Generate 13-layer domain specifications with bidirectional traceability.
npx skills add https://github.com/GuiMiran/spectra --skill spectra-guimiran
Please help me install this Agent Skill. Skill: spectra Source: https://github.com/GuiMiran/spectra/tree/main/skills Command: npx skills add https://github.com/GuiMiran/spectra --skill spectra-guimiran
Spectra provides a domain-first specification framework that enables AI agents to operate autonomously by codifying rules, invariants, and contracts before any code is written.
Generate a full 13-layer Spectra specification for a new domain, starting with Layer 00 Vision & Context.
High-intent search queries and answers about installing and using this skill.
To create domain specifications for autonomous AI agents, you use a domain-first framework that codifies business rules, invariants, and contracts upfront. Spectra generates a 13-layer structure starting from Vision and Context to ensure AI-driven governance.
Spec-driven development for complex domains involves defining exhaustive, contract-based specifications that capture business rules and decisions before implementation. This approach enables bidirectional traceability to ensure alignment between specs and code.
Bidirectional traceability between specs and implementation is maintained via a dedicated trace layer updated during iterations. This mechanism links requirements to code changes, ensuring continuous alignment throughout the development lifecycle.
Yes, spec-driven development applies to both early-stage project scoping and ongoing specification evolution. The framework supports generating comprehensive layers and updating a live trace layer during iterations to adapt to changing domain requirements.
A limitation of a 13-layer specification framework is the overhead required to maintain the exhaustive, contract-based layers and metadata. Teams must continuously update the live trace layer to pass the reconstruction test and ensure specs accurately recreate the system.
Yes, spec-driven development for autonomous AI agents requires mandatory frontmatter metadata, specifically name and description fields. This metadata ensures the AI can properly parse and govern the 13-layer domain specifications.
javierhbrGuide users from initial concepts to executable tasks through structured spec-driven workflows.
Smith-HappensGenerate traceability matrices linking requirements to tests and code.
aporto-techRun QA tests on web applications and fix bugs iteratively.
Datus-aiInitialize data engineering projects with lightweight context stores and AGENTS.md skeletons.
