DevOps AI Guidelines is a learning and guidance repository for using artificial intelligence in DevOps, including paths toward AI infrastructure architecture and enterprise adoption. It is for DevOps engineers, teams introducing AI, and technical leaders planning AI-enabled operations. The catalogue instruction provides an agent-facing part of these guidelines.
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/VersusControl/devops-ai-guidelinesWrote 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/versuscontrol/devops-ai-guidelines/scale-workload)<a href="https://agentmods.dev/commands/versuscontrol/devops-ai-guidelines/scale-workload"><img src="https://agentmods.dev/badge/commands/versuscontrol/devops-ai-guidelines/scale-workload/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/versuscontrol/devops-ai-guidelines/scale-workload"><img src="https://agentmods.dev/badge/commands/versuscontrol/devops-ai-guidelines/scale-workload.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 | $0.00008 | $0.00185 |
| Opus 5.5 | $0.00003 | $0.00074 |
| Sonnet 5 | $0.00002 | $0.00037 |
| Haiku 4.5 | $0.00001 | $0.00018 |
Grade A, and why
scale-workload 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 11d 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
Scale deployment ${input:deployment:deployment name} in namespace ${input:namespace:namespace} to ${input:replicas:target replicas} replicas.
- Call
describe_resourcefor the deployment and report current desired / ready replicas. - If the change increases replicas by more than 4x, warn the user.
- If the target is
0, warn that the workload will be unavailable. - Wait for an explicit "confirm" reply before calling
scale_deploymentwithconfirm: true. - After the call, poll
list_podsevery 10s (max 6 polls) and report the ready/desired count until they match.
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.
- 11d ago First seen · 19 lines · 8 tokens per session scan A dffa3591be94
scale-workload is a command published in the GitHub repository VersusControl/devops-ai-guidelines (1,527 stars, last pushed 3d ago), licensed MIT. It adds 8 tokens to every session and 185 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 2026-09-17.
Other commands, from other repositories
audit-plugin
Audit plugin skills, commands, and agents for structure, size, and naming issues.
tldr
Re-apply TLDR rules for this turn (verdict first, no filler).
scan
Scan AWS account for cost optimization.
lfe-plan-critique
Run a 5-lens pre-build critique of the approved active plan before the Builder starts. Acts as the Architect persona, read-only on src/. Writes .plans/plancritique.md. Use immediately after Brain approves activeplan.md.
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.
archive-ledger
../../../shared/commands/archive-ledger.md.