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/pledgeandgrow/pledge-skills/numpynpx skills add pledgeandgrow/pledge-skills --skill numpygit clone --depth 1 https://github.com/pledgeandgrow/pledge-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/pledgeandgrow/pledge-skills/numpy)<a href="https://agentmods.dev/skills/pledgeandgrow/pledge-skills/numpy"><img src="https://agentmods.dev/badge/skills/pledgeandgrow/pledge-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 5.1 | $0.00035 | $0.01991 |
| Opus 5 | $0.00017 | $0.00996 |
| Sonnet 5 | $0.00007 | $0.00398 |
| Haiku 4.5 | $0.00003 | $0.00199 |
Grade A, and why
numpy-docs 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 6d 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 — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
NumPy
NumPy is the fundamental package for scientific computing in Python, providing a powerful N-dimensional array object, broadcasting functions, and tools for integrating C/C++ and Fortran code.
Version: NumPy 2.5 (current)
Documentation: numpy.org/doc/stable/user/index.html
GitHub: github.com/numpy/numpy
Quick Reference
| Topic | File |
|---|---|
| Getting Started & Quickstart (What is NumPy, installation, array creation, printing, arithmetic, matrix product, in-place ops, upcasting, unary operations, universal functions, indexing/slicing/iterating, shape manipulation, stacking/splitting, copies and views, advanced indexing: integer/boolean, ix_, linear algebra, histograms, tips & tricks) | getting-started.md |
| Fundamentals (Array creation: 6 mechanisms, intrinsic functions, from disk, raw bytes, library functions; Data types: all numerical types, dtype specification, type conversion, array scalars; Indexing: basic, slicing, ellipsis, newaxis, advanced integer/boolean; Broadcasting: rules, examples, incompatible shapes; Copies and views: views vs copies, key operations table; I/O: genfromtxt, loadtxt, save/load, raw binary, standard formats; Strings and bytes: fixed-width, variable-width StringDType; Structured arrays: field access, datatype creation, subarrays; Universal functions: ufunc methods, type casting, custom ufuncs, error handling, buffer size) | fundamentals.md |
| Advanced (Interoperability: buffer protocol, array, array_ufunc, array_function, DLPack; Performant code: multiprocessing, ProcessPoolExecutor, shared memory, Numba, Cython, Dask; NumPy for MATLAB users: key differences, common equivalents table; How-tos: file I/O, indexing, regularly-spaced values, printing, verifying bugs; C-API: extending NumPy, Python as glue, F2PY, custom ufuncs in C; Under-the-hood: internal organization, strides, C code explanations, memory alignment, byte-swapping, custom array containers, subclassing ndarray; NumPy 2.0 migration: Ruff NPY201, promotion rules NEP 50, default integer, copy keyword, removed functions, C API changes; Glossary: all key terms) | advanced.md |
What ships with it
3 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.
- 6d ago First seen · 108 lines · 35 tokens per session scan A a56b50aad89d
numpy-docs is a skill published in the GitHub repository pledgeandgrow/pledge-skills (10 stars, last pushed 1mo ago), licensed MIT. It adds 35 tokens to every session and 1,991 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 2026-08-31.
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