 버그 수정에는 사용하지 마십시오.
developmentcode-reviewresearch
ai-research-reproduction
lllllllama
RigorPilot 재현 모드 오케스트레이터로, README 우선 딥러닝 저장소 재현을 위한 도구입니다. 사용자가 엔드투엔드 최소 신뢰성 흐름을 원할 때 사용하며, 저장소를 먼저 읽고 가장 작은 문서화된 추론 또는 평가 대상을 선택한 후, 인테이크, 설정, 신뢰 실행, 선택적 신뢰 훈련, 선택적 저장소 분석, 선택적 논문 격차 해소를 조정하고, 보수적 패치 규칙을 적용하며, 증거 가정 편차 및 인간 결정 지점을 기록합니다.
researchdevelopmentdocument
explore-code
lllllllama
Rigor Improve implementation leaf skill for auditable candidate implementation in deep learning research repositories. Use when the researcher explicitly authorizes exploratory work on an isolated branch or worktree to transplant modules, adapt a backbone, add LoRA or adapter layers, replace a head, or stitch together meaningful low-risk migration ideas with rollback-aware records in `explore_outputs/`. Do not use for end-to-end exploration orchestration on top of `current_research`, trusted...
developmentresearchcode-review
minimal-run-and-audit
lllllllama
Rigor Run skill for README-first deep learning repo reproduction. Use when the task is specifically to capture or normalize evidence from the selected smoke test or documented inference or evaluation command and write standardized `repro_outputs/` files, including patch notes when repository files changed. Do not use for training execution, initial repo intake, generic environment setup, paper lookup, target selection, hidden scientific-meaning changes, or end-to-end orchestration by itself.
developmenttestingcode-review
env-and-assets-bootstrap
lllllllama
README 우선 딥러닝 리포지토리 재현을 위한 Rigor Setup 스킬입니다. README에 문서화된 리포지토리에서 실행 전에 보수적인 conda 우선 환경, 체크포인트 및 데이터셋 경로 가정, 캐시 위치 힌트, 설정 노트를 준비하는 작업에 사용하세요. 리포지토리 스캔, 전체 오케스트레이션, 논문 해석, 최종 실행 보고, 또는 특정 재현 대상과 연결되지 않은 일반 환경 설정에는 사용하지 마십시오.
developmentdevops
explore-run
lllllllama
Rigor Improve / Rigor Explore run leaf skill for bounded exploratory evidence in deep learning research repositories. Use when the researcher explicitly authorizes exploratory runs such as small-subset validation, short-cycle guess-and-check, batch sweeps, idle-GPU search, or quick transfer-learning trials, with fair-comparison caveats and no-overclaim summaries in `explore_outputs/`. Do not use for end-to-end exploration orchestration on top of `current_research`, trusted baseline...
researchdevelopmentdata-analysis
safe-debug
lllllllama
딥러닝 연구 작업을 위한 엄격한 디버그/엄격한 감사 스킬입니다. 사용자가 트레이스백, 터미널 오류, CUDA OOM, 체크포인트 로드 실패, 형태 불일치, NaN 손실 증상 또는 훈련 실패를 붙여넣고 패치 전에 보수적인 진단을 원하며 디버그 수정이 연구 기여와 명확히 분리되어야 할 때 사용하세요. 광범위한 리팩토링, 추측성 적응, 자동 탐색적 패치 또는 일반적인 저장소 숙지에는 사용하지 마십시오.
developmenttestingcode-review