Borrowing it
Nothing to install: this file belongs to stbenjam/skillsaw. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/stbenjam/skillsaw/main/.agents/skills/skillsaw-create-plugin/SKILL.mdgit clone --depth 1 https://github.com/stbenjam/skillsawWrote 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/stbenjam/skillsaw/skillsaw-create-plugin)<a href="https://agentmods.dev/skills/stbenjam/skillsaw/skillsaw-create-plugin"><img src="https://agentmods.dev/badge/skills/stbenjam/skillsaw/skillsaw-create-plugin/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/stbenjam/skillsaw/skillsaw-create-plugin"><img src="https://agentmods.dev/badge/skills/stbenjam/skillsaw/skillsaw-create-plugin.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00068 | $0.01994 |
| Opus 5 | $0.00034 | $0.00997 |
| Sonnet 5 | $0.00014 | $0.00399 |
| Haiku 4.5 | $0.00007 | $0.00199 |
Grade A, and why
skillsaw-create-plugin 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 10d 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 — 195 lines — stays where its author put it; the contents beside it link to each section on GitHub.
skillsaw Create Plugin
Create a skillsaw rule plugin: a Python package that adds lint
rules to skillsaw. Once you build and publish to PyPI, anyone can
pip install it and skillsaw picks up the rules automatically — no config
required. Prefer this to per-repo custom-rules: files when sharing rules across repos.
Keep the terminology straight: a skillsaw plugin adds rules to the skillsaw linter itself. Never confuse it with the Claude Code plugins that skillsaw lints.
Review this reference material while you work:
- Read the plugin guide: https://skillsaw.org/plugins/
- Follow the rule-writing guide (lint tree, block types): https://skillsaw.org/custom-rules/ and https://skillsaw.org/lint-tree/
- Read the complete working example:
examples/plugins/skillsaw-example-plugin/in the skillsaw repo
Follow each step in order, and keep the user updated on progress at each stage.
Step 1: Gather requirements
Review the following with the user:
- What should each rule check? Get concrete examples of content that should pass and content that should fail.
- Rule IDs — use kebab-case, descriptive (e.g.
no-todo-instructions). Runskillsaw list-rulesand confirm no ID collides with a builtin rule or another installed plugin; skillsaw never loads colliding plugin rules. - Severity — set each rule to
error(must fix),warning(should fix), orinfo(advisory). - Package name — follow the convention
skillsaw-<name>on PyPI with moduleskillsaw_<name>(e.g.skillsaw-acme-rules/skillsaw_acme_rules). - Tunable settings — set anything a user could reasonably want to adjust
(patterns, thresholds, allowlists) in
config_schema; never hardcode it.
Step 2: Create the package layout
Create this layout:
skillsaw-<name>/
├── pyproject.toml
├── README.md
├── src/
│ └── skillsaw_<name>/
│ ├── __init__.py
│ └── rules.py
└── tests/
├── fixture/
│ └── CLAUDE.md # realistic fixture the rules run against
└── test_rules.py
What ships with it
2 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.
- 10d ago First seen · 195 lines · 68 tokens per session scan A acf43e9c135b
skillsaw-create-plugin is a skill published in the GitHub repository stbenjam/skillsaw (63 stars, last pushed yesterday), licensed Apache-2.0. It adds 68 tokens to every session and 1,994 once invoked, about $0.0003 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.
Other skills, from other repositories
matlab
Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.
pennylane
Hardware-agnostic quantum ML framework with automatic differentiation. Use when training quantum circuits via gradients, building hybrid quantum-classical models, or needing device portability across IBM/Google/Rigetti/IonQ. Best for variational algorithms (VQE, QAOA), quantum neural networks, and integration with…
dd-code-generation
Use pup CLI for immediate Datadog operations or generate code for integration into applications.
rocm-kernels
Provides guidance for writing and benchmarking optimized Triton kernels for AMD GPUs (MI355X, R9700) on ROCm, targeting HuggingFace diffusers (LTX-Video, SD3, FLUX) and transformers. Core kernels: RMSNorm, RoPE 3D, GEGLU, AdaLN. Includes XCD swizzle, autotune, diffusers integration patterns, and LTX-Video pipeline…
holoscan-install-wheel
Install Holoscan SDK Python wheel via pip into a venv. Use for Python installs; not for native C++/apt or Conda installs.
typing-exclusion-worker
Python typing exclusion worker: remove assigned mypy exclusion modules in small scoped batches, fix typing issues, run validation, and produce a structured completion summary. Use when running parallel typing-debt workers or when asked to remove modules from pyproject mypy exclusion overrides.