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/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/rules/stbenjam/skillsaw/development)<a href="https://agentmods.dev/rules/stbenjam/skillsaw/development"><img src="https://agentmods.dev/badge/rules/stbenjam/skillsaw/development/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/rules/stbenjam/skillsaw/development"><img src="https://agentmods.dev/badge/rules/stbenjam/skillsaw/development.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 | $0.00010 | $0.04959 |
| Opus 5.5 | $0.00004 | $0.01984 |
| Sonnet 5.5 | $0.00002 | $0.00992 |
| Haiku 4.5 | $0.00001 | $0.00496 |
Grade A, and why
development 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 20d 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 — 334 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Development rules for skillsaw.
Pre-push Checklist
CRITICAL: You MUST always run these steps before pushing changes.
- Ensure branch is up to date with UPSTREAM (stbenjam/skillsaw) — check git remotes then merge upstream's main.
- Run
make test— run the full test suite and ensure all tests pass. Never assume a problem is pre-existing; follow the boyscout rule and fix it anyway. - Run
make lint— check formatting (or runmake formatto fix it). - Run
make update— regenerate all generated files (must come after version bump). - Test against
openshift-eng/ai-helpers: clone it, runskillsaw, and ensure exit 0.
Pre-PR Checklist
Follow these checks before opening a PR:
- Check whether documentation is up to date for the changes on this branch.
- Example: you add a new feature, flag, or lint type.
Action: update
README.mdto include this. - Example: you encounter a problem during development you must keep for later.
Action: update
.apm/instructionsand runmake update.
- Example: you add a new feature, flag, or lint type.
Action: update
- Verify test coverage on a new feature, flag, lint type or bug fix is complete.
- Bug fixes must include regression protection.
- New features, linters, and rules need integration test coverage WITH fixtures (see testing rules).
Authoring Agentic Context
When you create or edit a skill, slash command, agent, hook, or instruction
file in this repo, load the skillsaw-lint skill and follow it: lint the
file with .venv/bin/skillsaw lint <path>, apply fixes, and re-lint until
clean before you finish. We ship the linter — our own context must pass it.
Post-PR Checklist
After opening a PR, keep monitoring for feedback from CodeRabbit, Gemini, and stbenjam. You may stop monitoring 20 minutes after you push a PR. Review and handle valid feedback that comes in.
Performance
Benchmark lint speed with the harness in benchmarks/ (make benchmark, make profile, make benchmark-save / make benchmark-compare
for regression checks — review DEVELOPMENT.md). When touching content rules,
the lint tree, or utils.py read paths, save a baseline on main and compare on your branch.
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.
- 20d ago Changed · +1 lines fcc9f213d317
- 27d ago Changed · +2 lines 77073dcc9e60
- 1mo ago Changed · +64 lines 421f1a8c489f
- 1mo ago First seen · 267 lines · 10 tokens per session scan A 645853d35bb8
development is a cursor rule published in the GitHub repository stbenjam/skillsaw (67 stars, last pushed today), licensed Apache-2.0. It adds 10 tokens to every session and 4,959 once invoked, about $0.0000 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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dev-standard
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