env-and-assets-bootstrap 是做什么的？
严谨设置技能，用于基于 README 的深度学习仓库复现。专为在基于 README 文档的仓库上运行之前，准备一个保守的 conda 优先环境、检查点和数据集路径假设、缓存位置提示以及设置说明而设计。请勿用于仓库扫描、完整编排、论文解读、最终运行报告，或与特定复现目标无关的通用环境设置。
严谨设置技能，用于基于 README 的深度学习仓库复现。专为准备保守的 conda 优先环境、检查点和数据集路径假设、缓存位置提示以及运行前设置说明而设计。
该技能专门用于在基于 README 文档的深度学习仓库中，为可复现的实验构建一个保守的 conda 优先环境。它通过分析 README 来提取依赖项（如 conda、pip、requirements.txt）、环境变量（如 CUDA_HOME、CHECKPOINT_DIR）以及缓存位置（如 TORCH_HOME、HUGGINGFACE_HUB_CACHE），并生成一个 `env_setup.sh` 脚本，其中包含创建 conda 环境、验证 GPU 兼容性、设置环境变量以及挂载数据集/检查点路径的步骤。该技能适用于 Claude Code 或类似 AI 代理平台，强调在运行前进行最小化、可审计的配置，以避免依赖冲突。它不执行完整的复现运行、代码扫描或结果报告，而是专注于环境准备阶段。
npx skills add https://github.com/lllllllama/ai-paper-reproduction-skill/tree/main/skills/env-and-assets-bootstrapInstalled? 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 →
严谨设置技能，用于基于 README 的深度学习仓库复现。专为在基于 README 文档的仓库上运行之前，准备一个保守的 conda 优先环境、检查点和数据集路径假设、缓存位置提示以及设置说明而设计。请勿用于仓库扫描、完整编排、论文解读、最终运行报告，或与特定复现目标无关的通用环境设置。
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.