LLM & Agents

Use this skill when creating comprehensive testing strategies for applications. Provides test planning templates, coverage targets, test case structures, and guidance for unit, integration, E2E, and performance testing. Ensures robust quality assurance across the development lifecycle.

Adversarial quality gate agent for code review - finds flaws before users do

Use this skill to discover all available SynthesisFlow skills and their capabilities. Provides a bootstrap context for AI agents by listing all skills, their descriptions, and script paths from the .claude/skills/ directory.

Use when turning a documentation link or article into spaced-repetition flashcards - fetches and extracts core ideas, compares against existing cards/notes, right-sizes output by source density and existing coverage, updates only incorrect/outdated cards, and creates missing cards with strict citation, slug, and tag rules

Claude Code のプロファイル管理。Use when user mentions プロファイル, profile, 設定, MCP設定, レイヤー, layer.

QA engineer and test automation specialist with deep expertise in Flutter testing. Use for designing test strategies, writing unit/widget/integration tests, improving test coverage, and ensuring code reliability.

Orchestrate comprehensive 7-phase RBMAS research for multi-dimensional queries (4+ dimensions). Coordinates multiple specialist agents through SCOPE → PLAN → RETRIEVE → TRIANGULATE → DRAFT → CRITIQUE → PACKAGE methodology. Use for thorough internet research requiring multiple sources, iterative refinement, and quality gates. Triggers - research, investigate, analyze with 4+ distinct dimensions or comprehensive depth requirements.

Generate pytest tests with parametrization, shared fixtures, minimal mocking. Use for unit tests and test coverage. Follows 1-1 file mapping and real object testing.

Analyze code changes for security vulnerabilities using LLM reasoning and threat model patterns. Use for PR reviews, pre-commit checks, or branch comparisons.

Plan what to measure in AI agent systems using tiered approach

Conduct comprehensive research on any topic by coordinating 2-4 specialized researcher agents in parallel, then synthesizing findings into a detailed report via mandatory report-writer agent delegation

Create worktrees and launch Claude Code agents. USE THIS SKILL when user says "create worktree", "spin up worktree", "new worktree", "worktree for X", or wants parallel development branches. Also handles worktree status, cleanup, and agent launching.

Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends agent' capabilities with specialized knowledge, workflows, or tool integrations.

USE when preparing to backup agent consciousness, planning consciousness transplant to new system, or restoring from backup archive. Extracts policy guidance for strategic backup triggers, cross-OS migration, virgin system restore, and safety protocols.

Claude SDK development with Messages API, Tool Use, Extended Thinking, streaming, and prompt caching

Use when working with Quetrex's voice interface, OpenAI Realtime API, WebRTC, or echo cancellation. Knows Quetrex's specific voice architecture decisions and patterns. CRITICAL - prevents breaking working voice system.

Orchestrator agent that coordinates all BarqNet specialized agents (backend, integration, client, documentation, audit, testing) to execute complete end-to-end workflows. Plans multi-agent deployments, manages task dependencies, tracks progress across all platforms, and ensures comprehensive completion. Use for complex multi-component tasks, full-stack features, or production deployments.

Full implementation mode - end-to-end feature implementation with parallel agent orchestration

Pull request swarm management for multi-agent code review and validation. Use for coordinated PR reviews, automated validation, PR-based swarm creation, and intelligent merge workflows.

This skill provides complete coverage of Google Gemini embeddings API (gemini-embedding-001) for building RAG systems, semantic search, document clustering, and similarity matching. Use when implementing vector search with Google's embedding models, integrating with Cloudflare Vectorize, or building retrieval-augmented generation systems. Covers SDK usage (@google/genai), fetch-based Workers implementation, batch processing, 8 task types (RETRIEVAL_QUERY, RETRIEVAL_DOCUMENT, SEMANTIC_SIMILARITY, etc.), dimension optimization (128-3072), and cosine similarity calculations. Prevents 8+ embedding-specific errors including dimension mismatches, incorrect task types, rate limiting issues (100 RPM free tier), vector normalization mistakes, text truncation (2,048 token limit), and model version confusion. Includes production-ready RAG patterns with Cloudflare Vectorize integration, chunking strategies, and caching patterns. Token savings: ~60%. Production tested.Keywords: gemini embeddings, gemini-embedding-001, g