ac-task-executor
Execute implementation tasks in autonomous coding. Use when running feature implementations, executing build tasks, processing feature queue, or orchestrating task completion.
ac-tdd-runner
Run TDD cycle for feature implementation. Use when implementing features with RED-GREEN-REFACTOR, running test-driven development, automating TDD workflow, or ensuring test-first development.
ac-test-generator
Generate tests for features using TDD approach. Use when creating test files, generating test cases, implementing RED phase of TDD, or scaffolding test infrastructure.
ac-workspace-manager
Manage git worktrees for isolated development. Use when creating isolated workspaces, managing parallel development, handling worktree lifecycle, or merging completed work.
accessibility-testing
Accessibility testing with axe-core and Playwright. Use when checking WCAG compliance, finding a11y issues, ensuring keyboard navigation, or testing screen reader compatibility.
ack-resources
AWS Controllers for Kubernetes (ACK) for Kubernetes-native AWS resource management. Use when managing AWS resources via kubectl, implementing GitOps for infrastructure, creating self-service developer platforms, integrating AWS services with EKS workloads, or adopting existing AWS resources into Kubernetes.
agent-cost-optimizer
Real-time cost tracking, budget enforcement, and ROI measurement for AI agent operations. Track token usage, predict costs, enforce budget caps ($50-70/month typical), optimize model selection, cache results, measure cost-to-value. Use when tracking AI costs, preventing budget overruns, optimizing spend, measuring ROI, or ensuring cost-effective AI operations.
agent-memory-system
Persistent memory architecture for AI agents across sessions. Episodic memory (past events), procedural memory (learned skills), semantic memory (knowledge graph), short-term memory (active context). Use when implementing cross-session persistence, skill learning, context preservation, personalization, or building truly adaptive AI systems with long-term memory.
alphavantage-api
Alpha Vantage financial API for stocks, forex, crypto, and 50+ technical indicators. Use when fetching time series data, technical analysis, fundamentals, economic indicators, or news sentiment.
analysis
Comprehensive analysis operations for code, skills, processes, data, and patterns. Task-based operations with pattern recognition, metrics calculation, trend identification, and actionable insights generation. Use when analyzing code quality, reviewing skill effectiveness, identifying process improvements, extracting patterns, or generating insights from data.
anthropic-docs-updater
Automated documentation update mechanism for anthropic-expert skill. Five-step workflow from update detection through documentation fetching and processing to skill integration and validation. Use when updating Anthropic documentation, checking for new releases, fetching latest docs, keeping anthropic-expert current, or synchronizing with Anthropic product changes.
anthropic-expert
Comprehensive Anthropic product expertise covering Claude models, Claude API, Python SDK, Agent SDK, Claude Code, and Model Context Protocol. Six integrated capabilities with complete documentation, searchable references, code examples, and cross-product integration patterns. Use when working with Claude API, building agents, using SDKs, developing with Claude Code, integrating MCP servers, learning Anthropic products, optimizing costs, implementing Anthropic features, managing context, using Opus 4.5, or implementing advanced tool patterns.
api-testing
REST and GraphQL API testing with Playwright. Use when testing APIs, mocking endpoints, validating responses, or integrating API tests with E2E flows.
auto-claude-build
Auto-Claude autonomous build system. Use when running builds, understanding agent workflow, managing parallel execution, or troubleshooting build issues.
auto-claude-cli
Auto-Claude CLI command reference and usage patterns. Use when running specs, managing builds, checking status, or using CLI commands for autonomous coding tasks.
auto-claude-memory
Auto-Claude Graphiti memory system configuration and usage. Use when setting up memory persistence, configuring LLM/embedding providers, querying knowledge graph, or optimizing memory performance.
auto-claude-optimization
Auto-Claude performance optimization and cost management. Use when optimizing token usage, reducing API costs, improving build speed, or tuning agent performance.
auto-claude-setup
Complete Auto-Claude installation and setup guide for all platforms. Use when installing Auto-Claude on WSL, Windows, Linux, or macOS, setting up development environment, or troubleshooting installation issues.
