pl-data-ml

Plugin pl-data-ml from marketplace prompt-library-marketplace in princemanjee/EngineeredPromptLibrary. Commands read from the repo's manifest — 26 days ago.

Skills (7)

• data-scientist

  Extracts statistically rigorous, business-actionable insights from complex datasets by following a structured Plan-and-Solve workflow with Chain-of-Thought execution and Self-Refine quality auditing — delivering every finding with confidence intervals, effect sizes, and explicit causal caveats.

• data-transformer

  Transforms structured data between schemas with zero data loss and zero fabrication by building a complete numbered transformation plan with all edge cases documented before executing a single step.

• llm-researcher

  Explains LLM and AI research concepts using a Reason/Procedure/Purpose structure anchored by Step-Back abstraction for first-principles grounding, verifies every technical claim and citation through Chain-of-Verification, and refines responses through a Self-Refine cycle — ensuring accuracy, depth,

• machine-learning-engineer

  Teaches machine learning concepts through a structured 6-step plan (Conceptual Intuition, Algorithm Recommendation, Implementation, Visual Aid, Common Pitfalls, Resources) using Plan-and-Solve decomposition and Chain-of-Thought reasoning — refined through a Self-Refine cycle to ensure explanations m

• mathematician

  Evaluates mathematical expressions with 100% numerical accuracy using Program-of-Thought pipelines that show every computational step in a traceable Computation Plan and Python Code block.

• scientific-data-visualizer

  Designs accurate, compelling, and technically implementable data visualizations that transform complex scientific datasets into clear visual narratives using a five-node Plan-and-Solve workflow with Designer's Note on data integrity.

• statistician

  Delivers rigorous, reproducible statistical analyses with a transparent four-phase workflow (Plan, Solve, Verify, Refine) — every answer includes a computation plan, explicit code derivation, point estimate with confidence interval, and a named-benchmark sanity check with PASS/FAIL judgment.

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