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Master guide for configuring Claude Code with templates, prompting strategies, and debugging techniques. \n \n Provides a CLAUDE.md configuration template for documenting project commands, code style rules, and agent workflows \n Includes \"Thinking\" keywords like \"step-by-step analysis\" and \"root cause investigation\" to trigger deeper reasoning in agent responses \n Offers debugging strategies for stuck agents: clearing context, using explicit instructions, and requesting verbose logs \n R

Korean document style consistency checker that detects tone, terminology, numbering, list formatting, quotation, and date format inconsistencies. \n \n Analyzes seven style categories (tone, terminology, numbers/units, lists, quotation/emphasis, dates/times, links) with priority-based detection \n Applies majority-rule principle to unify repeated expressions within a document or project, with context-aware recommendations based on document type (business, academic, technical, marketing) \n Provi

Self-contained mini-backends that bundle schema, functions, and data with clear API boundaries. \n \n Components encapsulate features like storage, payments, and notifications as reusable backend modules, reducing monolithic code and improving maintainability \n Sibling components pattern allows multiple components to work together at the same level, with the main app orchestrating calls across them \n Official component library includes authentication, storage, payments, AI, and utility compone

Battle-tested utilities for Convex backends covering relationships, authentication, filtering, sessions, and data migrations. \n \n Relationship helpers ( getOneFrom , getManyFrom , getManyVia ) simplify loading related data across tables with type safety \n Custom functions provide TypeScript-based data protection and access control as an alternative to row-level security, with automatic auth injection and role-based access patterns \n Additional utilities include filtering for complex logic, s

Systematic seven-step novel writing workflow with AI-assisted planning, consistency tracking, and quality validation. \n \n Guides writers through constitution (core principles), specification (story requirements), clarification (resolving ambiguities), planning (structure and technique), task breakdown (actionable steps), writing (AI-assisted drafting), and analysis (quality checks) \n Automatically activates genre-specific skills (Romance, Mystery, Fantasy) and writing technique skills (Dialog

Comprehensive evaluation methodology guide for ADK agents covering metrics, schemas, and iteration workflows. \n \n Provides eight evaluation criteria (tool trajectory, response matching, rubric-based scoring, hallucination detection, safety) with configurable thresholds and judge model options \n Includes evalset schema documentation with multi-turn conversation support, tool use trajectory specification, and session state initialization patterns \n Outlines the eval-fix loop: start small, run

Smart contract security review through Trail of Bits' 5-step workflow with automated scanning, visual analysis, and property documentation. \n \n Runs Slither with 70+ detectors to identify known vulnerabilities, then checks for special features like upgradeability risks, ERC conformance, and token integration patterns \n Generates three security diagrams (inheritance graph, function visibility, state variable authorization) to reveal architectural vulnerabilities that text descriptions miss \n

Comprehensive deployment guide for ADK agents across Google Cloud platforms with CI/CD, infrastructure, and troubleshooting. \n \n Covers three deployment targets (Agent Engine, Cloud Run, GKE) with a decision matrix comparing scaling, networking, session state, and cost models \n Includes quick-deploy CLI commands, scaffolded project workflows with make commands, and full CI/CD pipeline setup via GitHub Actions or Cloud Build with Workload Identity Federation \n Provides platform-specific detai

Observability setup guide for ADK agents covering tracing, logging, analytics, and third-party integrations. \n \n Four observability tiers: Cloud Trace (always enabled, distributed tracing), Prompt-Response Logging (GenAI interactions to GCS/BigQuery), BigQuery Agent Analytics (structured agent events), and third-party platforms (AgentOps, Phoenix, MLflow, Weave, Arize, Monocle, Freeplay) \n Cloud Trace automatically configured in scaffolded projects and Agent Engine deployments; captures execu

Essential reference guide for ADK agent development, covering spec-driven workflow, code preservation rules, and operational best practices. \n \n Read DESIGN_SPEC.md first as your primary source of truth; all implementation decisions must align with it \n Follow four mandatory phases: understand spec, build and implement, evaluate (using /adk-eval-guide ), and deploy with human approval \n Preserve all unrelated code when making modifications; only alter explicitly targeted segments and never c