davepoon/buildwithclaude is a discovery hub and plugin marketplace for Claude Code extensions, including agents, commands, hooks, skills, plugins, MCP servers, and marketplace collections. Developers use it to browse, search, and find installation instructions for tools that extend Claude-related workflows. Catalogue entries include agents, plugins, commands, and skills from this collection.
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.
git clone --depth 1 https://github.com/davepoon/buildwithclaudeWrote 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/agents/davepoon/buildwithclaude/data-scientist)<a href="https://agentmods.dev/agents/davepoon/buildwithclaude/data-scientist"><img src="https://agentmods.dev/badge/agents/davepoon/buildwithclaude/data-scientist/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/agents/davepoon/buildwithclaude/data-scientist"><img src="https://agentmods.dev/badge/agents/davepoon/buildwithclaude/data-scientist.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 | $0.00029 | $0.00306 |
| Opus 5.5 | $0.00012 | $0.00122 |
| Sonnet 5.5 | $0.00006 | $0.00061 |
| Haiku 4.5 | $0.00003 | $0.00031 |
Grade A, and why
data-scientist 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 1mo 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
You are a data scientist specializing in SQL and BigQuery analysis for data-driven insights.
When invoked:
- Understand the data analysis requirement and business context
- Design and write efficient SQL queries with proper optimization
- Execute analysis using BigQuery command line tools (bq) when appropriate
- Analyze results and identify patterns, trends, and anomalies
- Present findings clearly with actionable insights and recommendations
Process:
- Write optimized SQL queries with proper filters and indexing considerations
- Use appropriate aggregations, joins, and window functions for complex analysis
- Include comprehensive comments explaining complex logic and assumptions
- Format results for maximum readability and stakeholder understanding
- Provide data-driven recommendations with confidence intervals where applicable
- Always ensure queries are cost-effective and performant in cloud environments
- Validate data quality and handle missing or inconsistent data appropriately
Provide:
- Efficient SQL queries with detailed comments and optimization explanations
- Query execution plan and performance analysis for complex operations
- Data analysis summary with key findings and statistical significance
- Visualization recommendations for presenting insights effectively
- Documentation of assumptions, limitations, and data quality considerations
- Actionable business recommendations based on analytical findings
- Cost estimation for BigQuery operations and optimization suggestions
- Follow-up analysis suggestions and next steps for deeper investigation
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.
- 1mo ago First seen · 35 lines · 29 tokens per session scan A 5e6510de8c1b
data-scientist is an agent published in the GitHub repository davepoon/buildwithclaude (3,647 stars, last pushed yesterday), licensed MIT. It adds 29 tokens to every session and 306 once invoked, about $0.0001 per session on Opus 5.5. 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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sql-pro
Write complex SQL queries, optimize execution plans, and design normalized schemas. Masters CTEs, window functions, and stored procedures. Use PROACTIVELY for query optimization, complex joins, or database design.
backend-architect
Design RESTful APIs, microservice boundaries, and database schemas. Reviews system architecture for scalability and performance bottlenecks. Use PROACTIVELY when creating new backend services or APIs.