# r00-ComposioHQ-awesome-claude-skills--datascience

> 🤖 Data Science & AI/ML skill suite derived from ComposioHQ/awesome-claude-skills. Data pipelines, model training, evaluation, MLOps and analytical reporting. Provides 10 specialised commands for data-science, machine-learning, analytics workflows.

- Skill: `tameoltreasure34/r00-composiohq-awesome-claude-skills-datascience` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add tameoltreasure34/r00-composiohq-awesome-claude-skills-datascience`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tameoltreasure34/r00-composiohq-awesome-claude-skills-datascience/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Data & Analytics
- License: MIT
- Author: Tameoltreasure34 (https://skillmd.com/u/tameoltreasure34)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/tameoltreasure34/r00-composiohq-awesome-claude-skills-datascience

---


# 🤖 Data Science & AI/ML Skill Suite

> Derived from **ComposioHQ/awesome-claude-skills** · Focus: _Composio integrations, 500+ app connections, real actions_

## Overview

This skill provides 10 production-ready commands tailored for
**Data Science & AI/ML** workflows. All commands follow a consistent
interaction pattern with structured output, progress tracking and
actionable recommendations.

## Available Commands

- `/data-profiling` — Automated EDA report: distributions, nulls, outliers, correlations and drift
- `/feature-engineer` — Feature importance analysis with SHAP values and automated encoding recipes
- `/model-evaluate` — Model performance dashboard: ROC, PR curves, confusion matrix and bias check
- `/pipeline-scaffold` — Modular ML pipeline scaffold with versioning, logging and registry hooks
- `/ab-test-design` — Statistical A/B test design: sample size, power, MDE and sequential testing
- `/sql-optimize` — Query plan analysis, index recommendations and cost estimation
- `/dashboard-spec` — BI dashboard specification from KPI list with chart types and data sources
- `/data-contract` — Schema validation, SLA definition and data quality contract generation
- `/llm-eval` — LLM output evaluation harness: hallucination rate, faithfulness and latency
- `/anomaly-detect` — Time-series anomaly detection with root-cause attribution and alert tuning

## Interaction Pattern

Every command follows this structured response format:

```
1. CONTEXT CHECK   — Verify inputs and confirm scope with user
2. ANALYSIS        — Deep analysis with live progress display
3. FINDINGS TABLE  — Structured results with severity / priority
4. RECOMMENDATIONS — Prioritised action list (quick wins first)
5. NEXT STEPS      — Suggested follow-up commands
```

## UI Conventions

| Symbol | Meaning              |
|--------|----------------------|
| ✓      | Passed / complete    |
| ✗      | Failed / critical    |
| ⚠      | Warning / review     |
| ⟳      | In progress          |
| ░      | Pending              |
| 🔴     | Critical severity    |
| 🟠     | High severity        |
| 🟡     | Medium severity      |
| 🟢     | Low / informational  |

Progress bars use block characters:
`[████████░░] 80%`

## Quick Start

```bash
# Install this skill
cp -r . ~/.claude/skills/r00-ComposioHQ-awesome-claude-skills--datascience/

# In Claude Code
/read ~/.claude/skills/r00-ComposioHQ-awesome-claude-skills--datascience/SKILL.md
```

Then simply describe your task and Claude will route to the
appropriate command automatically.