Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/HybridAIOne/hybridclawnpx agentmods add skills/hybridaione/hybridclaw/warehouse-sqlWrote 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/hybridaione/hybridclaw/warehouse-sql)<a href="https://agentmods.dev/skills/hybridaione/hybridclaw/warehouse-sql"><img src="https://agentmods.dev/badge/skills/hybridaione/hybridclaw/warehouse-sql.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Rogue Agent · line 3 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
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 | $0.00027 | $0.01736 |
| Opus 5 | $0.00014 | $0.00868 |
| Sonnet 5 | $0.00005 | $0.00347 |
| Haiku 4.5 | $0.00003 | $0.00174 |
Grade A, and why
warehouse-sql 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 4d 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.
How it starts
The opening of the file, as written. The whole thing — 181 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Warehouse SQL
Use this skill when the user asks natural-language questions of a data warehouse, analytics database, or TPC-H-style reporting dataset.
Scope
- schema introspection for SQLite eval databases and pluggable Postgres, ClickHouse, BigQuery, and Snowflake backends
- cached schema summaries with explicit refresh commands for scheduled runs
- reproducible TPC-H-style evaluation cases for generated SQL
- deterministic SQL safety review before execution
- read-only execution by default
- write detection and explicit per-skill grant checks before any mutation
Default Workflow
- Refresh or read cached schema before asking the model to draft SQL:
python3 skills/warehouse-sql/scripts/warehouse_sql.py --format json schema --backend sqlite --database ./warehouse.db - Have the model draft SQL using the cached schema, then review it before execution:
For review-only commands, passpython3 skills/warehouse-sql/scripts/warehouse_sql.py --format json review "SELECT c_name FROM customer LIMIT 10"--model-reviewwith the original question to invoke HybridClaw's OpenAI-compatible gateway for the business-meaning review:python3 skills/warehouse-sql/scripts/warehouse_sql.py --format json review --model-review --question "Show the first 10 customers" "SELECT c_name FROM customer LIMIT 10" - Return the SQL to the user before execution when the user asks for review, when the query is broad, or when the result could expose sensitive business data.
- Execute only after the SQL review passes and include the original question:
python3 skills/warehouse-sql/scripts/warehouse_sql.py --format json query --backend sqlite --database ./warehouse.db --execute --question "Show the first 10 customers" "SELECT c_name FROM customer LIMIT 10"query --executealways invokes model review before running SQL. Configure model review withHYBRIDCLAW_GATEWAY_URL/GATEWAY_BASE_URLandHYBRIDCLAW_WAREHOUSE_SQL_MODEL_REVIEW_TOKEN,HYBRIDCLAW_GATEWAY_TOKEN, orGATEWAY_API_TOKEN. Execution requires--questionso the model can check whether the SQL answers the user's request.
What ships with it
5 files 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.
- 4d ago First seen · 181 lines · 27 tokens per session scan A 6f4b27c85b6b
warehouse-sql is a skill published in the GitHub repository HybridAIOne/hybridclaw (132 stars, last pushed yesterday), licensed MIT. It adds 27 tokens to every session and 1,736 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 2026-09-03.
Other skills, from other repositories
vector-database-engineer
Skill "vector-database-engineer" from frank-luongt/faos-skills-marketplace, covering vector database engineer, do not use this skill when, instructions, capabilities and use this skill when.
AgentDB Performance Optimization
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
AgentDB Vector Search
Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.
chroma
Embedding database for RAG and semantic search.
pinecone
Managed vector DB for production RAG and search.
ontology-core
A command-line tool for managing and querying Business Knowledge Networks, data sources, data views, and platform monitoring resources.