Gates expensive external research (perplexity deep_research) while allowing quick lookups. Enforces knowledge priority - local documentation and project patterns first, external research only when necessary. Permissive mode allows context7, local-rag, and quick perplexity searches. (project)
Multi-source parallel research using Perplexity, Gemini, and Claude. Three modes - Quick (1 query), Standard (3 queries), Extensive (8 queries). USE WHEN user says 'research', 'investigate', 'find out about', 'do research on', or requests comprehensive information gathering.
Browser Controller拡張機能を使ってChatGPT 5.2 Thinkingで3並列以上のウェブ検索・ブレスト・情報収集を実行する。難問にはHeavy thinking（heavy/extended）を使用。指定時のみChatGPT Proで深い考察・ファクトチェック。マルチターン継続（chat）、再取得（recover）、ファイル添付、タブ管理にも対応。「ウェブ検索」「横断検索」「並列検索」「ブレスト」「情報収集」「リサーチ」「再取得」「追加質問」を依頼されたときに使用する。
Collaborate with Codex CLI through iterative AI-to-AI discussion. Use whenever the user mentions "Codex" in any context—asking for Codex's opinion, consulting Codex, discussing with Codex, or any request involving Codex collaboration. Also triggers on phrases like "ask another AI", "get a second opinion from AI", or "AI-to-AI discussion".
Generates answers by synthesizing information from retrieved documents with citations. Use as final RAG pipeline step, when creating answers from multiple sources, or when the user mentions answer generation or synthesis.
OpenAI Codex CLI の仕様と使い方に関する知識を提供。モデル選択、推論レベル、承認モード、サンドボックス、組み込みツール、スラッシュコマンド、設定方法について回答。Use when user asks about Codex CLI, codex command, approval mode, sandbox, AGENTS.md, codex configuration, /model, /review, /compact, apply_patch, or reasoning level. Also use when user says Codex CLI について, codex の使い方, 承認モード, 推論レベル.
Orthogonally-integrated Hegelian syntopical analysis for SAQ/VIVA/concept grounding with systematic textbook citations. Implements thesis extraction → antithesis identification → abductive synthesis across multiple authoritative sources. Tensor-integrated with /m command: activates S×T×L synergies (textbook-grounding × pdf-search × qmd = 0.95). Triggers on requests for model SAQ responses, VIVA preparation, concept explanations requiring textbook evidence, or any PEX exam content needing systematic cross-reference validation.
Aggregates user reviews from Amazon/Reddit and expert reviews from specialized sites with sentiment analysis. Use when user asks to 'get reviews', 'what do users say', 'aggregate reviews', 'user feedback', 'expert opinions', or when orchestrator needs sentiment analysis and pros/cons extraction. Synthesizes consensus patterns across sources.
Three-stage Chain of Thought (CoT) analyzer for /deep-reasoning page. Performs comprehensive analysis in 3 stages Direct Impact → Secondary Effects → Final Conclusion for complex news articles. Optimized for depth over speed.
Filter and classify AI research content for relevance, topic, and author category. Use for bulk triage of raw content before detailed claim extraction.
Routes medical education grounding requests through atomic primitive composition. Four primitives (Σ Source, Τ Textbook, Δ Dialectical, Ρ Response) combine via operators (∘ ⊗ * |) for SAQ/VIVA/Academic response generation. Integrates: limitless, research, pieces, pdf-search, pdf-brain CLIs.
Craft long-form, Chinese blog posts that connect deep-learning papers with their code repositories using the auto-collected bundle (materials_manifest, paper_text, figures, repo_context, lsky cache). Use this skill whenever a user provides such a bundle and wants a TL;DR, method deep dive, experiment analysis, reproduction guide, and media assets.
Convert free-form reviewer notes to structured feedback format with interactive clarification.
This skill should be used when users request research on topics with compiled results saved to a file. It handles web searches, fetching authoritative sources, synthesizing information, and generating structured reports. Trigger phrases include "research and save", "search and summarize", "compile information about", or requests to investigate topics and save results.
Contextual Retrieval implementation for RAG - chunks clinical notes with LLM-generated context prepended to each chunk before embedding. Improves citation accuracy by 49% per Anthropic research.
Also called "wr". Optional research agent for broad multi-source research without polluting context. Use when user requests it or when research needs depth across many sites.
Retain epistemic control by validating AI outputs against logic, first principles, and reality. Detect confident nonsense, false completeness, and category errors.
Get second opinions from external AI agents (OpenAI, Gemini, GitHub). Use when user says "second opinion", "ask other AIs", "what would GPT say", "check with others", or wants to validate an approach.
Integration patterns for web search grounding, including query operator usage, API-based search orchestration, and citation metadata mapping. Triggers: google-search, grounding, search-api, citations, search-operators, web-search.
LLM evaluation and prompt optimization with Evidently.ai. Covers text descriptors, dataset metrics, LLM-as-a-Judge patterns, and automated prompt optimization for classification and generation tasks.
Rapid topic mastery for video/content prep. Takes a topic → generates 5 research questions → parallel PubMed + web search → outputs McKinsey-style brief in 5 minutes. Use BEFORE recording videos or writing content.
Browser Controller拡張機能を使ってChatGPT 5.2 Thinkingで3並列以上のウェブ検索・ブレスト・情報収集を実行する。難問にはHeavy thinking（heavy/extended）を使用。指定時のみChatGPT Proで深い考察・ファクトチェック。マルチターン継続（chat）、再取得（recover）、ファイル添付、タブ管理にも対応。「ウェブ検索」「横断検索」「並列検索」「ブレスト」「情報収集」「リサーチ」「再取得」「追加質問」を依頼されたときに使用する。
This skill should be used when the user asks "what evals can we create", "how do I evaluate this", "design an eval", "create evals for", "how do I know if my LLM is working", "measure quality", or mentions evals, evaluation, scoring rubrics, golden datasets, LLM-as-judge, quality metrics, or judge prompts.
