Agent skill · magnus919
haystack
Expert skill for production search and NLP pipelines with Haystack (deepset). Pipeline DAG composition, document stores, retrievers, PromptBuilder (Jinja2), generators, evaluation, Hayhooks deployment. Use when building search pipelines or comparing NLP application frameworks.
What it needs
About 3k tokens when loaded.
What this skill does
Haystack Expert Skill Haystack (by deepset) is a production-oriented framework for building search and NLP pipelines. Its core abstraction is the Pipeline — a directed acyclic graph of typed components with explicit connections. Unlike LangChain's LCEL (pipe operator) or LlamaIndex's query engines, Haystack pipelines are declared upfront with addcomponent and connect, giving validated, debuggable DAGs. Core Paradigm Core Principles 1. Pipelines are validated DAGs. addcomponent + connect. Pipeline validation catches errors BEFORE execution — leverage this during development. 2. Components are typed. Each component has input/output slots. Connections must match types. This prevents runtime errors. 3. PromptBuilder uses Jinja2. Templates are Jinja2 strings, not f-strings. {{documents}}, {{query}}, {{question}} are variable placeholders. 4. Indexing and query are separate pipelines. One pipeline loads/cleans/embeds/writes documents. Another retrieves/generates answers. They share the DocumentStore. 5. Evaluation is a pipeline too. Add evaluator components to measure faithfulness, relevancy, or custom metrics. Where to Start You already have... Start here --- --- Nothing — exploring Haystack Build a basic indexing + query pipeline Documents to index Build an indexing pipeline (converters, splitter, embedder, writer) A search use case Build a query pipeline (embedder, retriever, prompt, generator) A production deployment Add Hayhooks + evaluation pipeline Quick Reference Task Approach Reference ------ ---------- ----------- Build indexing pipeline addcomponent -> connect -> run references/pipeline-design.md Build query pipeline retriever -> promptbuilder -> generator references/pipeline-design.md Choose document store InMemory (dev), Elasticsearch/Pinecone (prod) references/document-stores.md Embedding retrieval SentenceTransformersTextEmbedder + EmbeddingRetriever references/retrievers.md Hybrid retrieval BM25 + Embedding in parallel, DocumentJoiner references/retrievers …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills magnus919/haystack