414 件 ・ 関連度順
概要と使いどころ
[Workflow] Use when activating the Research & Synthesis workflow — research a topic via web sources, then synthesize per --output={synthesis|business-eval|marketing|course} (knowledge report, business evaluation, marketing strategy, or course material).
日本語の概要は準備中です。原文の説明を表示しています。
Builds DRAFT user personas from the research notes, interview summaries, survey free text and support themes the user provides: for each persona the goals, pains, jobs to be done, context of use, quotes exactly as supplied with their participant codes, and the evidence count behind every attribute, with names, demographics and any detail no participant stated left UNKNOWN rather than invented. Use when the user asks to "build personas from these interviews", "who are our users based on this research", "draft user personas", "turn these research notes into personas" or "update our personas with the new interviews". Do not use for theming a batch of customer feedback into counts, use customer-feedback-theme-synthesis instead; for exit interviews, use exit-interview-synthesis; for the requirements those personas imply, use product-requirements-draft. Drafts for human review; never approves, authorises or signs off.
日本語の概要は準備中です。原文の説明を表示しています。
Use when working with Aws Cdk Deep — aWS CDK application and stack management. Covers stack synthesis, CloudFormation template inspection, diff analysis, deployment orchestration, context values, asset management, and construct library usage. Use when managing CDK apps, debugging synthesis errors, reviewing stack diffs, or analyzing deployed CloudFormation resources.
日本語の概要は準備中です。原文の説明を表示しています。
AiZynthFinder retrosynthetic route planning (CASP) from AstraZeneca Molecular AI. Monte Carlo tree search guided by a template-based neural expansion policy recursively disconnects a target SMILES until precursors are found in a purchasable stock. Covers config.yml (v4 format), aizynthcli batch screening, the AiZynthFinder/AiZynthExpander Python API, one-step disconnections, custom stocks via smiles2stock, scorers, Retro*/breadth-first/DFPN search alternatives, and reading output.json.gz / trees.json. Use for synthesis route planning, synthesizability screening, and building-block/precursor search. For reaction barriers use neb-irc-activation-energy; for 2D reaction scheme drawing use rdkit-chemdraw-cdxml.
日本語の概要は準備中です。原文の説明を表示しています。
Universal deep research agent team. 13-agent pipeline for rigorous academic research on any topic. 7 modes: full research, quick brief, paper review, lit-review, fact-check, Socratic guided research dialogue, and systematic review with optional meta-analysis. Covers research question formulation, Socratic mentoring, methodology design, systematic literature search, source verification, cross-source synthesis, risk of bias assessment, meta-analysis, APA 7.0 report compilation, editorial review, devil's advocate challenges, ethics review, and post-research literature monitoring. Triggers on: research, deep research, literature review, systematic review, meta-analysis, PRISMA, evidence synthesis, fact-check, guide my research, help me think through, 研究, 深度研究, 文獻回顧, 文獻探討, 系統性回顧, 後設分析, 事實查核, 引導我的研究, 幫我釐清, 幫我想想, 我不確定要研究什麼, 研究方向, 研究主題.
日本語の概要は準備中です。原文の説明を表示しています。
Performs retrosynthetic planning using AiZynthFinder (MCTS, template-based), Chemformer (template-free transformer), ASKCOS, and emerging RetroSynFormer with explicit handling of route scoring, building-block availability (eMolecules, Enamine, Mcule), forward prediction validation (Molecular Transformer), and disconnection-aware multi-objective search (MO-MCTS). Use when assessing synthetic feasibility of generated or selected molecules, planning multi-step syntheses, building synthesis-aware design pipelines, or screening libraries for retro-route feasibility.
日本語の概要は準備中です。原文の説明を表示しています。
The live fieldwork layer for psycho-logic — go look at the actual target market NOW instead of reasoning from your training-data stereotype of it. Use when a psycho-logic move needs grounding in a specific place: the user asks 'what do competitors in [region/country] actually do', 'what's the real price band / norm here', 'how do local leaders position themselves', 'research this market before we localise', 'what signals trust to buyers in X', or is about to launch/adapt in a market you haven't studied. Runs structured web research in the local language — local competitors and their signals, price bands, category conventions, review/trust sources, local terminology, taboos and regulations — and synthesises a compact perception profile the four moves consume. Inspired by Rory Sutherland's *Alchemy*. Routed to from psycho-logic; pair with cultural-context (the lens that tells you what to look for). 日本語・中文の相談（「現地の実態を調べて」「调研当地市场」）にも使う。
複数の AI / 人間レビュー結果 (review-self / review-external / findings-pool) を統合し、実在性・重大度・対応優先度・merge 可否を検証する。
Performs retrosynthetic planning using AiZynthFinder (MCTS, template-based), Chemformer (template-free transformer), ASKCOS, and emerging RetroSynFormer with explicit handling of route scoring, building-block availability (eMolecules, Enamine, Mcule), forward prediction validation (Molecular Transformer), and disconnection-aware multi-objective search (MO-MCTS). Use when assessing synthetic feasibility of generated or selected molecules, planning multi-step syntheses, building synthesis-aware design pipelines, or screening libraries for retro-route feasibility.
