Babysitter is a workflow engine for AI coding agents that enforces predefined steps, quality checks, human approvals, and decision records. It is used to coordinate complex, repeatable agent workflows across supported coding tools. The catalogue contains skills, agents, instructions, settings, a plugin, and an MCP integration for its workflow.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add a5c-ai/babysitter --skill langchain-retrievergit clone --depth 1 https://github.com/a5c-ai/babysitterWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/a5c-ai/babysitter/langchain-retriever)<a href="https://agentmods.dev/skills/a5c-ai/babysitter/langchain-retriever"><img src="https://agentmods.dev/badge/skills/a5c-ai/babysitter/langchain-retriever/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/a5c-ai/babysitter/langchain-retriever"><img src="https://agentmods.dev/badge/skills/a5c-ai/babysitter/langchain-retriever.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00019 | $0.00357 |
| Opus 5 | $0.00010 | $0.00179 |
| Sonnet 5 | $0.00004 | $0.00071 |
| Haiku 4.5 | $0.00002 | $0.00036 |
Grade A, and why
langchain-retriever scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
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
- VectorStoreRetriever: Basic similarity search
- MultiQueryRetriever: Generates query variations
- ContextualCompressionRetriever: Filters and compresses results
- EnsembleRetriever: Combines multiple retrievers
- SelfQueryRetriever: Structured metadata filtering
- 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
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 60 lines · 19 tokens per session scan A e49955a54880
langchain-retriever is a skill published in the GitHub repository a5c-ai/babysitter (1,788 stars, last pushed 5d ago), licensed MIT. It adds 19 tokens to every session and 357 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-05.
Other skills, from other repositories
documentation-search
Search the internal knowledge base for runbooks, architecture documentation, ADRs, best practices, and troubleshooting guides using RAG. Use when looking for internal documentation, deployment procedures, architecture decisions, or operational runbooks.
oracle
Designing and evaluating AI/ML systems: prompt engineering, RAG design, LLM application patterns, AI safety, evaluation frameworks, MLOps, cost optimization. Use for AI pipelines or eval harnesses.
seek
Designing search engines and vector DBs for full-text, vector, and hybrid retrieval, including permission-aware retrieval for multi-tenant or per-role corpora. Use for search design, index optimization, the RAG retrieval layer, or deciding where ACL filtering belongs in the query path.
llm-app-patterns
Production-ready patterns for building LLM applications, inspired by Dify and industry best practices.
rag-evaluation
Comprehensive RAG evaluation with retrieval metrics, generation quality, and end-to-end testing. Use this skill when measuring and improving RAG system performance. Activate when: RAG evaluation, RAGAS, retrieval metrics, generation quality, RAG testing, MRR, recall, faithfulness.
agentic-rag
Build autonomous RAG agents that reason, plan, and use tools for complex retrieval tasks. Use this skill when simple retrieve-and-generate isn't enough. Activate when: agentic RAG, RAG agent, multi-step retrieval, tool-using RAG, autonomous retrieval, query decomposition.