repo-intake-and-plan 是做什麼的？
Rigor Intake 輔助工具，用於以 README 為優先的深度學習儲存庫複現。當任務專門是掃描儲存庫、讀取 README 及常見專案文件、提取文檔命令、分類推論、評估與訓練候選項目，並將最小可信任的複現計畫回傳給主協調器時使用。請勿用於環境設置、資產下載、命令執行、最終報告、論文查詢或端到端協調。
Rigor Intake 輔助工具，用於以 README 為優先的深度學習儲存庫複現。專門用於掃描儲存庫、讀取 README 及常見專案文件、提取文檔命令、分類推論、評估與訓練候選項目，並將最小可信任的複現計畫回傳給主協調器。請勿用於環境設置、資產下載、命令執行、最終報告、論文查詢或端到端協調。
此技能是一個專門用於深度學習儲存庫複現的 Rigor Intake 輔助工具。其主要目的是以 README 檔案為起點，掃描整個儲存庫，讀取常見的專案文件，從中提取已文檔化的命令，並將這些命令分類為推論、評估和訓練候選項目。最終，它會生成一個最小但可信任的複現計畫，並回傳給主協調器。此技能適用於需要先理解儲存庫結構與使用方法的場景，能有效簡化複現流程並節省時間。支援的平台包括 Claude Code、Coder、Codex 等 AI 代理工具。其運作方式是透過分析專案的 README、設定檔、腳本等，自動化地提取關鍵資訊，無需手動逐一查閱文件。
npx skills add https://github.com/lllllllama/ai-paper-reproduction-skill/tree/main/skills/repo-intake-and-planInstalled? 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 →
Rigor Intake 輔助工具，用於以 README 為優先的深度學習儲存庫複現。當任務專門是掃描儲存庫、讀取 README 及常見專案文件、提取文檔命令、分類推論、評估與訓練候選項目，並將最小可信任的複現計畫回傳給主協調器時使用。請勿用於環境設置、資產下載、命令執行、最終報告、論文查詢或端到端協調。
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.