property-testing-guide
Introduces property-based testing with proptest, helping users find edge cases automatically by testing invariants and properties. Activates when users test algorithms or data structures.
rmcp-quickstart
Quick start guide for creating MCP servers with the rmcp crate - installation, concepts, and first server
rust-2024-migration
Guides users through migrating to Rust 2024 edition features including let chains, async closures, and improved match ergonomics. Activates when users work with Rust 2024 features or nested control flow.
test-coverage-advisor
Reviews test coverage and suggests missing test cases for error paths, edge cases, and business logic. Activates when users write tests or implement new features.
when-managing-github-projects-use-github-project-management
Comprehensive GitHub project management with swarm-coordinated issue tracking, project board automation, and sprint planning. Coordinates planner, issue-tracker, and project-board-sync agents to automate issue triage, sprint planning, milestone tracking, and project board updates. Integrates with GitHub Projects v2 API for advanced automation, custom fields, and workflow orchestration. Use when managing development projects, coordinating team workflows, or automating project management tasks.
when-gathering-requirements-use-interactive-planner
Use Claude Code's AskUserQuestion tool to gather comprehensive requirements through structured multi-select questions.
when-documenting-code-use-doc-generator
Automated comprehensive code documentation generation with API docs, README files, inline comments, and architecture diagrams
thiserror-expert
Provides guidance on creating custom error types with thiserror, including proper derive macros, error messages, and source error chaining. Activates when users define error enums or work with thiserror.
when-developing-ml-models-use-ml-expert
Specialized ML model development, training, and deployment workflow
when-detecting-fake-code-use-theater-detection
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when-deploying-cloud-swarm-use-flow-nexus-swarm
Deploy cloud-based AI agent swarms with event-driven workflow automation using Flow Nexus platform. Supports hierarchical, mesh, ring, and star topologies with E2B sandbox distribution.
theater-detection-audit
Performs comprehensive audits to detect placeholder code, mock data, TODO markers, and incomplete implementations in codebases. Use this skill when you need to find all instances of "theater" in code such as hardcoded mock responses, stub functions, commented-out production logic, or fake data that needs to be replaced with real implementations. The skill systematically identifies these instances, reads their full context, and completes them with production-quality code.
swarm-orchestration
Orchestrate multi-agent swarms with agentic-flow for parallel task execution, dynamic topology, and intelligent coordination. Use when scaling beyond single agents, implementing complex workflows, or building distributed AI systems.
swarm-advanced
Advanced swarm orchestration patterns for research, development, testing, and complex distributed workflows
style-audit
Audits code against CI/CD style rules, quality guidelines, and best practices, then rewrites code to meet standards without breaking functionality. Use this skill after functionality validation to ensure code is not just correct but also maintainable, readable, and production-ready. The skill applies linting rules, enforces naming conventions, improves code organization, and refactors for clarity while preserving all behavioral correctness verified by functionality audits.
stream-chain
Stream-JSON chaining for multi-agent pipelines, data transformation, and sequential workflows
sparc-methodology
SPARC (Specification, Pseudocode, Architecture, Refinement, Completion) comprehensive development methodology with multi-agent orchestration
sop-product-launch
Complete product launch workflow coordinating 15+ specialist agents across research, development, marketing, sales, and operations. Uses sequential and parallel orchestration for 10-week launch timeline.
sop-code-review
Comprehensive code review workflow coordinating quality, security, performance, and documentation reviewers. 4-hour timeline for thorough multi-agent review.
sop-api-development
Complete REST API development workflow coordinating backend, database, testing, documentation, and DevOps agents. 2-week timeline with TDD approach.
smart-bug-fix
Intelligent bug fixing workflow combining root cause analysis, multi-model reasoning, Codex auto-fix, and comprehensive testing. Uses RCA agent, Codex iteration, and validation to systematically fix bugs.
slash-command-encoder
Creates ergonomic slash commands (/command) that provide fast, unambiguous access to micro-skills, cascades, and agents. Enhanced with auto-discovery, intelligent routing, parameter validation, and command chaining. Generates comprehensive command catalogs for all installed skills with multi-model integration.
skill-forge
Advanced skill creation system for Claude Code that combines deep intent analysis, evidence-based prompting principles, and systematic skill engineering. Use when creating new skills or refining existing skills to ensure they are well-structured, follow best practices, and incorporate sophisticated prompt engineering techniques. This skill transforms skill creation from template filling into a strategic design process.
skill-creator-agent
Creates Claude Code skills where each skill is tied to a specialist agent optimized with evidence-based prompting techniques. Use this skill when users need to create reusable skills that leverage specialized agents for consistent high-quality performance. The skill ensures that each created skill spawns an appropriately crafted agent that communicates effectively with the parent Claude Code instance using best practices.
