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 skills/g1joshi/agent-skills/numpynpx skills add G1Joshi/Agent-Skills --skill numpygit clone --depth 1 https://github.com/G1Joshi/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/g1joshi/agent-skills/numpy)<a href="https://agentmods.dev/skills/g1joshi/agent-skills/numpy"><img src="https://agentmods.dev/badge/skills/g1joshi/agent-skills/numpy.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 | $0.00014 | $0.00250 |
| Opus 5 | $0.00007 | $0.00125 |
| Sonnet 5 | $0.00003 | $0.00050 |
| Haiku 4.5 | $0.00001 | $0.00025 |
Grade A, and why
numpy 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 3d 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
NumPy
NumPy is the bedrock of the Python ecosystem. v2.0 (2024) brought the first major ABI change in 15 years, improving performance and API consistency.
When to Use
- Linear Algebra: Matrix multiplication, eigenvalues.
- Array Manipulation: Reshaping, broadcasting.
- Foundation: When building libraries (like PyTorch or Pandas).
Core Concepts
Broadcasting
The magic rule that allows array(3x1) + array(3) to work.
Dtypes
Precision matters. float32 vs float64.
Stride Tricks
Efficient memory views without copying data.
Best Practices (2025)
Do:
- Check v2.0 compat: Many old libraries broke with NumPy 2.0.
- Use
numpy.strings: New string kernels in v2.0 are much faster.
Don't:
- Don't write
forloops: Always vectorize operations.
References
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.
- 3d ago First seen · 44 lines · 14 tokens per session scan A dc7eb1db3275
numpy is a skill published in the GitHub repository G1Joshi/Agent-Skills (12 stars, last pushed 6mo ago), licensed MIT. It adds 14 tokens to every session and 250 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 2026-08-30.
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