Transform codebase conventions into semantic rules for AI agents.
npx skills add https://github.com/darelabs-tech/dare-cli --skill dare-dna
Please help me install this Agent Skill. Skill: dare-dna Source: https://github.com/darelabs-tech/dare-cli/tree/main/.agents/skills/dare-dna Command: npx skills add https://github.com/darelabs-tech/dare-cli --skill dare-dna
This Skill bridges the gap between legacy codebases and modern AI-assisted development by transforming existing project patterns into actionable rules, preventing the AI from applying generic defaults to established brownfield projects.
Run the dare-dna skill to analyze the current directory and populate the project DNA documentation with established coding conventions.
High-intent search queries and answers about installing and using this skill.
To extract legacy codebase conventions, this Skill analyzes pre-existing dna-facts.json data and transforms codebase-specific patterns into actionable semantic rules. It automatically generates a PROJECT-DNA.md file containing the project's established naming, architecture, and testing guidelines.
AI assistants apply generic defaults to brownfield projects because they lack context about established architectural patterns. This Skill bridges that gap by codifying existing project conventions into semantic rules, ensuring new feature development adheres to legacy stylistic and architectural patterns.
You can automatically populate project DNA documentation by running this Skill against the current directory. It analyzes the legacy software project and generates consistent project-specific documentation outlining rules for error handling, validation, and architecture.
To generate consistent project-specific documentation, you need a pre-existing dna-facts.json file containing raw codebase facts. This Skill requires that data as input to extract semantic rules and populate the project DNA file accurately.
Yes, this approach works for onboarding legacy software projects to AI-assisted development. It operates on existing codebases to ensure new features maintain consistency with established error handling, validation, and architectural patterns without requiring manual rule definition.
A limitation of using semantic rule extraction for legacy refactoring is the strict dependency on a pre-existing dna-facts.json file. Without this prerequisite data containing raw codebase facts, the Skill cannot generate the actionable semantic rules needed for documentation.
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