READMEファーストの深層学習リポジトリ再現のための、厳密なセットアップスキル。conda優先環境、チェックポイント・データセットのパス前提、キャッシュ場所のヒント、セットアップメモを、実行前に準備する場合に使用します。
このスキルは、READMEドキュメントが整備された深層学習リポジトリの再現実験において、最初のステップとして必要な環境とアセットのブートストラップを厳密に実施するためのものです。主な目的は、condaベースの環境を保守的に構築し、チェックポイントやデータセットのパス前提、キャッシュ場所のヒント、セットアップメモを事前に整理することです。これにより、後続の実行ステップでパスエラーや依存関係の不一致を防ぎ、再現性を高めます。このスキルは、リポジトリのスキャンや完全なオーケストレーション、論文の解釈、最終レポート作成、または特定の再現目標に紐づかない汎用的な環境セットアップには使用しないでください。
npx skills add https://github.com/lllllllama/ai-paper-reproduction-skill/tree/main/skills/env-and-assets-bootstrapInstalled? Explore more 研究＆データ分析 skills: affaan-m/uspto-database, affaan-m/scholar-evaluation, affaan-m/literature-review · View all 6 →
READMEファーストの深層学習リポジトリ再現のための、厳密なセットアップスキル。READMEドキュメントが整備されたリポジトリで、実行前にconda優先環境、チェックポイント・データセットのパス前提、キャッシュ場所のヒント、セットアップメモを準備するタスクに特化して使用します。リポジトリスキャン、完全なオーケストレーション、論文解釈、最終レポート作成、または特定の再現目標に紐づかない汎用的な環境セットアップには使用しないでください。
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.