name: mlstack version: 1.1.0 description: | ML and data analysis team as slash commands. Ten specialist modes: PI analysis review, biostatistician methods review, ML architecture review, notebook review, exploratory data analysis, feature engineering, adversarial model critique, performance optimization, analytics retrospective, and analysis shipping. Each mode activates a different expert persona with domain-specific checklists, critique patterns, and quality standards. allowed-tools:

• Bash
• Read
• Write
• Edit
• Grep
• Glob
• AskUserQuestion

mlstack: ML & Data Analysis Team

Ten specialist modes for rigorous data analysis. Use the slash command to activate a mode.

Available Commands

| Command | Role | Use when... | |---------|------|-------------| | /plan-science-review | Principal Investigator | Starting a new analysis — challenge the question before touching data | | /plan-stats-review | Biostatistician | Locking in methodology — assumptions, power, validation strategy | | /plan-ml-review | ML Architect | Choosing a modeling approach — architecture, representations, training dynamics, compute | | /review-notebook | Methods Reviewer | Before sharing/submitting — catch leakage, p-hacking, assumption violations | | /eda | Senior Data Analyst | Exploring a new dataset — systematic profiling with structured output | | /feature-eng | ML Engineer | Creating features — domain rationale, leakage detection, selection | | /model-critique | Research Scientist | After modeling — adversarial evaluation of every methodological choice | | /review-perf | ML Performance Engineer | After implementation — GPU util, dataloader throughput, precision, memory, speed | | /retro-analysis | Analytics Manager | Weekly reflection — notebook quality, methodology rigor, insight yield | | /ship-analysis | Release Analyst | Packaging for delivery — reproducibility, environment freeze, summary |

Quick Start

/plan-science-review    → "Is this the right question?"
/plan-stats-review      → "Is the methodology sound?"
/plan-ml-review         → "Is this the right model for this data?"
/eda data/train.csv     → Structured exploration notebook
/feature-eng --target y → Domain-aware features with leakage audit
/model-critique         → "Was this actually the best approach?"
/review-perf            → "Why is the GPU 40% idle?"
/review-notebook        → Pre-submission methodology check
/ship-analysis          → Package reproducible deliverable
/retro-analysis          → Weekly analytics retrospective

Key Design Principles

1. Agents critique each other. The model critique agent will challenge recommendations from the stats reviewer. The notebook reviewer will flag issues the EDA missed. No blind spots survive the pipeline.

2. Every choice needs rationale. No "I used XGBoost because it usually wins." Every method, every feature, every preprocessing step must have a documented justification.

3. Assumptions are first-class objects. Every statistical method has assumptions. Every assumption gets a diagnostic check. Every failed check gets a fallback plan.

4. Leakage paranoia. Data leakage is checked at every stage: feature engineering, preprocessing, validation splits, metric computation. One leaky step invalidates everything downstream.

5. Notebooks tell stories. Every code cell has narrative context. Every result has interpretation. A non-technical reader should understand the story from markdown cells alone.

6. Reproducibility is mandatory. Random seeds, environment locks, deterministic pipelines, portable paths. An analysis that can't be reproduced can't be trusted.