sandbox-configurator
Configure Claude Code sandbox security with file system and network isolation boundaries
research-driven-planning
Loop 1 of the Three-Loop Integrated Development System. Research-driven requirements analysis with iterative risk mitigation through 5x pre-mortem cycles using multi-agent consensus. Feeds validated, risk-mitigated plans to parallel-swarm-implementation. Use when starting new features or projects requiring comprehensive planning with <3% failure confidence and evidence-based technology selection.
molfeat
Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.
reasoningbank-adaptive-learning-with-agentdb
Implement ReasoningBank adaptive learning with AgentDB for trajectory tracking, verdict judgment, memory distillation, and pattern recognition to build self-learning agents that improve decision-making through experience.
quick-quality-check
Lightning-fast quality check using parallel command execution. Runs theater detection, linting, security scan, and basic tests in parallel for instant feedback on code quality.
prompt-architect
Comprehensive framework for analyzing, creating, and refining prompts for AI systems. Use when creating prompts for Claude, ChatGPT, or other language models, improving existing prompts, or applying evidence-based prompt engineering techniques. Applies structural optimization, self-consistency patterns, and anti-pattern detection to transform prompts into highly effective versions.
production-readiness
Comprehensive pre-deployment validation ensuring code is production-ready. Runs complete audit pipeline, performance benchmarks, security scan, documentation check, and generates deployment checklist.
type-driven-design-rust
Type-driven design patterns in Rust - typestate, newtype, builder pattern, and compile-time guarantees
pptx-generation
Enterprise-grade PowerPoint deck generation system using evidence-based prompting techniques, workflow enforcement, and constraint-based design. Use when creating professional presentations (board decks, reports, analyses) requiring consistent visual quality, accessibility compliance, and integration of complex data from multiple sources. Implements html2pptx workflow with spatial layout optimization, validation gates, and multi-chat architecture for 30+ slide decks.
performance-analysis
Comprehensive performance analysis, bottleneck detection, and optimization recommendations for Claude Flow swarms
parallel-swarm-implementation
Loop 2 of the Three-Loop Integrated Development System. META-SKILL that dynamically compiles Loop 1 plans into agent+skill execution graphs. Queen Coordinator selects optimal agents from 86-agent registry and assigns skills (when available) or custom instructions. 9-step swarm with theater detection and reality validation. Receives plans from research-driven-planning, feeds to cicd-intelligent-recovery. Use for adaptive, theater-free implementation.
pair-programming
AI-assisted pair programming with multiple modes (driver/navigator/switch), real-time verification, quality monitoring, and comprehensive testing. Supports TDD, debugging, refactoring, and learning sessions. Features automatic role switching, continuous code review, security scanning, and performance optimization with truth-score verification.
network-security-setup
Configure Claude Code sandbox network isolation with trusted domains, custom access policies, and environment variables
ml-training-debugger
Diagnose machine learning training failures including loss divergence, mode collapse, gradient issues, architecture problems, and optimization failures. This skill spawns a specialist ML debugging ...
ml-expert
Implement machine learning solutions including model architectures, training pipelines, optimization strategies, and performance improvements. This skill spawns a specialist ML implementation agent...
micro-skill-creator
Rapidly creates atomic, focused skills optimized with evidence-based prompting, specialist agents, and systematic testing. Each micro-skill does one thing exceptionally well using self-consistency, program-of-thought, and plan-and-solve patterns. Enhanced with agent-creator principles and functionality-audit validation. Perfect for building composable workflow components.
interactive-planner
Use Claude Code's interactive question tool to gather comprehensive requirements through structured multi-select questions
intent-analyzer
Advanced intent interpretation system that analyzes user requests using cognitive science principles and extrapolates logical volition. Use when user requests are ambiguous, when deeper understanding would improve response quality, or when helping users clarify what they truly need. Applies probabilistic intent mapping, first principles decomposition, and Socratic clarification to transform vague requests into well-understood goals.
reasoningbank-intelligence
Implement adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement. Use when building self-learning agents, optimizing workflows, or implementing meta-cognitive systems.
agentdb-vector-search
Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.
agentdb-vector-search-optimization
Optimize AgentDB vector search performance using quantization for 4-32x memory reduction, HNSW indexing for 150x faster search, caching, and batch operations for scaling to millions of vectors.
agentdb-semantic-vector-search
Build semantic vector search systems with AgentDB for intelligent document retrieval, RAG applications, and knowledge bases using embedding-based similarity matching
agentdb-reinforcement-learning-training
Train AI agents using AgentDB's 9 reinforcement learning algorithms including Q-Learning, DQN, PPO, and Actor-Critic. Build self-learning agents, implement RL training loops with experience replay, and deploy optimized models to production.
agentdb-persistent-memory-patterns
Implement persistent memory patterns for AI agents using AgentDB - session memory, long-term storage, pattern learning, and context management for stateful agents, chat systems, and intelligent assistants
agentdb-performance-optimization
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
agentdb-memory-patterns
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
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