日本語の概要は準備中です。原文の説明を表示しています。
Performs retrosynthetic planning using AiZynthFinder (MCTS, template-based), Chemformer (template-free transformer), ASKCOS, and emerging RetroSynFormer with explicit handling of route scoring, building-block availability (eMolecules, Enamine, Mcule), forward prediction validation (Molecular Transformer), and disconnection-aware multi-objective search (MO-MCTS). Use when assessing synthetic feasibility of generated or selected molecules, planning multi-step syntheses, building synthesis-aware design pipelines, or screening libraries for retro-route feasibility.
日本語の概要は準備中です。原文の説明を表示しています。
Answer a question from the structured wiki before searching the web — "query the wiki", "what do we know about", synthesis request, cross-project research. Returns cited synthesis with wikilinks and counter-arguments.
日本語の概要は準備中です。原文の説明を表示しています。
Research synthesis from sources — "summarize these URLs or papers", "research this topic with these sources", "create a podcast about", "audio overview", "mind map", "quiz from these sources", "flashcards", "deep research on". Wraps Google NotebookLM via notebooklm-py.
日本語の概要は準備中です。原文の説明を表示しています。
会話で話し合った内容とコードベースの理解から仕様書を作り、プロジェクトのIssue管理先に公開します。課題、利用者の要望、実装・テスト方針、対象外を整理します。
学術・技術文献を検索し、選定基準に沿って比較・統合するスキルです。検索履歴や除外理由を残し、根拠の強さと引用を確認したレビューにまとめます。
複数の視点でまとめた重要な意思決定の草案に、Codexによる外部批評を一度追加します。結論の弱点や見落とし、反対意見の扱いを確認するスキルです。
Use when an operation issue asks to ingest a captured `raw/` source into the LLM Wiki, or the user says "ingest <slug>". Create durable source, entity, concept, synthesis, index, and log pages; use paperclip-distill for Paperclip bundles.
日本語の概要は準備中です。原文の説明を表示しています。
Use when an LLM Wiki operation issue asks a question. Answer from wiki pages and raw sources with citations, name gaps plainly, and offer to file durable synthesis when the answer should compound.
日本語の概要は準備中です。原文の説明を表示しています。
Use a file-based PARA memory system to store, retrieve, and organize durable knowledge across sessions. Trigger on saving facts, daily notes, entity records, weekly synthesis, recall, tacit user patterns, or plan memory.
日本語の概要は準備中です。原文の説明を表示しています。
Turn messages, meeting notes, and documents into sourced decisions, actions, dependencies, and risks
日本語の概要は準備中です。原文の説明を表示しています。
Use this skill when the user wants a systematic literature review, survey, or synthesis across multiple academic papers on a topic. Also covers annotated bibliographies and cross-paper comparisons. Searches arXiv and outputs reports in APA, IEEE, or BibTeX format. Not for single-paper tasks — use academic-paper-review for reviewing one paper.
日本語の概要は準備中です。原文の説明を表示しています。
Orchestrate multi-phase deep research with web search, memory retrieval, pattern matching, and synthesis into structured findings
日本語の概要は準備中です。原文の説明を表示しています。
Query AgentDB through the controller bridge -- semantic routing, hierarchical recall, causal graphs, context synthesis, pattern store/search
日本語の概要は準備中です。原文の説明を表示しています。
Nuclear-grade 12-agent pre-publish release gate. Runs /get-unpublished-changes to detect all changes since last npm release, spawns up to 10 ultrabrain agents for deep per-change analysis, invokes /review-work (orchestrator manual QA plus one gate reviewer) for holistic review, and 1 oracle for overall release synthesis. Runs ONLY when the user explicitly asks for a pre-publish review — a plain publish/release request MUST NOT trigger this; /publish ships directly. Triggers: 'pre-publish review', 'review before publish', 'release review', 'pre-release review', 'ready to publish?', 'can I publish?', 'pre-publish', 'safe to publish', 'publishing review', 'pre-publish check'.
日本語の概要は準備中です。原文の説明を表示しています。
Use when authoring a non-trivial Workflow for research, review, migration, or other multi-agent work, especially when the task needs multiple evidence sources, verification, or synthesis.
日本語の概要は準備中です。原文の説明を表示しています。