DCYFR
AI Lab Assistant
AI Agent Framework & Development Automation
DCYFR is an AI-powered agent swarm built on the @dcyfr/ai framework (published on npm). Features 20+ specialized agents, plugin marketplace with trust scoring, delegation framework v2, and autonomous task routing with reputation-based selection.
AI Contribution Areas
DCYFR's contributions to DCYFR Labs development, security, and documentation efforts.
Code Development
Full-stack features following design patterns, comprehensive test coverage, and TypeScript implementation
Code Review & Quality
Design token compliance validation, pattern enforcement, and accessibility checks
Security Analysis
OWASP compliance audits, vulnerability scanning, and secure architecture implementation
Documentation
Technical guides, decision trees, API references, and architectural documentation
Core Capabilities
AI Agent Framework (@dcyfr/ai)
Portable TypeScript framework published on npm with plugin architecture, multi-provider LLM support, delegation v2, and quality gates
- Plugin marketplace with trust scoring, audit trails, TLP classification, and sandbox isolation (Docker, gVisor, WebAssembly)
- Delegation framework v2: reputation engine, SLA contracts, HMAC auth, and 8 security scenario mitigations
- Multi-provider support: OpenAI, Anthropic, Ollama, GitHub Copilot — swap without changing code
- Companion packages: @dcyfr/ai-cli, @dcyfr/ai-rag, @dcyfr/ai-code-gen — all on npm
Code Development
Full-stack development with 20+ specialized agents (fullstack-developer, typescript-pro, security-engineer, test-engineer, etc.)
- Implement features following established patterns and design systems (100% design token compliance)
- Refactor code for performance, maintainability, and accessibility with agent routing
- Write comprehensive test suites (unit, integration, E2E) — 1000+ tests in @dcyfr/ai
- Debug complex issues across frontend, backend, and infrastructure via specialized agents
Security Analysis & Automation
Security-first agent swarm with pilot plugin security scanning, incident response SLAs, and automated vulnerability detection
- Automated security scanning: secrets detection (Gitleaks), SBOM generation, dependency audits
- Plugin marketplace security: trust scoring, audit tracking, incident timeline, emergency disablement
- Sandboxed plugin execution: Docker, gVisor, WebAssembly isolation with resource limits
- TLP classification enforcement: RED/AMBER/GREEN access control and security middleware chain
Documentation & Knowledge Management
Create clear, actionable documentation that serves both humans and AI systems
- Write comprehensive technical documentation and API references
- Create decision trees and quick reference guides
- Document architecture decisions and trade-offs
- Build knowledge bases that improve over time
Code Review & Quality Assurance
Enforce code quality, design patterns, and best practices across the codebase
- Review code for design token compliance and accessibility
- Validate TypeScript types and lint configurations
- Check test coverage and identify missing test cases
- Ensure consistent patterns and barrel export usage
Architecture & Planning
Guide technical decisions with deep understanding of trade-offs and constraints
- Recommend appropriate patterns for new features
- Plan refactoring strategies for complex codebases
- Evaluate technology choices and dependencies
- Design scalable, maintainable system architectures
Guiding Philosophy
- Augmentation over replacement — AI agent swarm enhances human expertise rather than replacing it
- Context is king — Deep understanding of codebase, patterns, and constraints enables better solutions
- Quality over speed — Correct, maintainable code matters more than quick shortcuts
- Reputation-driven delegation — Tasks route to agents with proven track records in specific domains
- Security-first architecture — TLP classification, audit trails, sandboxed execution, and 8+ security mitigations
- Teach, don't just do — Explain reasoning and trade-offs to build shared understanding
- Continuous learning — Adapt to new patterns, frameworks, and team conventions through Ralph Loop v2
Integration with DCYFR Labs
Agent Swarm Coordination
20+ specialized agents (framework, engineering, quality, operations) coordinate via delegation framework v2. Tasks route to agents with best reputation scores in specific capabilities. Automatic agent selection eliminates manual task routing.
Security Posture
Security middleware chain validates every delegation contract: identity (HMAC-SHA256), TLP clearance, threat vectors, content policy (prompt injection), permissions, chain depth, rate limits, and reputation gates. 8 adversarial scenarios mitigated (Agents of Chaos research).
Knowledge Amplification
Context Engineering Knowledge System (CEKS) in nexus/ — Polaris mission files, 6 cognitive partnership patterns, memory layer, session templates, and investigation registry. TypeScript API with 35 tests. Obsidian vault integration for knowledge graph visualization.
Continuous Learning (Ralph Loop v2)
Self-improvement loop: delegation failures analyzed, prompts rewritten, patterns saved to memory layer. Future similar tasks apply learned patterns automatically. Full rewrite history in data/rewrite-history.jsonl. Reputation scoring adapts based on task outcomes.
Development Approach
- Start with understanding — Gather context before making changes
- Follow existing patterns — Don't reinvent unless necessary
- Validate continuously — Run tests and checks after every change
- Document trade-offs — Explain why decisions were made
- Ask when uncertain — Clarify ambiguous requirements rather than guessing
- Prioritize maintainability — Code is read more than it's written