README 우선 딥러닝 저장소 재현을 위한 보수적인 Conda 기반 환경, 체크포인트 및 데이터셋 경로 가정, 캐시 위치 힌트, 설정 노트를 준비하는 엄격한 설정 스킬입니다.
이 스킬은 README 문서가 있는 딥러닝 저장소의 재현 작업을 시작하기 전에 필요한 환경 및 자산을 체계적으로 준비합니다. Conda를 우선으로 하는 보수적인 환경 설정, 체크포인트와 데이터셋 파일의 예상 경로 정의, 캐시 위치 힌트 제공, 그리고 실행 전 설정 노트 작성을 지원합니다. 주로 저장소 개별 분석, 전체 오케스트레이션, 논문 해석, 최종 실행 보고, 또는 특정 재현 대상이 없는 일반적인 환경 설정에는 사용되지 않습니다. 이 스킬은 Claude Code, Cursor, Codex 등 다양한 AI 에이전트 플랫폼에서 사용할 수 있도록 설계되었습니다.
npx skills add https://github.com/lllllllama/ai-paper-reproduction-skill/tree/main/skills/env-and-assets-bootstrapInstalled? 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 설정 스킬입니다. README로 문서화된 저장소에서 실행 전에 보수적인 Conda 기반 환경, 체크포인트 및 데이터셋 경로 가정, 캐시 위치 힌트, 설정 노트를 준비하는 작업에 사용합니다. 저장소 스캐닝, 전체 오케스트레이션, 논문 해석, 최종 실행 보고, 또는 특정 재현 대상이 없는 일반적인 환경 설정에는 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.
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.
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.
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.