auto-claude-spec
Auto-Claude spec creation and management. Use when creating feature specs, understanding spec pipeline phases, modifying requirements, or managing spec lifecycle.
auto-claude-troubleshooting
Auto-Claude debugging and troubleshooting guide. Use when fixing installation issues, debugging build failures, resolving agent errors, or diagnosing performance problems.
auto-claude-updater
Auto-update system for Auto-Claude skills and documentation. Use when checking for updates, synchronizing with upstream, updating skills automatically, or managing version compatibility.
auto-claude-workspace
Auto-Claude workspace and git worktree management. Use when reviewing changes, merging builds, managing branches, or understanding isolation strategy.
auto-updater
Automatically apply improvements to skills and the ecosystem based on system-reviewer findings and best-practices-learner insights. Workflow for automated improvement identification, priority assessment, safe application, validation, and rollback capability. Use when applying systematic improvements, automating enhancement cycles, bulk updating multiple skills, or implementing ecosystem-wide improvements.
autonomous-cost-optimizer
Token and cost optimization for autonomous coding. Use when tracking token usage, optimizing API costs, managing budgets, or improving efficiency.
autonomous-loop
Main orchestration loop for autonomous coding. Use when running autonomous sessions, orchestrating feature completion, managing continuous loops, or coordinating agent lifecycle.
autonomous-master
Master orchestrator for autonomous coding projects. Use when starting autonomous projects, continuing sessions, checking status, or running complete autonomous workflows.
autonomous-opus-loop
Autonomous Claude Code operation using Opus 4.5 for intelligent continuation decisions. Use when running long tasks, multi-step implementations, overnight development, or any workflow requiring continuous autonomous operation without human intervention.
autonomous-session-manager
Session lifecycle management for autonomous coding. Use when starting new coding sessions, resuming work, detecting session type (init vs continue), or managing auto-continuation between sessions.
bedrock-agentcore-deployment
Amazon Bedrock AgentCore deployment patterns for production AI agents. Covers starter toolkit, direct code deploy, container deploy, CI/CD pipelines, and infrastructure as code. Use when deploying agents to production, setting up CI/CD, or managing agent infrastructure.
bedrock-agentcore-evaluations
Amazon Bedrock AgentCore Evaluations for testing and monitoring AI agent quality. 13 built-in evaluators plus custom LLM-as-Judge patterns. Use when testing agents, monitoring production quality, setting up alerts, or validating agent behavior.
bedrock-agentcore-memory
Amazon Bedrock AgentCore Memory for persistent agent knowledge across sessions. Episodic memory for learning from interactions, short-term for session context. Use when building agents that remember user preferences, learn from conversations, or maintain context across sessions.
bedrock-agentcore-multi-agent
Amazon Bedrock AgentCore multi-agent orchestration with Agent-to-Agent (A2A) protocol. Supervisor-worker patterns, agent collaboration, and hierarchical delegation. Use when building multi-agent systems, orchestrating specialized agents, or implementing complex workflows.
bedrock-agentcore-policy
Amazon Bedrock AgentCore Policy for defining agent boundaries using natural language and Cedar. Deterministic policy enforcement at the Gateway level. Use when setting agent guardrails, access control, tool permissions, or compliance rules.
bedrock-agentcore
Amazon Bedrock AgentCore platform for building, deploying, and operating production AI agents. Covers Runtime, Gateway, Browser, Code Interpreter, and Identity services. Use when building Bedrock agents, deploying AI agents to production, or integrating with AgentCore services.
bedrock-agents
Amazon Bedrock Agents for building autonomous AI agents with foundation model orchestration, action groups, knowledge bases, and session management. Use when creating AI agents, orchestrating multi-step workflows, integrating tools with LLMs, building conversational agents, implementing RAG patterns, managing agent sessions, deploying production agents, or connecting knowledge bases to agents.
bedrock-automated-reasoning
Amazon Bedrock Automated Reasoning for mathematical verification of AI responses against formal policy rules with up to 99% accuracy. Use when validating healthcare protocols, financial compliance, legal regulations, insurance policies, or any domain requiring deterministic verification of AI-generated content.
