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Found 270 Skills
How to work in an OKF starter project (the `okf` starter pack): a knowledge base that is conformant with Google's Open Knowledge Format (OKF) from commit one — `concepts/`, `references/`, `notes/`, a reserved `index.md` navigation hub, and a reserved `log.md` change history. Read when the project has these folders plus reserved files, OR when asked whether a document is OKF-conformant, or to add a document to an OKF knowledge base. Carries the OKF conventions (non-empty `type` on every non-reserved doc; reserved files carry no frontmatter) as guidance, not enforcement. Complements the platform `open-knowledge` skill; does not replace it.
Create or update docs/knowledge-base/ chapters in mdbook format for human-first technical documentation.
Knowledge Base RAG implements the complete Retrieval-Augmented Generation pipeline: document ingestion, intelligent chunking, embedding generation, vector store indexing, semantic retrieval, and grounded response generation.
FAQ identification from support tickets, step-by-step tutorial creation, screenshot/video script guidance, search optimization, and self-service deflection tracking.
Build a knowledge base from web content with Firecrawl. Use for local reference docs, RAG-ready chunks, fine-tuning datasets, documentation mirrors, topic corpora, or LLM-ready markdown organized from web sources.
Shared foundation for Oracle & Corrector agents. Establishes the source hierarchy for resolving conflicts between documentation, code, and specs. Load this skill first when investigating how the system works.
Dify dataset retrieve API for knowledge base chunk search/testing. Use when integrating or debugging Dify knowledge base retrieval requests, retrieval_model options, or response shaping.
AI-native open knowledge base about Taiwan built with Astro v5, featuring bilingual content (zh-TW/en), D3.js knowledge graph, and structured Markdown SSOT architecture.
AI-first knowledge base and startup OS with file-based storage, AI agents, scheduled jobs, and embedded apps
Amazon Bedrock Knowledge Bases for RAG (Retrieval-Augmented Generation). Create knowledge bases with vector stores, ingest data from S3/web/Confluence/SharePoint, configure chunking strategies, query with retrieve and generate APIs, manage sessions. Use when building RAG applications, implementing semantic search, creating document Q&A systems, integrating knowledge bases with agents, optimizing chunking for accuracy, or querying enterprise knowledge.
Use this skill when designing help center architecture, writing support articles, or optimizing search and self-service. Triggers on knowledge base, help center, support articles, self-service, article templates, search optimization, content taxonomy, and any task requiring help documentation design or management.
How to work in a Knowledge Base project (the `knowledge-base` starter pack). Read when the project has the three-layer source-grounded layout — `external-sources/` → `research/` → `articles/` — or when asked how this project is organized. Carries the layer model, per-folder rules, status flows, and log discipline so this guidance does NOT live inside template bodies or log.md. The three procedures live elsewhere: ingest in the platform `open-knowledge` skill, research and consolidate as their own sibling skills in this pack. Complements the platform `open-knowledge` skill; does not replace it.