repo-intake-and-plan 是做什么的？
用于README优先的深度学习仓库复现的严格分析助手。当任务明确是需要扫描仓库、读取README和常见项目文件、提取文档化命令、分类推理、评估和训练候选命令，并向主编排器返回最小可信复现计划时使用。不要用于环境搭建、资产下载、命令执行、最终报告、论文搜索或端到端编排。
用于README优先的深度学习仓库复现的严格分析助手。扫描仓库、读取README和常见项目文件、提取文档化命令、分类推理/评估/训练候选命令，并返回最小可信复现计划。
该技能专门针对“仍处于探索阶段的深度学习仓库”的快速理解与复现规划，设计用于AI代理（如Claude Code、Cursor、Codex）的工具调用场景。它遵循“README优先”工作流：首先扫描目标仓库的目录结构并提取README、配置、脚本等核心文件，然后解析其中所有文档化的执行命令（如训练、推理、评估），最后自动对命令按用途分类（推理、评估、训练）并生成一个仅包含最少必要步骤的复现计划。它特别强调“最小可信”原则，避免引入无关步骤或依赖，因此适用于作为上层编排任务的前端模块，不处理环境搭建、资产下载、实际执行、论文搜索或报告生成等后续环节。主要支持GitHub仓库或本地路径的输入，要求代理能够访问文件系统及常用编程语言（Python/Shell）的代码解析。
npx skills add https://github.com/lllllllama/ai-paper-reproduction-skill/tree/main/skills/repo-intake-and-planInstalled? Explore more 研究与数据分析 skills: affaan-m/uspto-database, affaan-m/scholar-evaluation, affaan-m/literature-review · View all 6 →
用于README优先的深度学习仓库复现的严格分析助手。当任务明确是需要扫描仓库、读取README和常见项目文件、提取文档化命令、分类推理、评估和训练候选命令，并向主编排器返回最小可信复现计划时使用。不要用于环境搭建、资产下载、命令执行、最终报告、论文搜索或端到端编排。
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.