Automates AI-first development workflows from specification to deployment.
npx skills add https://github.com/servitola/dotfiles --skill methodology-servitola
Please help me install this Agent Skill. Skill: methodology Source: https://github.com/servitola/dotfiles/tree/main/claude-code/skills/methodology Command: npx skills add https://github.com/servitola/dotfiles --skill methodology-servitola
AI-driven project work suffers from scattered documentation, inconsistent specs, and governance gaps across teams. The methodology provides a structured, end-to-end pipeline with validators, clear roles, and centralized project knowledge to keep work aligned from idea to deployment.
Read the pipeline guide in references/pipeline.md to begin applying the methodology to your project.
High-intent search queries and answers about installing and using this skill.
An AI-first development workflow automates project progression from specification to deployment using a structured pipeline. It translates ideas into user specs, tech specs, and task decomposition while enforcing governance through automated validators at each stage.
Standardize AI project governance by applying a structured pipeline with automated validators at each stage. Centralize project knowledge using a YAML frontmatter metadata model to keep documentation aligned and current from idea to deployment.
Set up a spec-driven pipeline by reading the pipeline guide in references/pipeline.md. This initiates the methodology, applying validators and a YAML frontmatter metadata model to translate ideas into structured specs and task decomposition.
No specific dependencies are required to start managing AI pipelines. The methodology provides a self-contained workflow that uses YAML frontmatter for metadata modeling and reference files for pipeline guidance.
The best way to decompose tasks and ensure QA alignment is using a structured development pipeline. It applies automated validators during implementation and QA stages to catch defects early and maintain alignment with project-knowledge standards.
Avoid a structured AI governance pipeline for small, informal projects where scattered documentation poses minimal risk. The overhead of YAML metadata modeling and stage validators is designed for scalable, team-based development needing strict alignment.
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