ml-pipeline
Single entry point for ML-related tasks. Orchestrates three roles across a data-driven analysis pipeline: Product Manager formulates the task, ML Engineer owns analysis and modeling, SRE Engineer extracts production data. Domain specifics are read from the project''s CLAUDE.md.'
npx skills add majiayu000/claude-skill-registry --skill ml-pipeline-avav25-ai-assets --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
--- name: ml-pipeline description: ML pipeline orchestrator — single entry point for ML-related tasks. Coordinates ML Engineer (analysis, modeling, recommendations), SRE Engineer (production data extraction), and Product Manager (task formulation). Domain context from CLAUDE.md. MVP flow: define data requirements → extract from prod → analyze → model → recommend → feature plan. context: fork argument-hint: [analysis goal or dataset description] --- # ML Pipeline Single entry point for ML-related tasks. Orchestrates three roles across a data-driven analysis pipeline: **Product Manager** formulates the task, **ML Engineer** owns analysis and modeling, **SRE Engineer** extracts production data. Domain specifics are read from the project's `CLAUDE.md`. **Architecture note**: This workflow is extensible — future sub-flows (model training, prompt tuning, A/B testing) can be added as numbered branches in Step 2. ## 1. Receive ML Task Gather the ML task from the user: - **Objective**: What do you want to learn or improve? (e.g., "tune scoring weights", "improve data collection prompts", "analyze conversion funnel") - **Data source**: Where is the data? (production database, logs, external
- 1. Receive ML Task
- 2. Determine Pipeline Type
- 3. Read Project Context
- 4. Formulate ML Task
- 5. Define Data Requirements
- 6. Extract Production Data
- 6a. Establish Production Access
- 6b. Execute Data Extraction
- 6c. Data Handoff
- 7. Analyze Data and Build Models
- 7a. Exploratory Data Analysis (EDA)
- 7b. Statistical Analysis
- 7c. Modeling (if applicable)
- 7d. Document Results
What does the ml-pipeline skill do?
Single entry point for ML-related tasks. Orchestrates three roles across a data-driven analysis pipeline: Product Manager formulates the task, ML Engineer owns analysis and modeling, SRE Engineer extracts production data. Domain specifics are read from the project''s CLAUDE.md.'
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill ml-pipeline-avav25-ai-assets --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.