Knowledge skills
Find skills for document ingestion, chunking, embeddings, retrieval, source citation, semantic search, and grounded agent answers.
Built for builders searching for RAG, retrieval, knowledge base, embedding, and document ingestion skills for agents.
Matched
16
Stars
531K
Workflow
Retrieve
Data
Docs
Agent jobs
These pages are built for high-intent search and for agents that need a structured shortlist with install commands, trust signals, audit links, and real outcome evidence before installing third-party code.
01
Build a RAG knowledge base over product docs
02
Crawl documentation and prepare searchable markdown
03
Retrieve relevant source passages before answering
04
Compare embedding, indexing, and retrieval options
Task routes
Build RAG
Ingest docs, chunk content, retrieve relevant passages, and cite sources in agent answers.
Crawl docs
Turn documentation pages into clean markdown or records that an agent can search and reuse.
PDF to markdown
Extract readable text, tables, and metadata from PDFs for agent workflows.
Ranked shortlist
#01
Open-source LLM knowledge platform: turn raw documents into a queryable RAG, an autonomous reasoning agent, and a self-maintaining Wiki.
98
Quality
87
Trust
—
Proven
$ npx skills add Tencent/WeKnora#02
📑 PageIndex: Document Index for Vectorless, Reasoning-based RAG
100
Quality
86
Trust
—
Proven
$ npx skills add VectifyAI/PageIndex#03
Qdrant - High-performance, massive-scale Vector Database and Vector Search Engine for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
100
Quality
88
Trust
—
Proven
$ npx skills add qdrant/qdrantThis repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. Each technique has a detailed notebook tutorial.
100
Quality
86
Trust
—
Proven
$ npx skills add NirDiamant/RAG_Techniques#05
Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native database​.
100
Quality
90
Trust
—
Proven
$ npx skills add weaviate/weaviate#06
Private AI platform for agents, assistants and enterprise search. Built-in Agent Builder, Deep research, Document analysis, Multi-model support, and API connectivity for agents.
100
Quality
88
Trust
—
Proven
$ npx skills add arc53/DocsGPTEvaluation
Preserves source URLs and citation metadata
Separates ingestion, retrieval, and generation steps
Documents chunking and evaluation assumptions
Can be tested against a known question set
Questions
Check source preservation, retrieval quality, chunking strategy, supported data stores, and whether the workflow can cite evidence.
Yes. Document parsing prepares clean text or tables; RAG skills usually add indexing, retrieval, and source-grounded answer workflows.
Knowledge skills
Find skills for document ingestion, chunking, embeddings, retrieval, source citation, semantic search, and grounded agent answers.
Built for builders searching for RAG, retrieval, knowledge base, embedding, and document ingestion skills for agents.
Matched
16
Stars
531K
Workflow
Retrieve
Data
Docs
Agent jobs
These pages are built for high-intent search and for agents that need a structured shortlist with install commands, trust signals, audit links, and real outcome evidence before installing third-party code.
01
Build a RAG knowledge base over product docs
02
Crawl documentation and prepare searchable markdown
03
Retrieve relevant source passages before answering
04
Compare embedding, indexing, and retrieval options
Task routes
Build RAG
Ingest docs, chunk content, retrieve relevant passages, and cite sources in agent answers.
Crawl docs
Turn documentation pages into clean markdown or records that an agent can search and reuse.
PDF to markdown
Extract readable text, tables, and metadata from PDFs for agent workflows.
Ranked shortlist
#01
Open-source LLM knowledge platform: turn raw documents into a queryable RAG, an autonomous reasoning agent, and a self-maintaining Wiki.
98
Quality
87
Trust
—
Proven
$ npx skills add Tencent/WeKnora#02
📑 PageIndex: Document Index for Vectorless, Reasoning-based RAG
100
Quality
86
Trust
—
Proven
$ npx skills add VectifyAI/PageIndex#03
Qdrant - High-performance, massive-scale Vector Database and Vector Search Engine for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
100
Quality
88
Trust
—
Proven
$ npx skills add qdrant/qdrantThis repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. Each technique has a detailed notebook tutorial.
100
Quality
86
Trust
—
Proven
$ npx skills add NirDiamant/RAG_Techniques#05
Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native database​.
100
Quality
90
Trust
—
Proven
$ npx skills add weaviate/weaviate#06
Private AI platform for agents, assistants and enterprise search. Built-in Agent Builder, Deep research, Document analysis, Multi-model support, and API connectivity for agents.
100
Quality
88
Trust
—
Proven
$ npx skills add arc53/DocsGPTEvaluation
Preserves source URLs and citation metadata
Separates ingestion, retrieval, and generation steps
Documents chunking and evaluation assumptions
Can be tested against a known question set
Questions
Check source preservation, retrieval quality, chunking strategy, supported data stores, and whether the workflow can cite evidence.
Yes. Document parsing prepares clean text or tables; RAG skills usually add indexing, retrieval, and source-grounded answer workflows.