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 bcastelino/agent-skills-kit --skill ml-pipeline-workflowgit clone --depth 1 https://github.com/bcastelino/agent-skills-kitWrote 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/bcastelino/agent-skills-kit/ml-pipeline-workflow)<a href="https://agentmods.dev/skills/bcastelino/agent-skills-kit/ml-pipeline-workflow"><img src="https://agentmods.dev/badge/skills/bcastelino/agent-skills-kit/ml-pipeline-workflow/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/bcastelino/agent-skills-kit/ml-pipeline-workflow"><img src="https://agentmods.dev/badge/skills/bcastelino/agent-skills-kit/ml-pipeline-workflow.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 | $0.00044 | $0.01504 |
| Opus 5 | $0.00022 | $0.00752 |
| Sonnet 5 | $0.00009 | $0.00301 |
| Haiku 4.5 | $0.00004 | $0.00150 |
Grade A, and why
ml-pipeline-workflow 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 12d 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 — 252 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ML Pipeline Workflow
Complete end-to-end MLOps pipeline orchestration from data preparation through model deployment.
Do not use this skill when
- The task is unrelated to ml pipeline workflow
- You need a different domain or tool outside this scope
Instructions
- Clarify goals, constraints, and required inputs.
- Apply relevant best practices and validate outcomes.
- Provide actionable steps and verification.
- If detailed examples are required, open
resources/implementation-playbook.md.
Overview
This skill provides comprehensive guidance for building production ML pipelines that handle the full lifecycle: data ingestion → preparation → training → validation → deployment → monitoring.
Use this skill when
- Building new ML pipelines from scratch
- Designing workflow orchestration for ML systems
- Implementing data → model → deployment automation
- Setting up reproducible training workflows
- Creating DAG-based ML orchestration
- Integrating ML components into production systems
What This Skill Provides
Core Capabilities
-
Pipeline Architecture
- End-to-end workflow design
- DAG orchestration patterns (Airflow, Dagster, Kubeflow)
- Component dependencies and data flow
- Error handling and retry strategies
-
Data Preparation
- Data validation and quality checks
- Feature engineering pipelines
- Data versioning and lineage
- Train/validation/test splitting strategies
-
Model Training
- Training job orchestration
- Hyperparameter management
- Experiment tracking integration
- Distributed training patterns
-
Model Validation
- Validation frameworks and metrics
- A/B testing infrastructure
- Performance regression detection
- Model comparison workflows
-
Deployment Automation
- Model serving patterns
- Canary deployments
- Blue-green deployment strategies
- Rollback mechanisms
Reference Documentation
See the references/ directory for detailed guides:
- data-preparation.md - Data cleaning, validation, and feature engineering
- model-training.md - Training workflows and best practices
- model-validation.md - Validation strategies and metrics
- model-deployment.md - Deployment patterns and serving architectures
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 12d ago First seen · 252 lines · 44 tokens per session scan A 379714258850
ml-pipeline-workflow is a skill published in the GitHub repository bcastelino/agent-skills-kit (2 stars, last pushed 1mo ago), licensed MIT. It adds 44 tokens to every session and 1,504 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 2026-08-31.
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