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 vaquarkhan/data-engineering-agent-skills --skill duckdb-local-analytics-and-devgit clone --depth 1 https://github.com/vaquarkhan/data-engineering-agent-skillsWrote 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/vaquarkhan/data-engineering-agent-skills/duckdb-local-analytics-and-dev)<a href="https://agentmods.dev/skills/vaquarkhan/data-engineering-agent-skills/duckdb-local-analytics-and-dev"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/duckdb-local-analytics-and-dev/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/vaquarkhan/data-engineering-agent-skills/duckdb-local-analytics-and-dev"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/duckdb-local-analytics-and-dev.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00047 | $0.01100 |
| Opus 5 | $0.00023 | $0.00550 |
| Sonnet 5 | $0.00009 | $0.00220 |
| Haiku 4.5 | $0.00005 | $0.00110 |
Grade A, and why
duckdb-local-analytics-and-dev 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 9d 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 — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DuckDB Local Analytics And Dev
Overview
Use this skill when DuckDB is the fastest path to local analytical iteration. It helps agents build reproducible local workflows that accelerate development without confusing prototype convenience for production architecture.
When to Use
- prototyping data models and transformations locally before deploying to a warehouse
- reproducing production data issues with sample datasets
- running analytical queries during development without remote infrastructure
- building lightweight CLI tools, validators, or test harnesses
- validating dbt models locally with
dbt-duckdbadapter - creating proof-of-concept demonstrations with embedded analytics
Do not use this when the workload requires production durability, concurrent access, or distributed processing. DuckDB is a development and prototyping accelerator, not a production warehouse replacement.
Workflow
-
Define the purpose and scope of the local workflow. Include:
- what question or validation is this workflow answering?
- what sample data is needed and where does it come from?
- is this a one-time investigation or a repeatable development workflow?
- what is the promotion path to production if the prototype succeeds?
-
Set up reproducible data inputs.
- use sample files (CSV, Parquet, JSON) checked into the repository or downloaded by script
- document how sample data was generated or extracted
- keep sample sizes representative but small enough for fast iteration
- use DuckDB's ability to read Parquet, CSV, and JSON directly without import steps
- for sensitive data: use anonymized or synthetic samples only
-
Write transformations that map cleanly to production equivalents.
- use standard SQL that translates to the target warehouse dialect
- avoid DuckDB-specific functions unless the workflow stays local permanently
- structure queries in the same layered pattern (staging → intermediate → marts) as production
- when using
dbt-duckdb: use the same model structure and tests as the production adapter - document which DuckDB-specific features would need replacement in production
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.
- 9d ago First seen · 95 lines · 47 tokens per session scan A 993aa550454e
duckdb-local-analytics-and-dev is a skill published in the GitHub repository vaquarkhan/data-engineering-agent-skills (43 stars, last pushed 2mo ago), licensed MIT. It adds 47 tokens to every session and 1,100 once invoked, about $0.0002 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 2026-08-30.
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