tools
List all available Claude Code tools with their parameters and capabilities. Use to understand current tool availability before starting a task.
tool-design
Create custom tools using the @tool decorator for domain-specific agents. Use when building agent-specific tools, implementing MCP servers, or creating in-memory tools with the Agent SDK.
test
Run project test suite and report results in structured JSON format. Use to validate implementation before commit or review.
test-suite-setup
Set up test validation commands for any project type. Use when configuring test runners, setting up validation commands for a new project, or enabling closed-loop agent workflows.
test-e2e
Execute end-to-end test specification and report results. Use after implementation to validate user journeys before review.
template-meta-prompt-creation
Create Level 6 template meta-prompts that generate other prompts. Use when building prompt generators, designing high-leverage meta-prompts, or creating templates that scaffold other prompts.
template-engineering
Guide creation of meta-prompt templates that encode engineering workflows into reusable, scalable units. Use when creating slash commands that generate plans, designing workflow templates, or encoding team best practices into agentic prompts.
task-based-multiagent
Set up task-based multi-agent systems with shared task files. Use when setting up parallel agent execution, designing worktree isolation patterns, or implementing task file coordination.
system-prompt-engineering
Design effective system prompts for custom agents. Use when creating agent system prompts, defining agent identity and rules, or designing high-impact prompts that shape agent behavior.
swimlane-visualization
Design swimlane UI patterns for visualizing ADW execution. Use when building observability dashboards, monitoring agent workflows, or creating real-time status displays.
inverse-conway
Align architecture and team structure using inverse Conway maneuver
start-adw
Start an AI Developer Workflow with composable steps. Use when executing plan_build, plan_build_review, or plan_build_review_fix workflows.
standard-out-setup
Add console output and logging to make errors visible to agents. Standard out is a critical leverage point - without it, agents cannot see errors or understand application state. Use when agents fail silently, when debugging agentic workflows, or when setting up a new codebase for agentic coding.
ship
Validate state and merge branch to main for production deployment. Use as final ZTE step after all checks pass.
self-improve-prompt-design
Write self-improve prompts that sync expertise files with codebase reality. Use when creating maintenance workflows for agent experts, designing validation logic, or implementing the LEARN step of Act-Learn-Reuse.
seed-expertise
Generate initial expertise.yaml from codebase exploration. Use to bootstrap a new agent expert's mental model.
scout-and-build
Execute the scout-then-build pattern for a feature implementation. Use when you need to explore the codebase before implementing changes.
scaffold-layer
Create minimum viable agentic layer structure for a project. Use when adding agentic capabilities to a new project.
review-workflow-design
Design spec-based review workflows with visual proof and issue classification. Use when setting up review processes, validating against specifications, or implementing screenshot-based visual validation.
resolve-failed-test
Analyze a failed test, fix the underlying issue, and verify the fix. Use after /test reports failures.
resolve-failed-e2e-test
Analyze a failed E2E test, fix the underlying issue, and verify the fix. Use after /test-e2e reports failures.
reduce-delegate-framework
Apply R&D framework to optimize prompts and context. Use when optimizing context window usage, reducing prompt size, delegating to specialized agents, or applying systematic context management.
query-expert
Ask an agent expert a question using its expertise mental model. Use for quick domain-specific answers without code exploration.
pull-request
Create a GitHub pull request with full context linking back to the original issue. Use after implementation is complete and ready for review.
prompt-section-design
Design composable prompt sections for building agentic prompts. Use when creating reusable prompt components, designing LEGO-block prompt sections, or structuring prompts for the stakeholder trifecta.
prompt-level-selection
Guide selection of appropriate prompt level for a task. Use when choosing between simple prompts and complex workflows, applying the seven levels framework, or matching task complexity to prompt investment.
product-expert-design
Design user-facing agent experts for adaptive UX and personalization. Use when building product features that learn from user behavior, creating per-user expertise files, or implementing AI-driven personalization.
quality-attributes-taxonomy
The "-ilities" framework for non-functional requirements. Use when defining NFRs, evaluating architecture trade-offs, or ensuring quality attributes are addressed in system design. Covers scalability, reliability, availability, performance, security, maintainability, and more.
platform-design
Design Internal Developer Platforms, self-service capabilities, and golden paths
optimize-llm
Get LLM optimization recommendations for serving latency, inference costs, and throughput improvements
observability-patterns
Use when implementing observability strategy, correlating signals, or designing monitoring systems. Covers the three pillars (logs, metrics, traces) and their integration.
multi-region-deployment
Use when designing globally distributed systems, multi-region architectures, or disaster recovery strategies. Covers region selection, active-active vs active-passive, data replication, and failover patterns.
mtls-service-mesh
Use when implementing service-to-service security, mTLS, or service mesh patterns. Covers mutual TLS, Istio, Linkerd, certificate management, and service mesh security configurations.
mock-interview
Run an interactive system design mock interview - simulates a real interview with problem statement, follow-ups, and structured feedback
ml-system-design
End-to-end ML system design for production. Use when designing ML pipelines, feature stores, model training infrastructure, or serving systems. Covers the complete lifecycle from data ingestion to model deployment and monitoring.
ml-pipeline
Design an ML system for a problem
ml-inference-optimization
ML inference latency optimization, model compression, distillation, caching strategies, and edge deployment patterns. Use when optimizing inference performance, reducing model size, or deploying ML at the edge.
llm-serving-patterns
LLM inference infrastructure, serving frameworks (vLLM, TGI, TensorRT-LLM), quantization techniques, batching strategies, and streaming response patterns. Use when designing LLM serving infrastructure, optimizing inference latency, or scaling LLM deployments.
latency-optimization
Use when optimizing end-to-end latency, reducing response times, or improving performance for latency-sensitive applications. Covers latency budgets, geographic routing, protocol optimization, and latency measurement techniques.
latency-budget
Calculate and allocate latency budgets for a system - breaks down end-to-end latency into component budgets with optimization recommendations
internal-developer-platform
Use when designing Internal Developer Platforms (IDPs), building platform teams, or improving developer experience. Covers platform engineering principles, Backstage, portal design, and platform team structures.
instrumentation-planning
Plan instrumentation strategy before implementation, covering what to instrument, naming conventions, cardinality management, and instrumentation budget
incident-response
Use when designing incident management processes, creating runbooks, or establishing on-call practices. Covers incident lifecycle, communication, and postmortems.
improve-expertise
Run self-improve on an expert's mental model to sync with codebase. Use periodically to keep expertise files accurate.
idempotency-patterns
Use when designing idempotent APIs, handling retries safely, or preventing duplicate operations. Covers idempotency keys, at-most-once semantics, and duplicate prevention.
golden-paths
Use when designing standardized development workflows, paved roads, or opinionated defaults. Covers golden path patterns, template design, developer workflow optimization, and guardrails.
gameday-planning
Use when planning GameDay exercises, designing failure scenarios, or conducting chaos drills. Covers GameDay preparation, execution, and follow-up.
explain
Explain a systems design concept
etl-elt-patterns
Use when designing data pipelines, choosing between ETL and ELT approaches, or implementing data transformation patterns. Covers modern data pipeline architecture.
estimation-techniques
Back-of-envelope calculations for system design. Use when estimating QPS, storage, bandwidth, or latency for capacity planning. Includes latency numbers every programmer should know and common estimation patterns.
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