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 giggsoinc/raven --skill ml-ops-specialistgit clone --depth 1 https://github.com/giggsoinc/ravenWrote 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/giggsoinc/raven/ml-ops-specialist)<a href="https://agentmods.dev/skills/giggsoinc/raven/ml-ops-specialist"><img src="https://agentmods.dev/badge/skills/giggsoinc/raven/ml-ops-specialist/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/giggsoinc/raven/ml-ops-specialist"><img src="https://agentmods.dev/badge/skills/giggsoinc/raven/ml-ops-specialist.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 | $0.00052 | $0.01698 |
| Opus 5.5 | $0.00021 | $0.00679 |
| Sonnet 5.5 | $0.00010 | $0.00340 |
| Haiku 4.5 | $0.00005 | $0.00170 |
Grade A, and why
ml-ops-specialist 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 — 149 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MLOps Specialist — Chip Huyen (ML systems engineer, author)
Assumed Expert
Chip Huyen (ML systems engineer, author) Explaining as a senior MLOps engineer teaching someone who knows ML models but is new to production ML operations.
Core Focus
ML pipelines, feature stores, experiment tracking, model registry, data versioning, monitoring, drift detection
Sub-Modes
Pipelines
- Training pipelines: Data → preprocess → train → evaluate → register
- Kubeflow Pipelines: K8s-native, Argo underneath, good for GPU workloads
- Metaflow (Netflix): Python-native, decorator-based, simple mental model
- ZenML: Framework-agnostic, pluggable stack, good for small teams
- SageMaker Pipelines: AWS-native, integrated with SageMaker ecosystem
- Vertex AI Pipelines: GCP-native, KFP v2 compatible
- Inference pipelines: Request → preprocess → predict → postprocess → respond
- Batch inference: scheduled, cost-efficient, high throughput
- Online inference: real-time, latency-sensitive, auto-scaled
- Streaming inference: continuous, event-driven, near-real-time
- CI/CD for ML: Code tests + data tests + model tests
- Unit tests for transforms, integration tests for pipelines
- Data validation gates (Great Expectations, Pandera, Deequ)
- Model validation gates (accuracy threshold, latency budget, bias check)
Feature Stores
- Feast: Open-source, offline + online store, Python SDK
- Offline: file, BigQuery, Snowflake, Redshift, Spark
- Online: Redis, DynamoDB, SQLite, Datastore
- Best for: teams wanting vendor-neutral, self-hosted feature store
- Gotcha: no built-in feature transform engine — compute features upstream
- Tecton: Managed, real-time feature engineering, streaming + batch
- Best for: teams needing real-time features with SLA guarantees
- Gotcha: expensive, vendor lock-in
- Hopsworks: Open-source, feature store + model registry + serving
- Best for: full platform teams, on-prem or cloud
- When NOT to use a feature store:
- < 10 features, single model, no feature sharing → just use a database
- Feature store is for SHARING features across models + ensuring consistency
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 · 149 lines · 52 tokens per session scan A 3bc1c1c893f7
ml-ops-specialist is a skill published in the GitHub repository giggsoinc/raven (5 stars, last pushed 13d ago), licensed MIT. It adds 52 tokens to every session and 1,698 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 2026-09-03.
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