ml-pipeline-workflow SKILL.md skillA
ml-pipeline-workflow from dicklesworthstone/pi_agent_rust is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add dicklesworthstone/pi_agent_rust/ml-pipeline-workflow@git:20260205.788be98Pins git:20260205.788be98 in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- dicklesworthstone/pi_agent_rust
- Path
tests/ext_conformance/artifacts/agents-wshobson/machine-learning-ops/skills/ml-pipeline-workflow/SKILL.md- Versions recorded
- 1
- Latest version
- git:20260205.788be98, committed Feb 5, 2026
- Size
- 7,032 bytes
- Repository stars
- 1,966
- License
- a license file GitHub does not identify
Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment workflows.
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# ML Pipeline Workflow Complete end-to-end MLOps pipeline orchestration from data preparation through model deployment. ## Overview This skill provides comprehensive guidance for building production ML pipelines that handle the full lifecycle: data ingestion → preparation → training → validation → deployment → monitoring. ## When to Use This Skill - 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 1. **Pipeline Architecture** - End-to-end workflow design - DAG orchestration patterns (Airflow, Dagster, Kubeflow) - Component dependencies and data flow - Error handling and retry strategies 2. **Data Preparation** - Data validation and quality checks - Feature engineering pipelines - Data versioning and lineage - Train/validation/test splitting strategies 3. **Model Training** - Training job orchestration - Hyperparameter management - Experiment tracking integration …
Read the whole file at its exact version.
How to install
mdr add dicklesworthstone/pi_agent_rust/ml-pipeline-workflow@sha256:1452bab2b05e7057The line at the top of this page pins the label git:20260205.788be98, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
[](https://markdownregistry.com/a/art_hecvhki7dmtfwbgg)
6 badge image fetches in 30 days. What the badge shows.
Versions
The registry has recorded 1 version of this file, committed on Feb 5, 2026.
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (7032 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
dicklesworthstone/pi_agent_rust · 1,966 stars · license a license file GitHub does not identify · pushed 2026-11-06 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_hecvhki7dmtfwbgg GET https://markdownregistry.com/api/v1/resolve?ref=dicklesworthstone/pi_agent_rust/ml-pipeline-workflow GET https://markdownregistry.com/api/v1/blob/1452bab2b05e705701bdadbbaeff3543037ce653adf87199c394611b984e016a
Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.
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