Data & AI Team

Generate a comprehensive Product Requirements Document (PRD) in Markdown, detailing user stories, acceptance criteria, technical considerations, and metrics. Optionally create GitHub issues upon user confirmation.

Use when the user wants to analyze retention, cohort behavior, engagement trends, or understand how different user groups perform over time. Triggers on: 'cohort analysis', 'retention analysis', 'user retention', 'cohort retention', 'week 1 retention', 'retention curve'.

Expert guidance for fine-tuning LLMs with Axolotl - YAML configs, 100+ models, LoRA/QLoRA, DPO/KTO/ORPO/GRPO, multimodal support

Send and receive cryptographically signed messages between AI agents using the Agent Messaging Protocol (AMP). Use when the user asks to "send a message to an agent", "check agent inbox", "message another agent", "reply to a message", "notify an agent", or any inter-agent communication task.

Use when a team needs strategic guidance at any stage of a hackathon — from initial ideation through pitch delivery. Specifically:\n\n<example>\nContext: A team of four arrives at a 24-hour AI hackathon with no idea, a vague interest in healthcare, and two hours before the kick-off presentation deadline.\nuser: \"We have no idea yet, the theme is AI for Good, and we need a concept in the next 2 hours. We have two ML engineers, one frontend dev, and a designer.\"\nassistant: \"I need a few details first — what sponsor APIs are available, and is there a specific judging track we must target? Once I have that, I'll generate three ranked concept options with feasibility scores, and we'll lock one in within 15 minutes so the team can start the environment setup.\"\n<commentary>\nInvoke hackathon-ai-strategist for pre-hackathon ideation when the clock is already running and the team needs a structured, fast-converging concept selection process.\n</commentary>\n</example>\n\n<example>\nContext: A team is 14 hours into a 24-hour hackathon. Their original plan involved a real-time video analysis feature that is behind schedule and may not be completable.\nuser: \"We're behind. The video pipeline isn't working and we only have 10 hours left. What do we cut?\"\nassistant: \"Let's triage immediately. Tell me what's working reliably right now, what the demo absolutely needs to show, and which judging criteria are worth the most points. I'll give you a re-scoped MVP plan with explicit cut decisions so the team can align and get back to building within the next 30 minutes.\"\n<commentary>\nUse hackathon-ai-strategist for mid-hackathon triage when a team needs a fast re-scoping decision backed by judging criteria analysis, not a general project manager.\n</commentary>\n</example>\n\n<example>\nContext: A team has a working prototype with 6 hours left before judging. They need to turn it into a compelling 3-minute demo and slide deck.\nuser: \"We have something working. How do we structure the pitch and demo for the next 6 hours?\"\nassistant: \"I'll outline a time-annotated 3-minute pitch structure and a demo reliability checklist. Then we'll split the remaining time: 2 hours on demo stabilization, 2 hours on slides, 1 hour on rehearsal, 1 hour buffer. Walk me through what the product does so I can draft the hook and problem statement.\"\n<commentary>\nInvoke hackathon-ai-strategist when a team transitions from building to presenting and needs a concrete pitch structure, demo script, and rehearsal plan.\n</commentary>\n</example>

Audit a project for software supply chain risk, including dependency vulnerabilities, lockfile issues, malicious package indicators, SBOM coverage, and license concerns.

Expert Power BI data modeling guidance using star schema principles, relationship design, and Microsoft best practices for optimal model performance and usability.

Expert-level software engineering agent. Deliver production-ready, maintainable code. Execute systematically and specification-driven. Document comprehensively. Operate autonomously and adaptively.

Expert Power BI DAX guidance using Microsoft best practices for performance, readability, and maintainability of DAX formulas and calculations.

Use when designing LLM systems for production, implementing fine-tuning or RAG architectures, optimizing inference serving infrastructure, or managing multi-model deployments.