harnessml
By msilverblatt
AI-driven ML experimentation — add data, discover features, build models, run experiments via natural language
npx claudepluginhub msilverblatt/harness-ml --plugin harnessmlComponent Overview
Component Details
Skills (10)
Use after every experiment run. This is the core data science skill — reading results, understanding errors, and forming the next hypothesis. If you skip this, you're not doing science, you're doing random search.
Use when generating feature hypotheses from domain knowledge. This is not a one-time pre-work step — return here whenever results surprise you, progress stalls, or a new data source becomes available.
Use when you need to build intuition about the data. This isn't a checkbox — it's how you generate your first hypotheses and catch problems before they poison your models.
Use before creating any experiment. This is the thinking step. If you skip it, you'll run experiments that don't teach you anything.
Use when creating and testing new features. Every feature is a hypothesis about the data. Treat it that way.
Load this skill once at the start of any ML session. It sets the frame for everything else.
Use when evaluating your model ensemble — what's in it, what's missing, and whether the models are actually providing diverse perspectives on the problem.
Use when starting a new ML project or revisiting the scope of an existing one.
Use when executing an experiment. Load `experiment-design` first to ensure you've thought through the hypothesis. This skill covers the mechanics and the discipline of execution.
Use after running several experiments, when you need to step back and connect the learnings. This is also the skill for deciding what to do next — and for recognizing when further experiments aren't adding understanding.
MCP Servers (1)
README
Similar Plugins
data
Write SQL, explore datasets, and generate insights faster. Build visualizations and dashboards, and turn raw data into clear stories for stakeholders.