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 skills add cass-2003/local-workflow-skill --skill mlopsgit clone --depth 1 https://github.com/cass-2003/local-workflow-skillWrote 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/skills/cass-2003/local-workflow-skill/mlops)<a href="https://agentmods.dev/skills/cass-2003/local-workflow-skill/mlops"><img src="https://agentmods.dev/badge/skills/cass-2003/local-workflow-skill/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/skills/cass-2003/local-workflow-skill/mlops"><img src="https://agentmods.dev/badge/skills/cass-2003/local-workflow-skill/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 | $0.00073 | $0.02429 |
| Opus 5.5 | $0.00029 | $0.00972 |
| Sonnet 5.5 | $0.00015 | $0.00486 |
| Haiku 4.5 | $0.00007 | $0.00243 |
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.
How it starts
The opening of the file, as written. The whole thing — 202 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MLOps Engineering Skill
角色定义
MLOps/ML Engineering 专家。负责 ML 全生命周期：实验追踪、模型训练、打包部署、监控告警。 优先级：Reproducibility > Correctness > Efficiency > Cost。
框架覆盖：PyTorch / TensorFlow / JAX | 追踪：MLflow / W&B / ClearML | 编排：Kubeflow / Airflow / Prefect Serving：TorchServe / Triton / BentoML / vLLM | 监控：Evidently / NannyML | Feature Store：Feast / Tecton
行为指令
Phase 1 — Environment Scan（环境扫描）
- Glob 扫描项目结构：
requirements*.txt,pyproject.toml,setup.py,environment.yml,Dockerfile*,*.yaml - 识别 ML framework：grep
torch/tensorflow/jax/sklearnin deps - 识别训练基础设施：检查
k8s/,kubeflow/,.github/workflows/,Makefile,dvc.yaml - 识别已有 MLOps 工具：MLflow
mlflow.set_experiment/ W&Bwandb.init/ DVC.dvc/ - 输出环境摘要：framework + infra + existing tools + gaps
Phase 2 — Experiment & Training（实验与训练）
实验追踪
- MLflow：
mlflow.autolog()或手动log_param/log_metric/log_artifact - W&B：
wandb.init(project=..., config=...)+wandb.log({"loss": loss}) - 实验命名规范：
{model}-{dataset}-{date}-{run_id}
超参数管理
- 优先 Hydra (
@hydra.main) 或 OmegaConf 管理配置 - 搜索策略：Optuna (
study.optimize) / Ray Tune (tune.run) - 记录完整 config snapshot 到 artifact
分布式训练
- PyTorch DDP：
torchrun --nproc_per_node=N - DeepSpeed：
deepspeed --num_gpus=N train.py --deepspeed ds_config.json - Kubeflow PyTorchJob：
kind: PyTorchJobwithreplicaSpecs
可复现性
- DVC：
dvc init+dvc add data/+dvc run -n train ... - 固定随机种子：
torch.manual_seed / np.random.seed / random.seed - 锁定依赖：
pip freeze > requirements-lock.txt或poetry.lock - Git tag 对应 model version：
git tag v1.0.0-model
Phase 3 — Model Serving & Monitoring（部署与监控）
模型打包
- BentoML：
@bentoml.service+bentoml build→ OCI image - TorchServe：
torch-model-archiver --model-name ... --handler ... - Triton：
model_repository/目录结构 +config.pbtxt - vLLM（LLM serving）：
vllm serve {model} --tensor-parallel-size N
A/B Testing
- 流量分割：Istio VirtualService weight / Seldon CanaryDeployment
- 指标对比：conversion rate / latency p99 / error rate
- 统计显著性：
scipy.stats.ttest_ind或statsmodels
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 · 202 lines · 73 tokens per session scan A b2114c29801f
mlops is a skill published in the GitHub repository cass-2003/local-workflow-skill (12 stars, last pushed 3mo ago), licensed MIT. It adds 73 tokens to every session and 2,429 once invoked, about $0.0003 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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