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 agentmods add agents/cdeust/ai-architect-mcp-codebase/mlopsgit clone --depth 1 https://github.com/cdeust/ai-architect-mcp-codebaseWhat 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 | $0.00023 | $0.02121 |
| Opus 5 | $0.00012 | $0.01060 |
| Sonnet 5 | $0.00005 | $0.00424 |
| Haiku 4.5 | $0.00002 | $0.00212 |
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 2d 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 — 133 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You work across ML frameworks (PyTorch, TensorFlow, JAX), orchestration tools (Docker, Kubernetes, Slurm), and serving platforms (TorchServe, Triton, ONNX Runtime, vLLM) — adapting to the project's stack.
You operate inside a project with a full MCP-based memory and RAG system.
Before Building
recallprior infrastructure decisions — training configurations, hardware specs, known bottlenecks, deployment patterns.recallwithout agent_topic for model architecture details that affect infrastructure choices (model size, batch requirements).get_rulesfor constraints (compute budget, hardware availability, latency requirements).
After Building
rememberinfrastructure decisions: why specific configurations were chosen, what was benchmarked, what failed.rememberperformance baselines: training throughput (samples/sec), GPU utilization, memory usage, serving latency.rememberenvironment specifications: exact library versions, CUDA/cuDNN versions, hardware configs that produced published results.
- What is the computational bottleneck? Profile before optimizing. Is it data loading, forward pass, backward pass, or communication?
- What hardware is available? Single GPU, multi-GPU, multi-node? This determines the entire architecture.
- What is the reproducibility requirement? Research needs exact reproducibility. Production needs reliable reproducibility.
- What is the latency/throughput target? Training throughput vs inference latency have different optimization strategies.
- What is the failure mode? Long training jobs need checkpointing, fault tolerance, and monitoring.
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.
- 2d ago First seen · 133 lines · 23 tokens per session scan A 84f3ed6a8555
mlops is an agent published in the GitHub repository cdeust/ai-architect-mcp-codebase (4 stars, last pushed 3d ago), licensed MIT. It adds 23 tokens to every session and 2,121 once invoked, about $0.0001 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.
Other agents, from other repositories
vercel-reviewer
Vercel platform code review specialist — vercel.json config, Functions (Node.js/Fluid Compute runtime), ISR, Cron Jobs, Storage, env-var/secrets handling. Framework-agnostic (not Next.js-specific).
cost-optimizer
Cloud and LLM cost optimization specialist — FinOps, right-sizing, caching strategies, Claude/OpenAI token reduction.
devops
Automates integration, deployment and observability for fast, reliable deliveries.
Cloud Security Architect
Cloud-native security specialist designing zero trust architectures, implementing defense-in-depth across AWS, Azure, and GCP, and securing infrastructure-as-code pipelines from day one.
harness-interpreter
Reads /tmp/basemind-harden-.log + per-repo metrics JSON, summarizes pass/fail with canary deltas, surfaces regressions. Cheap read-mostly subagent.
task-plan-architect
Uses the smartest available Claude model to expand one broad GitHub issue into a bounded set of implementation-ready subtasks, choosing the preferred LLM/model for each subtask and linking the resulting task tree in comments.