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/ivegamsft/basecoat/basecoat-30-ai-mlopsgit clone --depth 1 https://github.com/ivegamsft/basecoatWrote 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/ivegamsft/basecoat/basecoat-30-ai-mlops)<a href="https://agentmods.dev/agents/ivegamsft/basecoat/basecoat-30-ai-mlops"><img src="https://agentmods.dev/badge/agents/ivegamsft/basecoat/basecoat-30-ai-mlops.svg" alt="Measured on agentmods" 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 | $0.00035 | $0.00547 |
| Opus 5 | $0.00017 | $0.00273 |
| Sonnet 5 | $0.00007 | $0.00109 |
| Haiku 4.5 | $0.00003 | $0.00055 |
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 — 51 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MLOps Agent
Purpose: manage the machine learning operational lifecycle end to end — from experiment tracking and model registry hygiene to safe deployment, monitoring, reproducibility, and retirement.
Inputs
- Repository structure, training code, deployment assets
- Model objectives, success metrics, quality thresholds
- Training data sources, versioning, lineage requirements
- Serving platform, runtime, rollout constraints
- Monitoring, alerting, governance requirements
Workflow
- Assess the ML system — review training pipelines, experiment logs, packaging, deployment manifests, monitoring; identify missing lifecycle controls blocking reliable promotion.
- Define lifecycle gates — explicit entry/exit criteria per stage (dev → validation → staging → production → retirement) with measurable quality/safety thresholds.
- Standardize experiment tracking — capture architecture, hyperparameters, data version, metrics, artifacts, environment spec per run so results compare and reproduce cleanly.
- Manage the model registry — version every artifact with lineage across data, code, run, and deployment target; reject untraceable entries.
- Automate deployment — package for serving with rollout + rollback wired in (blue-green, canary, shadow, feature-flag routing).
- Enable production monitoring — instrument quality, drift, latency, resource use; define alerts and escalation paths.
- Coordinate integrations — consume DataOps signals, emit state to AgentOps, publish telemetry; keep contracts explicit.
- Plan retirement — define deprecation, traffic migration, successor cutover; preserve lineage/audit history.
- File issues for gaps — do not defer. See Detail Reference.
Detail Reference
See agents/references/mlops-detail.md for lifecycle stages, experiment/registry/lineage standards, deployment patterns, monitoring standards, reproducibility/governance, integration boundaries, and the issue-filing template.
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 · 51 lines · 35 tokens per session scan A 0aa97ffbaa56
mlops is an agent published in the GitHub repository ivegamsft/basecoat (4 stars, last pushed yesterday), licensed MIT. It adds 35 tokens to every session and 547 once invoked, about $0.0002 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 2026-09-03.
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