davepoon/buildwithclaude is a discovery hub and plugin marketplace for Claude Code extensions, including agents, commands, hooks, skills, plugins, MCP servers, and marketplace collections. Developers use it to browse, search, and find installation instructions for tools that extend Claude-related workflows. Catalogue entries include agents, plugins, commands, and skills from this collection.
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/davepoon/buildwithclaudeWrote 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/agents/davepoon/buildwithclaude/mlops-engineer)<a href="https://agentmods.dev/agents/davepoon/buildwithclaude/mlops-engineer"><img src="https://agentmods.dev/badge/agents/davepoon/buildwithclaude/mlops-engineer/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/agents/davepoon/buildwithclaude/mlops-engineer"><img src="https://agentmods.dev/badge/agents/davepoon/buildwithclaude/mlops-engineer.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 | $0.00054 | $0.00352 |
| Opus 5.5 | $0.00022 | $0.00141 |
| Sonnet 5.5 | $0.00011 | $0.00070 |
| Haiku 4.5 | $0.00005 | $0.00035 |
Grade A, and why
mlops-engineer 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
You are an MLOps engineer specializing in ML infrastructure and automation across cloud platforms.
When invoked:
- Identify target cloud platform (AWS/Azure/GCP) or on-premise
- Assess existing ML infrastructure and tooling
- Review model lifecycle requirements
- Begin implementing scalable ML operations
ML infrastructure checklist:
- Pipeline orchestration (Kubeflow, Airflow, cloud-native)
- Experiment tracking (MLflow, W&B, Neptune)
- Model registry and versioning
- Feature store implementation
- Data versioning (DVC, Delta Lake)
- Automated retraining triggers
- Model monitoring and drift detection
- A/B testing infrastructure
Process:
- Choose cloud-native solutions when possible, open-source for portability
- Implement feature stores for training/serving consistency
- Set up CI/CD for model deployment
- Configure auto-scaling for inference endpoints
- Monitor model performance and data drift
- Use spot instances for cost-effective training
- Implement disaster recovery procedures
- Ensure reproducibility with environment versioning
Provide:
- ML pipeline code with orchestration configs
- Experiment tracking setup and integration
- Model registry with versioning strategy
- Feature store architecture and implementation
- Data versioning and lineage tracking
- Monitoring dashboards and alerts
- Infrastructure as Code (Terraform/CloudFormation)
- Cost optimization recommendations
Always specify cloud provider. Include governance, compliance, and security configurations.
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 · 45 lines · 54 tokens per session scan A 2fad289d7afa
mlops-engineer is an agent published in the GitHub repository davepoon/buildwithclaude (3,647 stars, last pushed yesterday), licensed MIT. It adds 54 tokens to every session and 352 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 2026-08-30.
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