Most agent demos stop at reading files and calling APIs. The moment you ask an OpenClaw agent to answer a real business question — “which customers churned last quarter?”, “what’s the p95 latency on checkout?”, “find similar support tickets to this one” — you need the agent to actually talk to a database. Over the past few weeks a cluster of new and updated ClawHub skills has made that dramatically easier, covering classic SQL, modern NoSQL, and the vector stores that power retrieval-augmented generation. Here are five worth installing today.
1. pg-claw: Postgres with guardrails
pg-claw is the skill most Postgres shops end up installing first. It wraps the standard libpq client but adds a schema-introspection step that runs automatically on first connect, so the agent gets a compact summary of tables, columns, and foreign keys injected into its context rather than having to \SELECT * FROM information_schema\ every time. It also enforces read-only mode by default — writes require an explicit \–allow-writes\ flag — which is the kind of guardrail you want when an LLM is composing your SQL.
npx clawhub@latest install pg-clawSource: github.com/openclaw/skills. Good for: analytics agents, internal tooling, anything where you’d otherwise be hand-writing a natural-language-to-SQL layer.
2. mongo-pilot: NoSQL without the footguns
MongoDB’s flexible schema is great for developers and terrible for LLMs, which tend to hallucinate field names. mongo-pilot solves this by sampling documents from each collection on startup and building a probabilistic schema the agent can reason about. It exposes a single query tool that accepts either a MQL document or a natural-language description, and returns results capped at a configurable row limit so your context window doesn’t explode.
npx clawhub@latest install mongo-pilotFind it on the VoltAgent awesome-openclaw-skills list under the data category.
3. qdrant-claw: Vector search for RAG pipelines
If you’re building a retrieval-augmented agent, qdrant-claw is the most polished vector-store skill on ClawHub right now. It handles collection creation, upserts, hybrid search (dense + sparse), and payload filtering, and it ships with a built-in chunker so you can point it at a directory of markdown or PDFs and get an indexed knowledge base in one command. The 0.4 release added native support for Qdrant’s new multi-vector mode, which is particularly useful for ColBERT-style late-interaction retrieval.
npx clawhub@latest install qdrant-claw4. duckdb-lens: Analytics on local files
Not every question needs a production database. duckdb-lens gives your agent an embedded DuckDB instance that can query CSVs, Parquet files, and JSON directly from disk or S3. It’s become the go-to skill for “drop a folder of spreadsheets on the agent and ask questions” workflows, and the fact that it runs entirely in-process means there’s no server to configure. It’s maintained in the sundial-org awesome list and updated almost weekly.
npx clawhub@latest install duckdb-lens5. redis-claw: Caches, queues, and session memory
Rounding out the list, redis-claw is less about analytics and more about giving agents a fast key-value scratch space. You can use it as an ephemeral memory store between turns, a rate-limiter for outbound API calls, or a pub/sub bus to coordinate multiple agents. It also speaks Redis Streams, which makes it a surprisingly capable lightweight task queue for multi-agent setups.
npx clawhub@latest install redis-clawA note on safety
Database skills are one of the higher-risk categories on ClawHub because a hallucinated DELETE or DROP can ruin your day. Every skill on this list defaults to read-only, but that’s a convention, not a hard guarantee — always run agents against a replica or a scoped database user with the minimum privileges needed, and keep backups. If you’re installing a skill from a less-trafficked fork, check the source for any exec-style tools that could shell out and bypass the read-only mode. The LeoYeAI openclaw-master-skills repo publishes a weekly vetting report that’s worth bookmarking.
Putting it together
A realistic stack for an analytics agent in April 2026 looks something like pg-claw for the warehouse, duckdb-lens for ad-hoc file queries, and qdrant-claw for semantic search over internal docs — with redis-claw underneath for memory and rate-limiting. That’s four install commands and maybe ten minutes of configuration, and you end up with an agent that can answer questions no single-skill demo ever could. The database layer has quietly become one of the most mature corners of the ClawHub ecosystem, and it’s a good time to take advantage of it.
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