bedrock-fine-tuning
Amazon Bedrock Model Customization with fine-tuning, continued pre-training, reinforcement fine-tuning (NEW 2025 - 66% accuracy gains), and distillation. Create customization jobs, monitor training, deploy custom models, and evaluate performance. Use when customizing Claude, Titan, or other Bedrock models for domain-specific tasks, adapting to proprietary data, improving accuracy on specialized workflows, or distilling large models to smaller ones.
bedrock-flows
Build visual AI workflows with Amazon Bedrock Flows. Create flows with prompt nodes, knowledge bases, Lambda, inline code, condition branching, iterators, collectors, and DoWhile loops. Version management, aliases, deployment. Use when building multi-step AI workflows, orchestrating models and services, creating condition-based routing, implementing iterative processing, or deploying production AI pipelines.
bedrock-guardrails
Comprehensive Amazon Bedrock Guardrails implementation for AI safety with 6 safeguard policies (content filters, PII redaction, topic denial, word filters, contextual grounding, automated reasoning). Use when implementing content moderation, detecting prompt attacks, preventing hallucinations, protecting sensitive data, enforcing compliance policies, or securing generative AI applications with mathematical verification.
bedrock-inference
Amazon Bedrock Runtime API for model inference including Claude, Nova, Titan, and third-party models. Covers invoke-model, converse API, streaming responses, token counting, async invocation, and guardrails. Use when invoking foundation models, building conversational AI, streaming model responses, optimizing token usage, or implementing runtime guardrails.
bedrock-knowledge-bases
Amazon Bedrock Knowledge Bases for RAG (Retrieval-Augmented Generation). Create knowledge bases with vector stores, ingest data from S3/web/Confluence/SharePoint, configure chunking strategies, query with retrieve and generate APIs, manage sessions. Use when building RAG applications, implementing semantic search, creating document Q&A systems, integrating knowledge bases with agents, optimizing chunking for accuracy, or querying enterprise knowledge.
bedrock-prompts
Amazon Bedrock Prompt Management for creating, versioning, and managing prompt templates with variables, multi-variant A/B testing, and flow integration. Use when creating reusable prompt templates, managing prompt versions, implementing A/B testing for prompts, integrating prompts with Bedrock Flows, optimizing prompt engineering, or building production prompt catalogs.
codex-auth
Setup and manage OpenAI Codex CLI authentication including ChatGPT Plus/Pro OAuth, API keys, and multi-account management. Use when configuring Codex access, switching accounts, or troubleshooting authentication.
best-practices-learner
Extract learnings and best practices from skill development experience, review findings, and pattern analysis. Task-based operations for pattern extraction, learning documentation, guideline updates, knowledge sharing, and continuous improvement. Use when extracting learnings from completed skills, updating best practices, improving development process, or feeding continuous improvement cycle.
boto3-ecs
AWS Boto3 SDK patterns for Amazon ECS cluster management, task definitions, services, and Fargate deployments. Use when working with ECS clusters, managing task definitions, deploying services, running one-off tasks, monitoring deployments, or integrating ECS with Python applications.
boto3-eks
AWS Boto3 SDK patterns for Amazon EKS cluster management, node groups, authentication tokens, and Kubernetes client integration. Use when working with EKS clusters, managing node groups, generating kubeconfig, creating authentication tokens, integrating Kubernetes Python client, managing Fargate profiles, or implementing IRSA authentication.
browser-e2e-tester
Browser-based E2E testing for feature verification. Use when running end-to-end tests, validating features in browser, verifying user flows, or testing feature completion.
browser-use-integration
Self-hosted AI browser automation using Browser Use with any LLM (Claude, GPT, Ollama). Use when building web scraping agents, data extraction pipelines, self-hosted automation, or when you need flexibility without API rate limits.
cdk8s-apps
CDK8s for type-safe Kubernetes manifests using Python. Use when building complex K8s applications programmatically, generating manifests from code, creating reusable infrastructure patterns, or managing multi-environment deployments.
checkpoint-manager
State snapshots and rollback for safe experimentation. Use when creating checkpoints, rolling back changes, managing recovery points, or implementing safe experimentation.
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