「owl」の検索結果

Build and query a local Markdown knowledge base ("vault"). TWO functions — (1) CONVERT raw files (PDF, Word/docx, PowerPoint/pptx, Excel/xlsx, csv/tsv, images, html, md/txt, json/yaml/code, audio/video) into clean Markdown with retrieval-friendly frontmatter; local-first (pandoc / python-pptx / openpyxl / pymupdf4llm / whisper), with cloud OCR (MinerU) only as a fallback. (2) ANSWER questions over the resulting vault with retrieval discipline — self-monitor coverage, flag missing/lossy content, and propose Maps-of-Content (MOCs). Triggers: "build/sync my local knowledge base", "convert these files to markdown for AI", "整理我的资料库", "把文件转成 md 给 AI 读", "本地知识库", "读我的本地 vault 回答", "这个主题我的资料里怎么说". Not for: one-off web research, or files that are already in a single doc you can read 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。

Sync high-value dev knowledge (skills, decisions, lessons) into Google NotebookLM for cloud recall, podcasts, and flashcards. Pairs with Dockit/deep-search. Use for NotebookLM, nlm, knowledge base, skill sync, or cross-machine AI 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。

Knowledge systematization engine — analyze codebases, generate Personas, JTBD, Process Flows, technical docs, SOP user guides, API references. Output as Markdown or VitePress Premium. SEO-optimized, AI/LLM-readable. One scan = complete knowledge base.

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。

Odoo 20 development knowledge base covering actions, controllers, data files, API decorators, SQL constraints, indexes, modules, fields, manifests, mixins, ORM methods, migrations, OWL components, performance, QWeb reports, security, testing, transactions, translations, and XML views. Use when writing, reviewing, or debugging Odoo 20 Python, XML, or JavaScript code, creating or modifying modules, fixing performance issues, or looking up Odoo 20 API patterns and best practices.

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。

Odoo 19 development knowledge base covering actions, controllers, data files, API decorators, SQL constraints, indexes, modules, fields, manifests, mixins, ORM methods, migrations, OWL components, performance, QWeb reports, security, testing, transactions, translations, and XML views. Use when writing, reviewing, or debugging Odoo 19 Python, XML, or JavaScript code, creating or modifying modules, fixing performance issues, or looking up Odoo 19 API patterns and best practices.

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。

AFFiNE is an open source workspace that combines docs, whiteboards, and lightweight databases in one product. This skill is useful when an agent needs to structure notes, maintain linked knowledge, or automate content workflows around AFFiNE workspaces and self-hosted deployments.

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 operating skill: bind the public task, act from evidence, preserve boundaries, and verify the delivered result.

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 wants to build, refresh, or visualize the repo's knowledge graph — code structure plus the committed wiki/ — via graphify. Produces an interactive graph + a GRAPH_REPORT.md of God Nodes, surprising connections, and suggested questions. Trigger on "/graph", "build the knowledge graph", "graph the repo", "map the codebase", "refresh the graph", "visualize the second brain".

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。

Integrate a new source into the project knowledge base. Wraps the agent-knowledge-updater agent. Source can be a local file path, a URL, or pasted content. The updater reads the source, classifies it, identifies which existing library files it touches, makes surgical updates or creates new files, rebuilds the shelf-index, and appends to log.md.

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。

Parallel map-reduce bulk ingest for large source sets. Map phase dispatches read-only knowledge-extractor agents (≤N-wide, default Haiku) emitting per-source JSON extracts; Python routes extracts by target library file (fuzzy-merging new topics, pre-allocating new files, flagging oversized topics); reduce phase dispatches one agent-knowledge-updater per file (parallel, one writer per file); single shelf-index rebuild + one log.md entry. Resumable. Supersedes kb-ingest-batch.

