| name | r00-alirezarezvani-claude-code-tresor--datascience |
| description | 🤖 Data Science & AI/ML skill suite derived from alirezarezvani/claude-code-tresor. Data pipelines, model training, evaluation, MLOps and analytical reporting. Provides 10 specialised commands for data-science, machine-learning, analytics workflows.
|
| version | 1.0.0 |
| domain | datascience |
| tags | ["data-science","machine-learning","analytics","pandas","mlops"] |
| source | https://github.com/alirezarezvani/claude-code-tresor |
| license | MIT |
🤖 Data Science & AI/ML Skill Suite
Derived from alirezarezvani/claude-code-tresor · Focus: utilities, autonomous skills, expert agents, slash commands
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
cp -r . ~/.claude/skills/r00-alirezarezvani-claude-code-tresor--datascience/
/read ~/.claude/skills/r00-alirezarezvani-claude-code-tresor--datascience/SKILL.md
Then simply describe your task and Claude will route to the
appropriate command automatically.
