Reading
AgentSpec is a specification-driven data engineering framework designed for Antigravity IDE. It enables a complete data pipeline development workflow from brainstorming to delivery through 63 specialized agents and 25 knowledge base domains.
Section 01
AgentSpec is a specification-driven data engineering framework designed for Antigravity IDE. It enables a complete data pipeline development workflow from brainstorming to delivery through 63 specialized agents and 25 knowledge base domains.
Section 02
Traditional AI-assisted data engineering faces several core issues:
Cold Start Problem: Each session starts from scratch with no project history memory, leading to repeated mistakes.
Hallucination Generation: AI may generate seemingly reasonable but actually incorrect SQL, such as wrong incremental strategies or unreasonable partition key choices.
Context Fragmentation: Complex data pipelines involve multiple components (dbt models, Spark jobs, Airflow DAGs), making it hard for AI to maintain global consistency.
Uncontrollable Quality: Lack of systematic verification mechanisms, leading to issues often exposed only after deployment.
AgentSpec solves these problems through the "Spec-First" approach—define clear specification documents before writing code, and all agents work based on these specs to ensure consistency and traceability.
Section 03
AgentSpec defines five stages from concept to delivery:
Section 04
Command: /brainstorm
In this stage, AI agents help users explore ideas, compare different solutions, and raise discovery questions. The output is a BRAINSTORM_{FEATURE}.md document containing at least 3 key questions and 2+ feasible solutions. The YAGNI filter ensures no over-engineering.
Section 05
Command: /define
Transforms brainstorming into a structured requirements document DEFINE_{FEATURE}.md. Each requirement is scored for clarity (Clarity Score), which must reach above 12/15 to proceed to the next stage. This ensures requirements are clear enough for agents to understand accurately.
Section 06
Command: /design
Generates a complete design document DESIGN_{FEATURE}.md, including file list, pipeline architecture diagram (Mermaid format), Architecture Decision Records (ADR), and acceptance criteria. The output of the design stage serves as the blueprint for all subsequent implementations.
Section 07
Command: /build
This is the execution phase. The system automatically delegates tasks to specialized agents based on the design document. If the design specifies dbt models, PySpark jobs, and Airflow DAGs, the system will assign them to the dbt-specialist, spark-engineer, and pipeline-architect agents to work in parallel. The output includes code and a BUILD_REPORT.md test report.
Section 08
Command: /ship
The archiving phase after project completion. Generates a SHIPPED_{DATE}.md document recording lessons learned and knowledge base updates. This ensures project knowledge is accumulated and reused in future projects.
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