2,451 件 ・ 関連度順
概要と使いどころ
Create a compact handoff note so a new chat/session can first acknowledge the current state before work resumes. Use when the user asks to close the current session, make a message for the next session, create/export a handoff, resume later, セッションを閉じる, 引き継ぎを作る, or 伝言を作る. Do not use for pasted handoff notes that instruct the current agent to resume work; consume those as instructions instead.
Export the current chat session as a structured Markdown work timeline or blog-draft seed. Use when the user asks for a session log, work log, timeline, 作業ログ, ブログネタ, blog draft, article seed, Zenn, Qiita, or はてな. Do not use for verbatim dialogue export or distilled reusable knowledge only.
"Apply pragmatist philosophy (Peirce, James, Dewey) to frame knowledge as instrumental for action, evaluate ideas by their practical consequences, and conduct inquiry as problem-solving. Use this skill when the user needs to bridge theory and practice, evaluate competing theories by their usefulness, employ abductive reasoning to generate hypotheses, or when they ask 'which theory is more useful here', 'how do I move from abstract ideas to actionable knowledge', or 'what practical difference does this distinction make'.".
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Analyze competitors using Blue Ocean Strategy's ERRC Grid and Strategy Canvas (W. Chan Kim and Renee Mauborgne) to identify uncontested market space, not just who is better on the same axes. Produces an ERRC Grid, Strategy Canvas, positioning matrix, messaging comparison, and differentiation map. Use when the user asks for competitive analysis, competitor research, "how do we compare to X", positioning vs competitors, market landscape, "analyze our competition", "where is the blue ocean", or wants to update competitor docs. Writes to knowledge/markets/competitors.md. For a lightweight competitor summary in the knowledge base, see brand-context. For positioning against them, see positioning-doc.
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。
Initialize, adopt, and route work for a separate Obsidian knowledge vault through the portable claude-obsidian core. Use for vault setup, scaffolding, workspace selection, cross-project configuration, or choosing the correct wiki sub-skill. Triggers: /wiki, set up wiki, scaffold vault, create knowledge base, adopt this vault, Obsidian vault, second brain setup, persistent wiki.
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 the user gives a topic and wants an automated topic-driven narrated explainer, podcast, or knowledge-summary video (Bilibili / YouTube / Xiaohongshu / Douyin / WeChat Channels), or asks to learn visual design patterns from a reference video/image. Trigger when the user mentions creating a knowledge video, narrated explainer, video podcast, or animated infographic-style video from a topic — even if they don't say "video podcast" explicitly. Also trigger when the user wants to regenerate, re-render, rebuild, update, or iterate on a narrated video this skill already produced — e.g. they edited the script/prompt, changed the visuals, or swapped the background music and want the final video remade (reuse the existing videos/{name}/ directory, never start a new project). Do NOT trigger for generic video editing, trimming, format conversion, color grading, or non-narrative video tasks. Produces 4K video via research → script → TTS → Remotion → MP4 + BGM.
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 building or maintaining a personal LLM-powered knowledge base. Triggers: ingesting sources into a wiki, querying wiki knowledge, linting wiki quality, 'add to wiki', 'what do I know about', or any mention of 'LLM wiki' or 'Karpathy wiki'.
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。
Discovers requirements and designs a borderless open data lakehouse using Lakehouse for Apache Iceberg and BigQuery data agents. Use when architecting multi-cloud storage infrastructure (Cloud Storage, AWS S3, Azure Blob), establishing ingestion and AI serving subsystems, configuring Cross-Cloud Interconnect, or deploying Gemini Enterprise Agent Platform and BigQuery data agents. Don't use for single-cloud data warehouses, or when the focus is on Knowledge Catalog metadata governance and Spark-driven IDE analytics workflows (use google-cloud-solution-agentic-analytics-spark-knowledge-catalog instead).
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 setting up CMS workspaces, creating or publishing content types for Experience Cloud sites, managing content scheduling, or configuring audience targeting on content variants. Triggers: 'set up CMS workspace', 'publish content to Experience Cloud site', 'content scheduling community', 'audience targeting content', 'managed content vs unmanaged', 'custom content type metadata', 'CMS channel configuration'. NOT for moderating member-posted content — use admin/experience-cloud-moderation. NOT for Knowledge vs an external CMS or CMS Connect — use architect/knowledge-vs-external-cms.
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。
Search the knowledge base in [Notion](https://composio.dev/toolkits/notion) for answers to customer questions.
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。
Master the `agentbrain` CLI (npm package `agentbrain-cli`). Use this skill whenever the user runs, installs, configures, or asks about `agentbrain` commands; when they interact with the AgentBrain enterprise data hub — organizations, connectors, knowledge bases, workflows, media/documents, knowledge graph, permissions, LLM/prompt config, cost & usage, governance, audit logs; when uploading documents/files to AgentBrain (`agentbrain media upload`); when authenticating (login/logout/token/apiKey); when retrieving MCP context (`agentbrain retrieve-context`); when running SQL against a connector; when the user pastes any `agentbrain …` command; when they edit `~/.agentbrain/config.json`; or when they mention AgentBrain, agent-brain, agentbrain-cli, or the `.agentbrain` folder.
