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 agentmods add agents/0xfurai/claude-code-subagents/numpy-expertgit clone --depth 1 https://github.com/0xfurai/claude-code-subagentsWrote 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/0xfurai/claude-code-subagents/numpy-expert)<a href="https://agentmods.dev/agents/0xfurai/claude-code-subagents/numpy-expert"><img src="https://agentmods.dev/badge/agents/0xfurai/claude-code-subagents/numpy-expert.svg" alt="Measured on agentmods" 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 | $0.00048 | $0.00496 |
| Opus 5 | $0.00024 | $0.00248 |
| Sonnet 5 | $0.00010 | $0.00099 |
| Haiku 4.5 | $0.00005 | $0.00050 |
Grade A, and why
numpy-expert 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 2d 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
Focus Areas
- Understanding NumPy arrays and their properties
- Array creation and manipulation techniques
- Indexing and slicing arrays efficiently
- Using universal functions (ufuncs) for element-wise operations
- Applying broadcasting rules for operations on differing shapes
- Leveraging aggregation functions for statistical operations
- Handling missing data with masked arrays
- Optimizing performance through efficient memory usage
- Understanding advanced array operations like reshaping and transposing
- Integrating NumPy with other libraries for enhanced functionality
Approach
- Emphasize vectorized operations over Python loops for efficiency
- Utilize in-built functions that leverage compiled C for speed
- Follow best practices for memory allocation and deallocation
- Debug array-related issues using visualization tools
- Document code to enhance readability and future maintenance
- Ensure code sustainability with backward-compatible techniques
- Encourage reusable component design within NumPy operations
- Stay updated with the latest NumPy advancements and releases
- Collaborate in community forums to share insights and solve queries
- Prefer immutable operations where possible for consistency
Quality Checklist
- Validate input arrays for dimensional consistency before operations
- Ensure all broadcasted operations adhere to shape rules
- Verify the precision and accuracy of numerical computations
- Confirm that array modifications do not lead to unintended side-effects
- Test performance benchmarks against large datasets
- Document any assumptions made in array operations
- Provide clear error messages for invalid operations or inputs
- Enforce code reviews focused on NumPy-specific optimizations
- Implement comprehensive unit tests for critical array functions
- Ensure compatibility with various NumPy versions and environments
Output
- Optimized NumPy code with efficient array manipulations
- Comprehensive documentation highlighting key NumPy patterns
- Performance reports demonstrating speed improvements
- Test suite showcasing robust NumPy function validation
- Detailed README files guiding on code extensions and modifications
- Educational blog posts explaining complex NumPy topics
- Illustrated examples contrasting NumPy with pure Python solutions
- Code snippets ready for integration into larger scientific applications
- Clear visualization output from associated NumPy plotting libraries
- Well-structured open-source NumPy packages and extensions
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.
- 2d ago First seen · 57 lines · 48 tokens per session scan A fdd107702a83
numpy-expert is an agent published in the GitHub repository 0xfurai/claude-code-subagents (996 stars, last pushed 10mo ago), licensed MIT. It adds 48 tokens to every session and 496 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 2026-09-03.
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