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 skills add caoyuan-fire/engi-foundry-skill --skill engifoundry-execgit clone --depth 1 https://github.com/caoyuan-fire/engi-foundry-skillWrote 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/caoyuan-fire/engi-foundry-skill/engifoundry-exec)<a href="https://agentmods.dev/skills/caoyuan-fire/engi-foundry-skill/engifoundry-exec"><img src="https://agentmods.dev/badge/skills/caoyuan-fire/engi-foundry-skill/engifoundry-exec/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/caoyuan-fire/engi-foundry-skill/engifoundry-exec"><img src="https://agentmods.dev/badge/skills/caoyuan-fire/engi-foundry-skill/engifoundry-exec.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 | $0.00061 | $0.02112 |
| Opus 5 | $0.00030 | $0.01056 |
| Sonnet 5 | $0.00012 | $0.00422 |
| Haiku 4.5 | $0.00006 | $0.00211 |
Grade A, and why
engifoundry-exec 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 9d 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 — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
EngiFoundry Exec
Execution Selection
This section applies only to the controlling Agent arranging execution. A session that received engifoundry.executor-task/v1 is already the Executor Worker; the protocol is a terminal routing fact. It skips Executor selection and invocation, must not compare or infer its model identity, executes only the referenced Job, and returns one engifoundry.executor-handback/v1 object. It must never invoke or select another Executor.
The controlling Agent reads ./engifoundry.config.json, the project-owned workspace guide, the complete Executor and Workflow config files, contracts.md, and executor-task.template.json in full. Follow the Executor config's schemaRef and read that complete schema before selecting or invoking an Executor. Do not extract only executor, command, model, or usage; the surrounding fields and every declared Gate remain binding. Then read the Phase, PAK, and Job contracts before acting.
Compare only the active model with the configured Executor model; CLI identity and executorId do not decide execution ownership. When the configuration pins the same canonical model as the active model, the controlling Agent directly executes eligible Jobs in the current session without invoking an Executor Worker or producing a Worker handback. This is normal configured execution, not fallback or self-approval. The controlling Agent still applies all Exec discipline, evidence, Review, approval, and completion rules.
When the pinned models differ, invoke the configured Executor through its verified CLI usage, including the configured canonical model argument. A cli-default selection makes no model identity promise and therefore has no same-model fast path; invoke its configured CLI. Once the invocation accepts the task, model selection is settled by the controlling session and the receiving Worker executes it. Natural-language model self-identification is not trusted evidence: a model may know only its family name rather than the configured product alias.
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.
- 9d ago First seen · 70 lines · 61 tokens per session scan A 928c0e47a904
engifoundry-exec is a skill published in the GitHub repository caoyuan-fire/engi-foundry-skill (2 stars, last pushed 15d ago), licensed Apache-2.0. It adds 61 tokens to every session and 2,112 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 2026-08-31.
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