lllllllla/minimal-run-and-audit
RigorPilot 可信执行与报告技能，用于基于README优先的深度学习仓库复现。当任务旨在捕获或规范化所选冒烟测试或记录的推理/评估命令的证据，并写入标准化的 repro_outputs/ 文件（包括当仓库文件更改时的补丁说明）时使用。请勿用于训练执行、初始仓库导入、通用环境设置、论文查询、目标选择、隐藏科学含义变更或独立端到端编排。
RigorPilot 可信执行与报告技能，用于基于README优先的深度学习仓库复现。当任务旨在捕获或规范化所选冒烟测试或记录的推理/评估命令的证据，并写入标准化的 `repro_outputs/` 文件（包括当仓库文件更改时的补丁说明）时使用。
该技能是 RigorPilot 工具链中用于“最小运行与审计”的核心组件，专门针对基于 README 优先原则的深度学习仓库复现场景设计。它自动执行选定的冒烟测试或文档中描述的推理/评估命令，捕获执行环境、命令输出、系统资源使用等关键证据，并将其规范化地写入 `repro_outputs/` 目录下。如果复现过程中仓库文件发生更改，技能还会生成补丁说明（patch notes），详细记录每次修改的内容，确保操作的可追溯性。该技能适用于 Cursor 和 Codex 等 AI 编码代理平台，通过执行预设命令并标准化输出格式，极大简化了从论文到可复现结果的完整证据链生成过程。它遵循严格的隔离和审计策略：不会执行训练任务、初始仓库导入、通用环境设置、论文查询、目标选择、隐藏科学含义变更或独立端到端编排，从而将风险限制在最小范围内。
npx skills add https://github.com/lllllllama/ai-paper-reproduction-skill/tree/main/skills/minimal-run-and-auditInstalled? 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 →
RigorPilot 可信执行与报告技能，用于基于README优先的深度学习仓库复现。当任务旨在捕获或规范化所选冒烟测试或记录的推理/评估命令的证据，并写入标准化的 repro_outputs/ 文件（包括当仓库文件更改时的补丁说明）时使用。请勿用于训练执行、初始仓库导入、通用环境设置、论文查询、目标选择、隐藏科学含义变更或独立端到端编排。
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.
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.