agentspec

**31 slash commands** for the SDD workflow, data engineering, visualization, and developer productivity.

Analyze meeting transcripts — extract decisions, action items, and create SSOT documents

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Generate comprehensive, production-ready README.md by analyzing codebase with explorer + documenter agents

Generate a comprehensive project status report — active features, recent decisions, agent recommendations, and health assessment

AgentSpec deploys **58 specialized agents** across **8 categories**, each built on a **three-tier template system** with mandatory **KB-First knowledge resolution**. Every agent carries a cognitive framework that enforces structured confidence scoring, provenance tracking, and explicit stop conditions -- turning raw LLM capability into disciplined, auditable domain expertise.

Cloud data platform specialist for Snowflake, Databricks, BigQuery, and infrastructure decisions. Use PROACTIVELY when comparing platforms, optimizing costs, or provisioning data infrastructure. <example> Context: User comparing cloud platforms user: "Should we use Snowflake or Databricks for our analytics?" assistant: "I'll use the data-platform-engineer agent to compare options." </example> <example> Context: User needs cost optimization user: "Our Snowflake bill is too high, help optimize" assistant: "Let me invoke the data-platform-engineer to analyze costs." </example>

GenAI Systems Architect for multi-agent orchestration, agentic workflows, and production AI systems. Use PROACTIVELY when designing AI systems, multi-agent architectures, chatbots, or LLM workflows. <example> Context: User wants to design an AI system user: "Design a customer support chatbot with routing" assistant: "I'll use the genai-architect to design the multi-agent architecture." </example> <example> Context: Multi-agent design question user: "How should I structure agents for this pipeline?" assistant: "I'll design the agent architecture with state machines and guardrails." </example>

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Open table format and catalog specialist for Iceberg, Delta Lake, and lakehouse governance. Use PROACTIVELY when working with Iceberg, Delta, catalog setup, or format migration. <example> Context: User needs Iceberg table setup user: "Set up Iceberg tables with partition evolution" assistant: "I'll use the lakehouse-architect agent to design the setup." </example> <example> Context: User comparing table formats user: "Should we use Delta Lake or Iceberg?" assistant: "Let me invoke the lakehouse-architect to compare formats." </example>

Intelligent agent routing -- automatically matches tasks to the best specialist agent based on file patterns, intent keywords, and domain context. Loaded every session to give Claude explicit routing rules for all 58 AgentSpec agents.

Data engineering expertise for pipelines, schemas, data quality, SQL, lakehouse, and streaming. Use PROACTIVELY when the user discusses data pipelines, ETL/ELT, schema design, dimensional modeling, data quality checks, SQL optimization, dbt models, Spark jobs, Airflow DAGs, streaming pipelines, lakehouse architecture, or data contracts.

Create Excalidraw diagram JSON files that make visual arguments. Use when the user wants to visualize workflows, architectures, or concepts.

Spec-Driven Development workflow guidance for structured feature development. Use PROACTIVELY when the user discusses building features, planning implementations, capturing requirements, designing architectures, or shipping completed work. Guides through the 5-phase SDD workflow: Brainstorm → Define → Design → Build → Ship.

Generate beautiful, self-contained HTML pages that visually explain systems, code changes, plans, and data. Use when the user asks for a diagram, architecture overview, diff review, plan review, project recap, comparison table, or any visual explanation of technical concepts. Also use proactively when you are about to render a complex ASCII table (4+ rows or 3+ columns) — present it as a styled HTML page instead.

1 hook across 1 event