AI 論文再現的主體協調器，用於端到端、最小可信的再現流程。
此技能作為一個主體協調器，專注於 AI 論文再現流程。其核心方法是「README 優先」，即先徹底讀取目標倉庫，然後從中選擇最簡單且有文件記載的推理或評估目標作為起點。它會協調整個流程，包括倉庫資料攝取、環境設置、可信執行，並可選地進行可信訓練、倉庫分析與論文差距解決。該技能強制執行保守的補丁規則，記錄證據、假設、偏差以及人工決策點，最終生成標準化的 `repro_outputs/` 結果包。它特別適用於需要端到端、最小可信再現流程的場景，但明確不應用於論文摘要、通用環境設置、獨立的倉庫掃描或單獨的命令執行等。
npx skills add https://github.com/lllllllama/ai-paper-reproduction-skill/tree/main/skills/ai-paper-reproductionInstalled? Explore more 研究與資料分析 skills: affaan-m/uspto-database, affaan-m/scholar-evaluation, affaan-m/literature-review · View all 6 →
主體協調器，用於 README 優先的 AI 倉庫再現。當使用者想要一個端到端、最小可信的再現流程時使用。此流程會先讀取倉庫，選擇最小的有文件記載的推理或評估目標，協調資料攝取、設置、可信執行、可選的可信訓練、可選的倉庫分析以及可選的論文差距解決，強制執行保守的補丁規則，記錄證據、假設、偏差以及人工決策點，並寫入標準化的 repro_outputs/ 結果包。請勿用於論文摘要、通用環境設置、獨立的倉庫掃描、單獨的命令執行、無聲的協議變更，或在倉庫基礎再現之外進行廣泛的研究協助。
This repo contains 4 individual skills — each has its own dedicated page.
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.
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.