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By jcmrs
Co-create rigorous multi-perspective technical specs for GitHub projects through conversational AI, using dynamic memory graphs, adaptive workflows, and multi-role analysis for Spec Kit methodology.
npx claudepluginhub jcmrs/claude-code-spec-kit-subagent-pluginUses power tools
Uses Bash, Write, or Edit tools
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Based on adoption, maintenance, documentation, and repository signals. Not a security audit or endorsement.
Spec-driven development methodology for Claude Code. Provides skills for requirements engineering (EARS format), design documentation, task breakdown, AI prompting strategies, quality assurance, and troubleshooting.
Create technical specifications through interactive planning and expert review
Specification-Driven Development with Process Discipline for Claude Code
Spec-driven development with search, conflict detection, and reporting
Harness-native ECC plugin for engineering teams - 64 agents, 262 skills, 84 legacy command shims, reusable hooks, rules, MCP conventions, and operator workflows for Claude Code plus adjacent agent harnesses
Comprehensive C4 architecture documentation workflow with bottom-up code analysis, component synthesis, container mapping, and context diagram generation
Procedural Memory System - Learns procedures, processes, and workflows from session experiences, generates project-scoped rules for consistent behavior. Shame on you.
Persistent schema-driven running log with three-component architecture: quick-capture ideas, AI auto-detection, and backlog review librarian
Leverage Google Gemini's CLI for analyzing large codebases beyond typical context limits using @ syntax for file/directory inclusion
Transforms messy human intent and repository analysis into living operational domain profiles through 6-phase knowledge engineering pipeline
Semantic validation and translation between natural language user intent and domain-specific technical implementations. Bridges the gap between human natural language and AI specificity of needs.
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