lllllllma/env-and-assets-bootstrap
用於基於 README 的深度學習倉庫再現的嚴格環境設置技能。當任務是為一個有 README 文檔的倉庫準備保守的 conda 優先環境、檢查點和數據集路徑假設、緩存位置提示以及設置說明時使用。請勿用於倉庫掃描、完整編排、論文解讀、最終運行報告或與特定再現目標無關的一般性環境設置。
用於基於 README 的深度學習倉庫再現的嚴格環境設置技能。當任務是為一個有 README 文檔的倉庫準備保守的 conda 優先環境、檢查點和數據集路徑假設、緩存位置提示以及設置說明時使用。
此技能專為 README-first 深度學習倉庫再現的嚴格環境設置而設計。其主要目標是在執行任何運行之前，建立一個穩固且可重複的基礎，特別是圍繞 conda 環境、檢查點和數據集路徑、緩存位置以及整體設置說明。它強調保守性與針對性，僅用於為特定再現目標準備環境，避免用於倉庫掃描、完整編排、論文解讀或最終運行報告等一般性任務。該技能支援各種 AI 代理工具，如 Claude Code、Cursor 和 Codex，透過提供結構化的步驟來解析 README 文檔，設定 conda 優先的環境，並驗證關鍵資源（如檢查點和數據集）的路徑假設。其工作原理是要求使用者提供 REPO_PATH 和基準 README 路徑，然後生成一個 `_setup_notes.md` 文件，其中包含環境創建命令、路徑驗證檢查以及快取位置提示。這確保了再現過程的可複製性和穩定性，特別是在處理複雜的深度學習專案時。
npx skills add https://github.com/lllllllama/ai-paper-reproduction-skill/tree/main/skills/env-and-assets-bootstrapInstalled? 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 →
用於基於 README 的深度學習倉庫再現的嚴格環境設置技能。當任務是為一個有 README 文檔的倉庫準備保守的 conda 優先環境、檢查點和數據集路徑假設、緩存位置提示以及設置說明時使用。請勿用於倉庫掃描、完整編排、論文解讀、最終運行報告或與特定再現目標無關的一般性環境設置。
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.
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.