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 atretyak1985/swarmery --skill automationgit clone --depth 1 https://github.com/atretyak1985/swarmeryWrote 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/atretyak1985/swarmery/automation)<a href="https://agentmods.dev/skills/atretyak1985/swarmery/automation"><img src="https://agentmods.dev/badge/skills/atretyak1985/swarmery/automation/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/atretyak1985/swarmery/automation"><img src="https://agentmods.dev/badge/skills/atretyak1985/swarmery/automation.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 | $0.00047 | $0.00830 |
| Opus 5.5 | $0.00019 | $0.00332 |
| Sonnet 5.5 | $0.00009 | $0.00166 |
| Haiku 4.5 | $0.00005 | $0.00083 |
Grade A, and why
automation 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 1mo 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 — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Convert manual runbooks into parameterized, idempotent scripts with safety gates, optionally graduating them to self-healing controllers, and design chaos experiments against the project's cloud infrastructure (.claude/project.json -> cloud.*) with mandatory environment guards. Output is executable shell or Python automation with dry-run, confirmation, and rollback. Examples use Kubernetes/kubectl — adapt to cloud.runtime.
Rules (never violate)
- Zero hardcoded namespaces, deployment names, hostnames, or credentials — everything is a parameter or environment variable.
- Every script:
set -euo pipefail(or equivalent), a--dry-runflag skipping all destructive operations, timestamped logging, and a rollback per destructive step. - Chaos experiments require
ALLOW_CHAOS=trueand reject any namespace containing "prod"; never against production without explicit written confirmation. - Scripts go to
devops/scripts/<runbook-name>.sh(or.py) via the Write tool — never echoed through Bash — and are PR-reviewed before cluster execution. - STOP and ask on ambiguous runbooks, PVC/StatefulSet data deletion, or restart-loop risk; refuse credential rotation without a secrets manager.
Resources
- Read
resources/script-requirements.mdwhen producing a script — procedure, safety rules, self-check, common mistakes, escalation, failure modes, toil measurement. - Read
resources/example-scripts.mdfor full reference scripts — a gated restart runbook and a guarded chaos pod-kill.
How to use
What it does
Turns a manual operations runbook — restart a deployment, flush a cache, scale a service — into a parameterized, idempotent script reviewable in a pull request before anyone runs it against a cluster. It can also produce a guarded chaos experiment. Every script takes its targets as parameters, supports --dry-run, logs with timestamps, and carries a rollback.
When to use it
- A manual runbook runs more than twice a week with stable steps.
- The task restarts pods, flushes caches, scales deployments, or rotates secrets.
- You want a guarded chaos experiment, or a toil-reduction analysis of an operational area.
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.
- 1mo ago First seen · 66 lines · 47 tokens per session scan A 977b55b15037
automation is a skill published in the GitHub repository atretyak1985/swarmery (7 stars, last pushed today), licensed Apache-2.0. It adds 47 tokens to every session and 830 once invoked, about $0.0002 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 2026-09-03.
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