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/olehsvyrydov/AI-development-teamWrote 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/olehsvyrydov/ai-development-team/mlops)<a href="https://agentmods.dev/commands/olehsvyrydov/ai-development-team/mlops"><img src="https://agentmods.dev/badge/commands/olehsvyrydov/ai-development-team/mlops/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/olehsvyrydov/ai-development-team/mlops"><img src="https://agentmods.dev/badge/commands/olehsvyrydov/ai-development-team/mlops.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 | $0.00030 | $0.00133 |
| Opus 5.5 | $0.00012 | $0.00053 |
| Sonnet 5.5 | $0.00006 | $0.00027 |
| Haiku 4.5 | $0.00003 | $0.00013 |
Grade A, and why
mlops 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.
What it actually says
/mlops — MLOps Engineer
Invoke the mlops-engineer skill (claude/skills/operations/mlops/mlops-engineer/SKILL.md).
Model serving & inference infrastructure, ML/AI pipelines, training-data pipelines, model deployment & monitoring. Consult the workflow-engine. For app-level LLM product features (RAG, agents, prompts, evals, guardrails) use /ai; for general cloud infrastructure use /devops.
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 · 10 lines · 30 tokens per session scan A 8cb35a403eaa
mlops is a command published in the GitHub repository olehsvyrydov/AI-development-team (17 stars, last pushed today), licensed MIT. It adds 30 tokens to every session and 133 once invoked, about $0.0001 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.
Other commands, from other repositories
data-pipeline
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pre-deploy.skeleton
Description: This section is populated by Bootstrap with manifest data. Required manifest fields: project.description, stack.primary, project.structure, project.subprojects Example output.
optimize-pipeline
Optimize an existing pipeline on demand — survey topology, model-tier cost, and past-run signals, lock an optimization plan with the user, then batch-apply it atomically with a mandatory post-apply audit.
change-models
Set, change, or audit model preferences for pipeline agents — interactive 6-mode workflow (global/workspace prefs, per-agent overrides, first-run wizard, catalog refresh).
multi-execute
Multi-model collaborative execution: prototype from plan, refactor, multi-model audit, delivery.
multi-workflow
Multi-model collaborative dev workflow with intelligent routing (frontend, backend).