minimal-run-and-audit 是做什麼的？
RigorPilot 的可信執行與回報技能，用於以 README 為優先的深度學習研究再現。當任務特別需要從選定的冒煙測試或已記錄的推論/評估指令中擷取或標準化證據，並寫入標準化的 repro_outputs/ 檔案（包含當儲存庫檔案變更時的修補筆記）時使用。請勿用於訓練執行、初始儲存庫匯入、通用環境設定、論文查詢、目標選擇、隱含科學意義的變更，或單獨執行端到端編排。
RigorPilot 的可信執行與回報技能，用於以 README 為優先的深度學習研究再現。專門用來從選定的冒煙測試或已記錄的推論/評估指令中擷取或標準化證據，並寫入標準化的 `repro_outputs/` 檔案，包含當儲存庫檔案變更時的修補筆記。
這個技能是 RigorPilot 框架中專門負責可信執行與回報的模組，專注於深度學習研究再現流程中的「證據標準化」階段。當開發者需要從指定的冒煙測試、推論腳本或評估命令中擷取執行結果，並將其轉換為結構化的 `repro_outputs/` 目錄結構時，這個技能能自動化處理。它會自動記錄所有標準輸出、錯誤訊息、產生的圖表與數據檔案，並在修改儲存庫原始碼時產生一致的修補筆記。此技能支援 Claude Code、Cursor 與 Codex 等主流 AI 代理平台，但不適用於訓練執行、初始儲存庫匯入、通用環境設定、論文查詢、目標選擇或隱含科學意義變更的場景。其運作方式是基於 SKILL.md 文件中定義的嚴格規範，確保每次執行都能產出可驗證且可追溯的再現報告。
npx skills add https://github.com/lllllllama/ai-paper-reproduction-skill/tree/main/skills/minimal-run-and-auditInstalled? 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 →
RigorPilot 的可信執行與回報技能，用於以 README 為優先的深度學習研究再現。當任務特別需要從選定的冒煙測試或已記錄的推論/評估指令中擷取或標準化證據，並寫入標準化的 repro_outputs/ 檔案（包含當儲存庫檔案變更時的修補筆記）時使用。請勿用於訓練執行、初始儲存庫匯入、通用環境設定、論文查詢、目標選擇、隱含科學意義的變更，或單獨執行端到端編排。
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.