langchain-retriever
LangChain retriever implementation with various retrieval strategies for RAG applications
Best use case
langchain-retriever is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
LangChain retriever implementation with various retrieval strategies for RAG applications
Teams using langchain-retriever should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/langchain-retriever/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How langchain-retriever Compares
| Feature / Agent | langchain-retriever | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
LangChain retriever implementation with various retrieval strategies for RAG applications
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# LangChain Retriever Skill ## Capabilities - Implement various LangChain retriever types - Configure vector store retrievers - Set up multi-query retrievers for improved recall - Implement contextual compression retrievers - Design ensemble retrievers combining multiple strategies - Configure self-query retrievers for structured filtering ## Target Processes - rag-pipeline-implementation - advanced-rag-patterns ## Implementation Details ### Retriever Types 1. **VectorStoreRetriever**: Basic similarity search 2. **MultiQueryRetriever**: Generates query variations 3. **ContextualCompressionRetriever**: Filters and compresses results 4. **EnsembleRetriever**: Combines multiple retrievers 5. **SelfQueryRetriever**: Structured metadata filtering 6. **ParentDocumentRetriever**: Returns parent chunks ### Configuration Options - Search type (similarity, mmr, similarity_score_threshold) - Number of documents to retrieve (k) - Score thresholds - Metadata filtering - Compression settings ### Dependencies - langchain - langchain-community - Vector store client
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process-builder
Scaffold new babysitter process definitions following SDK patterns, proper structure, and best practices. Guides the 3-phase workflow from research to implementation.
babysitter
Orchestrate via @babysitter. Use this skill when asked to babysit a run, orchestrate a process or whenever it is called explicitly. (babysit, babysitter, orchestrate, orchestrate a run, workflow, etc.)
yolo
Run Babysitter autonomously with minimal manual interruption.
user-install
Install the user-level Babysitter Codex setup.
team-install
Install the team-pinned Babysitter Codex workspace setup.
retrospect
Summarize or retrospect on a completed Babysitter run.
resume
Resume an existing Babysitter run from Codex.
project-install
Install the Babysitter Codex workspace integration into the current project.