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 the remote rules registry — the shared registry (often connected as a local folder or git repo) that the project's rules are published to and pulled from. Three modes, picked from the user's wording: create scaffolds a new local rules registry ("create a local rules registry", "init a rules registry"); update migrates/promotes local knowledge-base rules from .unikit/memory/ into the remote registry with automatic semver bumps ("migrate local rules to the remote registry", "promote my rules to the registry", "push local rules", "publish local rules", "update the rules registry"); sync pulls registry updates back into memory ("sync the rules registry", "pull registry updates"). Pass "--module <id>" to scope to one knowledge-base module. This skill ONLY orchestrates the CLI (`unikit-ai rules *`) — it never edits .unikit.json or .unikit/memory/ sources directly.
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。
AI Personal Knowledge Manager — Zettelkasten, Obsidian, PARA, Note-Taking System, Second Brain สำหรับนักเรียนรู้ตลอดชีวิต
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。
When the user wants to optimize content for AI search engines, get cited by LLMs, or appear in AI-generated answers. Also use when the user mentions 'AI SEO,' 'AEO,' 'GEO,' 'LLMO,' 'answer engine optimization,' 'generative engine optimization,' 'LLM optimization,' 'AI Overviews,' 'optimize for ChatGPT,' 'optimize for Perplexity,' 'AI citations,' 'AI visibility,' 'zero-click search,' 'how do I show up in AI answers,' 'LLM mentions,' 'optimize for Claude/Gemini,' 'llms.txt,' 'OKF,' 'Open Knowledge Format,' 'knowledge bundle,' or 'agent-readable site.' Use this whenever someone wants their content to be cited or surfaced by AI assistants and AI search engines. For traditional technical and on-page SEO audits, see seo-audit. For structured data implementation, see schema.
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。
Manages the Morphir knowledge base under kb/ — OKF bundles and concept documents. Use when adding content to a bundle, creating a new bundle, checking the knowledge base for conformance or provenance drift, building or querying its SQLite index, managing intent through its lifecycle, recording or reading architectural decisions as Decision Records, or navigating, searching and listing its bundles, concepts and links.
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。
Personal knowledge base powered by Ensue for capturing and retrieving understanding. Use when user wants to save knowledge, recall what they know, manage their toolbox, or build on past learnings. Triggers on "save this", "remember", "what do I know about", "add to toolbox", "my notes on", "store this concept".
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。
Continuous learning framework — learn from work, organize knowledge, build feedback loops. Use when: recording lessons, organizing knowledge, or setting up learning systems that persist across sessions.
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。
Render a wiki into ONE self-contained, offline HTML snapshot (pages + provenance) for sharing or reviewing wiki knowledge. NOT for capturing knowledge (mk:wiki) or generic diagrams (mk:preview).
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。
Capture, gate, query, and render long-term project knowledge through the gated `mewkit wiki` subsystem. Use to create a wiki, propose/approve candidates (scanner-gated), hand off a skill's terminal artifact as a scanned candidate, recall context, search the FTS index, or list pages. Agents may only PROPOSE candidates; canonical pages are written only via human approve. NOT for short-lived JSON memory (see mk:memory); NOT for fetching external sources (see mk:wiki-research); NOT for HTML snapshots (see mk:wiki-render).
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。
Representations for hierarchical skill structures including knowledge graphs and ontological decomposition
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。
Design deployment-focused distillation systems that balance model size, accuracy, calibration, and cascade escalation under real resource limits. Best for teacher-student compression, threshold design, and failure-aware deployment. Activate on "model compression", "teacher- student", "distillation score", "cascade model", "edge deployment", or "model calibration". NOT for generic deep-learning overviews, prompt optimization, or training work without a concrete distillation objective.
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。
Data structures and algorithms for AI agent episodic memory. Covers vector stores (HNSW, IVF, PQ), temporal indexing, knowledge graphs with triple stores, hierarchical summarization, forgetting curves, working/long-term/ procedural memory, and memory consolidation. Deep analysis of MemGPT/Letta, Zep/Graphiti, Mem0, and the Stanford generative agents memory architecture. Teaches the CS fundamentals behind how agents remember, retrieve, and forget. Activate on: "agent memory", "episodic memory", "vector search algorithm", "HNSW", "memory retrieval", "forgetting curve", "knowledge graph memory", "MemGPT", "Letta", "Zep", "Mem0", "memory consolidation", "temporal retrieval", "agent long-term memory", "memory layer". NOT for: conversation protocol design (use agent-conversation-protocols), agent infrastructure selection (use agentic-infrastructure-2026), building RAG pipelines (use ai-engineer).
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。
Framework for coordinating meaning across autonomous agents that use different conceptual models, vocabularies, and knowledge representations. Treats ontologies as first-class negotiable resources rather than hardcoded implementation details.
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。
Methodology for eliciting expert knowledge about cognitive processes, decisions, and mental models in complex tasks
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。