lllllllama/rigorpilot-skills

repo-intake-and-plan

Rigor Intake helper for README-first deep learning repo reproduction.

소스 보기

사용 방법

  1. 설치 명령을 복사하고 자신의 에이전트 환경에 설치하기 전에 소스 패키지를 확인하세요.
  2. 필요한 도구, 인증 정보, 지원 환경은 원본 문서에서 확인하세요.
  3. 입력과 원하는 산출물을 설명하고 출처와 결과를 대조하세요.
npx skills add https://github.com/lllllllama/rigorpilot-skills --skill repo-intake-and-plan

시작 전 확인

  • 설치에는 모델 구독, API 한도, 파일 접근 권한이 포함되지 않습니다.
  • 별점 수와 설치 수는 활동 지표이며 독립적인 품질·보안 검증이 아닙니다.
원본 Skill 문서

원본 저장소의 제목, 예시, 코드, 표, 링크, 이미지를 유지해 표시합니다.

repo-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/.

관련 작업

같은 저장소의 Skills

더 많은 Skills

모든 Skills
lllllllama
커뮤니티

paper-context-resolver

Rigor Paper Context helper for README-first deep learning repo reproduction. Use only when the README and repository files leave a narrow reproduction-critical gap and the task is to resolve a specific paper detail such as dataset split, preprocessing, evaluation protocol, checkpoint mapping, or runtime assumption from primary paper sources while recording conflicts. Do not use for general paper summary, repo scanning, environment setup, command execution, title-only paper lookup, or replacing R

설치 수
47.7만
GitHub Stars
484
업데이트
9월 23일
lllllllama
커뮤니티

env-and-assets-bootstrap

Rigor Setup skill for README-first deep learning repo reproduction. Use when the task is specifically to prepare a conservative conda-first environment, checkpoint and dataset path assumptions, cache location hints, and setup notes before any run on a README-documented repository. Do not use for repo scanning, full orchestration, paper interpretation, final run reporting, or generic environment setup that is not tied to a specific reproduction target.

설치 수
47.6만
GitHub Stars
484
업데이트
9월 23일
lllllllama
커뮤니티

minimal-run-and-audit

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 reprooutputs/ 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.

설치 수
47.6만
GitHub Stars
484
업데이트
9월 23일
lllllllama
커뮤니티

ai-research-explore

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 currentresearch with auditable repo understanding, idea gating, fair comparison, and governed experiments written to exploreoutputs/. Do not use for README-first trusted reproduction, open-ended direction finding, narrow code-on

설치 수
33.7만
GitHub Stars
484
업데이트
9월 23일