README 우선 딥러닝 저장소 재현을 위한 엄격한 인테이크 도우미입니다. 저장소를 스캔하고, README 및 일반 프로젝트 파일을 읽고, 문서화된 명령어를 추출하고, 추론, 평가, 훈련 후보를 분류한 후, 가장 신뢰할 수 있는 최소 재현 계획을 메인 오케스트레이터에게 반환하는 작업에 사용합니다. 환경 설정, 에셋 다운로드, 명령어 실행, 최종 보고, 논문 조회, 종단간 오케스트레이션에는 사용하지 마십시오.
이 스킬은 README-first 접근 방식을 사용하여 딥러닝 저장소를 재현 가능하게 만드는 작업을 돕기 위해 설계된 전문 도구입니다. 주로 AI 에이전트 환경(예: Claude Code, Cursor, Codex 등)에서 사용되며, 저장소를 스캔하여 README 및 설정 파일을 분석하고, 문서화된 명령어를 추출한 후, 추론(inference), 평가(evaluation), 훈련(training) 단계로 분류합니다. 그런 다음 가장 작은 규모의 신뢰할 수 있는 재현 계획(예: 순서대로 실행할 명령어 목록)을 생성하여 메인 오케스트레이터에게 전달합니다. 이 스킬은 재현 과정의 초기 단계에만 집중하며, 환경 설정, 에셋 다운로드, 실제 명령어 실행, 최종 보고서 작성, 논문 검색, 또는 전체 파이프라인 조정과 같은 작업은 수행하지 않습니다. 따라서 엄격한 저장소 분석 및 계획이 필요한 경우에 이상적입니다.
npx skills add https://github.com/lllllllama/ai-paper-reproduction-skill/tree/main/skills/repo-intake-and-planInstalled? Explore more 연구 & 데이터 분석 skills: affaan-m/uspto-database, affaan-m/scholar-evaluation, affaan-m/literature-review · View all 6 →
README 우선 딥러닝 저장소 재현을 위한 엄격한 인테이크 도우미입니다. 저장소를 스캔하고, README 및 일반 프로젝트 파일을 읽고, 문서화된 명령어를 추출하고, 추론, 평가, 훈련 후보를 분류한 후, 가장 신뢰할 수 있는 최소 재현 계획을 메인 오케스트레이터에게 반환하는 작업에 사용합니다. 환경 설정, 에셋 다운로드, 명령어 실행, 최종 보고, 논문 조회, 종단간 오케스트레이션에는 사용하지 마십시오.
This repo contains 4 individual skills — each has its own dedicated page.
Main orchestrator for README-first AI repo reproduction. Use when the user wants an end-to-end, minimal-trustworthy reproduction 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, or broad research assistance outside repository-grounded reproduction.
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.
RigorPilot trusted execution and reporting 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.
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 README guidance by default.
USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.
Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.
Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.
The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
Agent skill repository discovered by 10x-chat research.
Review backend code for quality, security, maintainability, and best practices based on established checklist rules. Use when the user requests a review, analysis, or improvement of backend files (e.g., `.py`) under the `api/` directory. Do NOT use for frontend files (e.g., `.tsx`, `.ts`, `.js`). Supports pending-change review, code snippets review, and file-focused review.