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。

Build and query fully local RAG knowledge bases from documents (PDF, MD, code, etc.) using RLAMA and Ollama — no cloud, no data leaving the machine. Triggers on 'local knowledge base', 'search documents', 'document Q&A', 'RAG query', 'ingest files', 'semantic 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。

デジタルプロダクト系の専門領域（UI/UX, マーケティング, コピーライティング, ASO, グロース等）の 暗黙知を抽出・言語化・構造化し、他のSkillで使えるエージェント定義と評価基準を生成するスキル。 素人では判断できない「良い/悪い」の基準を、AI が専門家レベルで調査・分析・言語化し、 再利用可能なドメインナレッジとして出力する。 Use when: 評価基準を作りたい、ドメイン知識を抽出したい、専門家の視点を言語化したい、 暗黙知を構造化したい、ベストプラクティスを整理したい。 Triggers: "ドメイン知識", "専門知識", "評価基準", "domain expertise", "expertise extract", "知識抽出", "暗黙知", "ベストプラクティス抽出", "評価基準を作りたい", "専門家の視点", "ドメインナレッジ", "domain knowledge", "expertise builder"

AI systematic knowledge acquisition from unfamiliar territory — deliberate model-building with feedback loops. Maps spaced repetition principles to AI reasoning: survey the territory, hypothesize structure, explore with probes, integrate findings, verify understanding, and consolidate for future retrieval. Use when encountering an unfamiliar codebase or domain, when a user asks about a topic requiring genuine investigation rather than recall, when multiple conflicting sources require building a coherent model, or when preparing to teach a topic and deep understanding is required first.

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。

Epistemic transparency — acknowledging uncertainty, flagging limitations, avoiding overconfidence, and communicating what is known, unknown, and uncertain with proportional confidence. Maps the HEXACO personality dimension to AI reasoning: truthful calibration of confidence, proactive disclosure of gaps, and resistance to the temptation to appear more certain than warranted. Use before presenting a conclusion, when answering questions where knowledge is partial or inferred, after noticing a temptation to state uncertain information as certain, or when a user is making decisions based on provided information.

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。

Reference Hildegard von Bingen's Physica natural history knowledge. Covers classification of plants, stones, animals, fish, birds, elements, and trees with their medicinal, symbolic, and practical properties. Enables cross- referencing between categories and application guidance. Use when exploring a specific plant, stone, or animal from Hildegard's perspective, researching medieval natural history and cosmology, cross-referencing properties across categories, or integrating Physica knowledge into health, spiritual, or creative practice.

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。

Queries the NCATS Translator ARAX production API for bounded, typed, provenance-rich one-hop and endpoint-pinned two-hop biomedical knowledge-graph relationships. Use for Biolink-constrained RTX-KG2 lookup, explicit selected-provider ARAX federation, separate entity normalization, qualifier-aware graph traversal, and inspection of TRAPI edge bindings, publications, and knowledge-source provenance. Do not use for inference, ranking, open-ended pathfinding, clinical guidance, or sensitive queries.

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 to create, maintain, query, or visualize a local Markdown knowledge base. ThinkWiki can initialize a wiki, import files or webpages, collect inbox items, answer from existing knowledge, save higher-value outputs, and generate HTML viewer and graph artifacts.

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 a knowledge artifact needs review before sharing or execution. Not for source or remote-system changes.

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 these skills when you need to discover and explore data assets in the Knowledge Catalog. It allows you to search for entries, lookup specific metadata, and explore aspect types to understand your data platform's assets.

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。

コンペ知識を knowledge/ ストック層（原子ページ + INDEX）に効率よく蓄積・検索する。daily_report のフローから再利用可能な知見を蒸留して昇格させ、重複を排除しながら最新化する。知見が出たとき・週次整理・過去知見を引きたいときに使う。

Run a dogfooding session for hyalo — build from source, exercise the CLI against real knowledgebases (own KB, MDN, GitHub Docs, VS Code docs), find bugs, verify recent fixes, assess UX, measure performance, and write a structured report. Use this skill whenever the user says /dogfood, "dogfood", "run a dogfood session", "test hyalo against real data", or wants to exercise hyalo on real knowledgebases to find issues.

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 you need to query an OpenCTI instance — is this IOC already known, what entities/reports/campaigns exist for an actor — or push new intel into it — creating indicators/observables, labelling, TLP markings, relationships, or importing a STIX 2.1 bundle. Two-way integration. Commonly invoked by /ip-investigation and friends to check whether an indicator is already in your knowledge base, and by analytical skills that want to publish their findings back to OpenCTI. Reads $OPENCTI_URL and $OPENCTI_TOKEN.

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 needing global or relational understanding of codebases or knowledge corpora. Keywords: GraphRAG, knowledge graph, entity extraction, community summarization, graph traversal, Microsoft GraphRAG.

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。

Consolidated Galyarder Framework Knowledge intelligence bundle.

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。