consciousnessbrawler/r13-danielrosehill-claude-slash-commands-datascience

41 stars Качество 40 Тренд 40

🤖 Data Science & AI/ML skill suite derived from danielrosehill/Claude-Slash-Commands.

Обзор

Source focus: slash commands, hooks, flat command listing Data pipelines, model training, evaluation, MLOps and analytical reporting. This collection provides and , all with a consistent structured-output UI so you always know exactly where you are and what to do next. All commands display structured output with: - — real-time step tracking - — sorted by severity (🔴🟠🟡🟢) - — quick wins → medium-term → strategic - — at-a-glance metrics after each command Every command follows this 5-step structure: This suite is derived from which focuses on: slash commands, hooks, flat command listing. Improvements in this adaptation: - Domain-specific command vocabulary for Data Science & AI/ML - Enhanced structured output with visual progress tracking - Prioritised action plans with time estimates - Workflow orchestration for end-to-end processes - Consistent UI conventions across all commands

README

🤖 Data Science & AI/ML Skills Suite

Derived from danielrosehill/Claude-Slash-Commands

Adaptation of danielrosehill/Claude-Slash-Commands for Data Science & AI/ML use cases. Source focus: slash commands, hooks, flat command listing

What This Skill Suite Does

Data pipelines, model training, evaluation, MLOps and analytical reporting.

This collection provides 10 specialised commands and 5 multi-step workflows, all with a consistent structured-output UI so you always know exactly where you are and what to do next.

Quick Install

# Clone this skill
cp -r . ~/.claude/skills/r00-danielrosehill-Claude-Slash-Commands--datascience/

# Register in Claude Code
# In a Claude Code session:
/read ~/.claude/skills/r00-danielrosehill-Claude-Slash-Commands--datascience/SKILL.md

Commands

Command	Description
`/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

Usage:

/data-profiling 
/feature-engineer --scope full --output md

Workflows (Multi-step)

Workflow	Description
`ml-project-init`	End-to-end ML project: EDA → baseline → feature engineering → model → deploy
`data-migration`	Data warehouse migration: audit → schema map → ETL → validation → cutover
`reporting-pipeline`	Automated reporting pipeline: source → transform → validate → visualise → deliver
`model-retraining`	Scheduled model retraining: drift detect → retrain → shadow → promote → monitor
`analytics-sprint`	2-week analytics sprint: question → data → analysis → insight → recommendation

Usage:

/workflows:ml-project-init  --scope full

UI Design

All commands display structured output with:

Progress panels — real-time step tracking
Findings tables — sorted by severity (🔴🟠🟡🟢)
Action checklists — quick wins → medium-term → strategic
Summary cards — at-a-glance metrics after each command

Progress Display Example

╔══════════════════════════════════════════════════╗
║  ML Pipeline  —  churn_prediction_v3             ║
╠══════════════════════════════════════════════════╣
║  Data ingestion   ✓   1.2M rows loaded            ║
║  Profiling        ✓   12 features, 3 issues found ║
║  Feature eng.     ✓   47 features created          ║
║  Training         ⟳   Epoch 18/50  [███████░░░]   ║
║  Evaluation       ░   Pending                      ║
║  Registry push    ░   Pending                      ║
╚══════════════════════════════════════════════════╝

DATA QUALITY ISSUES
  ✗  customer_age   → 847 nulls (0.07%)  → Impute with median
  ⚠  last_purchase  → 12 future dates    → Clip to today
  ⚠  revenue        → 3σ outliers: 214   → Review before drop

Interaction Pattern

Every command follows this 5-step structure:

① Scope Confirmation  — verify target and options with user
② Live Analysis       — progress bar while working
③ Findings Table      — structured results sorted by impact
④ Action Plan         — prioritised, time-boxed recommendations
⑤ Next Steps          — suggested follow-up commands

Source Repository

This suite is derived from danielrosehill/Claude-Slash-Commands which focuses on: slash commands, hooks, flat command listing.

Improvements in this adaptation:

Domain-specific command vocabulary for Data Science & AI/ML
Enhanced structured output with visual progress tracking
Prioritised action plans with time estimates
Workflow orchestration for end-to-end processes
Consistent UI conventions across all commands

License

MIT — free to use, modify and distribute.

View this README on GitHub

Установка

npx skillfish add consciousnessbrawler/r13-danielrosehill-claude-slash-commands-datascience