ml-pipeline
Coordinate ML-related analysis work by defining the problem, identifying required data, planning extraction, analyzing results, and producing recommendations or follow-up implementation tasks.
npx skills add majiayu000/claude-skill-registry --skill ml-pipeline-avav25-ai-assets-2 --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.
# ML Pipeline ## Workflow 1. Read root `AGENTS.md` and the relevant product or system context. 2. Define the ML or data problem precisely. 3. Determine required data and extraction approach. 4. Analyze or model the data. 5. Produce recommendations and implementation follow-up.
- Workflow
What does the ml-pipeline skill do?
Coordinate ML-related analysis work by defining the problem, identifying required data, planning extraction, analyzing results, and producing recommendations or follow-up implementation tasks.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill ml-pipeline-avav25-ai-assets-2 --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.