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/sherpa-sh/Sherpa-ActionWrote 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/commands/sherpa-sh/sherpa-action/build)<a href="https://agentmods.dev/commands/sherpa-sh/sherpa-action/build"><img src="https://agentmods.dev/badge/commands/sherpa-sh/sherpa-action/build/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/commands/sherpa-sh/sherpa-action/build"><img src="https://agentmods.dev/badge/commands/sherpa-sh/sherpa-action/build.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 | $0.00012 | $0.00109 |
| Opus 5 | $0.00006 | $0.00055 |
| Sonnet 5 | $0.00002 | $0.00022 |
| Haiku 4.5 | $0.00001 | $0.00011 |
Grade A, and why
build 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.
What it actually says
If (.sherpa.sh/build-info.md doesn't exist) { run the plugin command /Sherpa/detect-build-info }
Else {
Use the info in .sherpa.sh/build-info.md and the lastest file in .sherpa.sh/executionlog to build the project.
Examine the build commands in the executionlog. Decide if you should just repeat them. Usually, you want to repeat them.
}
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 · 9 lines · 12 tokens per session scan A 1d9c39dc5f83
build is a command published in the GitHub repository sherpa-sh/Sherpa-Action (22 stars, last pushed 7mo ago), licensed Apache-2.0. It adds 12 tokens to every session and 109 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.
Other commands, from other repositories
gh-security-review
Use to perform a dedicated security review of current branch changes before pushing.
gh-release
Create a new release for the marketplace or a specific plugin.
scan
Scan AWS account for cost optimization.
gh-setup
Use to set up or update gh-workflow in a repository - analyzes tech stack, detects conventions, generates workflow configuration, and upgrades existing installations to latest version.
langgraph-checkpoints
LangGraph checkpointing and persistence. Use when implementing fault-tolerant workflows, resuming interrupted executions, or debugging with state history. Triggers on LangGraph checkpoint, persistence, fault tolerance, resume workflow, state history, checkpointer, thread state.
ollama-local
Local LLM inference with Ollama. Use when setting up local models for development, running models in CI pipelines, or reducing inference cost. Triggers on Ollama, local LLM, local inference, offline model, self-hosted model, LangChain Ollama, model quantization.