Ship smarter code.
AI coding with structure, memory, and quality gates.
The open-source methodology that guides AI assistants through structured development loops. Analyze, spec, build, audit, ship. Works with Claude Code, OpenAI Codex, Gemini CLI, GitHub Copilot, Cursor, and Windsurf.
Why KnowzCode?
Structure that helps, not hinders — a methodology for AI-assisted development that scales with complexity
Adaptive Development Loop
Tasks scale from micro fixes to full 5-phase workflows with quality gates between each phase
Living Documentation
Architecture diagrams and specs auto-update alongside code changes — never go stale
Session Memory
WorkGroups track complete context so you can say "continue" and resume exactly where you left off
Multi-Platform
Works with Claude Code, Codex, Gemini, Cursor, Copilot, and Windsurf via platform adapters
Specialist Agent Roles
Analyst, architect, builder, reviewer, security, testing, and knowledge roles coordinate through the workflow
Powered by Knowz
Team decisions, patterns, and learnings stored in vaults and queried live during development via MCP
How KnowzCode Works
A phased workflow with approval gates that guides AI from impact analysis through implementation to quality audit.
Goal → Analyze Impact → Draft Specs → Build with TDD → Audit Quality → Ship. Each transition is an approval gate requiring sign-off before advancing.
Useful for: Teams that want predictable, reviewable AI output instead of surprise refactors.
Micro tasks (single file) skip the full loop. Light tasks (≤3 files) get a 2-phase fast path. Full features get the complete 5-phase workflow with multi-agent teams. Auto-classified by complexity.
Useful for: Right-sizing the process — quick fixes stay quick, complex features get proper rigor.
Read-only audits compare implementation against the spec, tests, architecture, and known risks before finalization.
Useful for: Maintaining code quality at scale without manual review bottlenecks.
Your team's decisions, patterns, and learnings are stored in Knowz vaults and queried live by agents during development. Knowledge accumulates across projects and sessions.
Useful for: Teams tired of repeating the same mistakes or rediscovering the same patterns.
Specialist roles gather context, build with TDD, audit results, and advise on security and testing. Parallel work stays coordinated through WorkGroups.
Useful for: Complex features that benefit from parallel specialized analysis and implementation.
Claude Code and Codex use native skills. Gemini uses GEMINI.md plus native commands and agents. Copilot, Cursor, and Windsurf use platform-specific prompt and rules adapters.
Useful for: Teams with mixed tooling who want a consistent development methodology.
Quick Start
Install the plugin, generate your project adapters, and start building in under a minute
# Add the marketplace
/plugin marketplace add knowz-io/knowz-skills
# Install KnowzCode
/plugin install knowzcode@knowz-skills
# Initialize in your project
/knowzcode:setup
# Start building with structure
/knowzcode:work "add user authentication with JWT"
The install guide covers Codex, Claude Code, Gemini CLI, Cursor, GitHub Copilot, and Windsurf adapters.
Full setup guideBuilt for Every Developer
Whether you're solo or part of a large team, KnowzCode scales with you
For Solo Developers
- Structured AI-assisted coding without losing context between sessions
- Personal knowledge vault captures learnings, patterns, and decisions
- TDD workflows ensure your solo code is production-quality
- Quality audits catch issues a second pair of eyes would find
- Adaptive scaling means quick fixes stay quick
- Works offline — knowledge syncs when connected
For Teams
- Shared knowledge vault means AI assistants learn from the whole team
- Architecture decisions and patterns captured automatically
- New team members get up to speed via knowledge-backed AI guidance
- Consistent quality gates across all contributors
- Multi-agent workflows parallelize complex feature work
- Audit trails for every AI-assisted change
For Open Source
- Contributors get context from project knowledge vault via MCP
- Spec-driven PRs are easier to review and understand
- Quality gates maintain standards across diverse contributors
- Architectural decisions documented and queryable
- Automated TDD reduces maintainer review burden
- Works on any AI coding platform contributors prefer
For Enterprises
- Audit trails and quality gates built into every workflow
- Security officer agent reviews changes for vulnerabilities
- Multi-tenant knowledge isolation between projects
- Compliance-ready with structured spec approval workflows
- Institutional knowledge preserved when team members leave
- Documented audit findings and verification status for every change