Kid Sid@Claude Spellbook

Use when writing or debugging non-trivial Go — error handling patterns, goroutine/channel design, interface composition, generics, context propagation, or Go-specific idioms like table-driven tests and functional options.

Use when writing boto3 or AWS SDK v3 code — configuring IAM auth, reading/writing S3, designing DynamoDB access patterns, writing Lambda handlers, processing SQS batches, or troubleshooting credential and throttling errors.

Use when implementing login flows, issuing or validating JWTs, setting up OAuth2/OIDC with a provider, designing role-based or attribute-based access control, securing API endpoints, or handling token refresh and revocation.

Use when writing Python code that integrates with Azure Blob Storage, AI Search, Document Intelligence, or Key Vault — or when configuring Managed Identity auth, designing a hybrid search index, or troubleshooting Azure SDK retry behavior.

Use when setting up or debugging GitHub Actions pipelines — adding quality gates, configuring OIDC cloud auth, building matrix test runs, publishing artifacts to GHCR/PyPI/npm, or promoting builds from staging to production.

Use when choosing a Redis data structure for a use case, implementing caching or rate limiting, building pub/sub or Streams-based real-time messaging, or writing atomic operations like distributed locks.

Use when writing or optimizing a Dockerfile or docker-compose.yml for local dev, debugging containers that behave differently from local, or reducing image size for Python and Node.js apps. Not for Kubernetes resources or Helm — use containerization.

Use when building Next.js App Router pages, server and client components, fetching data in server components, configuring layouts and middleware, handling routing patterns, optimizing images and fonts, or deploying a Next.js application.

Use when writing or debugging non-trivial Python — async pitfalls, type system patterns, dataclass vs Pydantic decisions, decorator design, generator efficiency, or language-specific idioms like structural pattern matching and slots.

Use when building, wiring, or debugging an Agentex agent — choosing agent type, configuring acp.py and manifest.yaml, using adk.messages or adk.state, or resolving Windows-specific setup issues.

Use when building or refactoring Angular applications — choosing between signals, RxJS, and NgRx for state, configuring routing with guards and lazy loading, optimizing change detection, or writing TestBed component tests.

Use when adding or debugging caching in a service — choosing a cache strategy, designing TTLs, preventing stampedes, reasoning about invalidation, or configuring HTTP Cache-Control headers.

Use when structuring a FastAPI application, designing dependency injection chains, defining Pydantic v2 schemas, adding JWT authentication, or writing async route tests with httpx.

Use when designing a GraphQL schema, implementing resolvers or mutations, solving the N+1 query problem with DataLoader, setting up subscriptions, paginating with Cursor Connections, securing a GraphQL API, or choosing between GraphQL and REST.

Use when writing async MongoDB queries with Motor, designing aggregation pipelines, creating indexes, running multi-document transactions, or working with adk.state in Agentex agents.

Use when making architecture-level React decisions — component decomposition, choosing where state should live, selecting a state manager (Context vs Redux vs Zustand) or a data-fetching library (TanStack Query vs SWR), or planning a rendering-performance budget. For advanced hook patterns, use react.

Use when defining request/response schemas, writing custom validators, controlling serialization for PATCH endpoints, validating non-model data with TypeAdapter, or configuring app settings from environment variables with pydantic-settings.

Use when composing Tailwind utilities, building responsive or dark-mode layouts, defining component variants with cva, resolving conflicting classes with tailwind-merge, or configuring a custom theme.

Use when building or debugging the Agentex ADK temporal agent type — structuring workflows and activities, handling signal routing, managing state across replays, or diagnosing workflow failures and retry exhaustion. For standalone Temporal workers outside of Agentex, use general-temporal.

Use when designing a webhook delivery system, implementing HMAC signature verification on a receiver, handling retries and failures on the sender side, building idempotent webhook consumers, or testing webhook integrations locally.

Use when building or debugging LangGraph workflows — designing state graphs, adding conditional routing, wiring checkpointers, streaming tokens, implementing human-in-the-loop interrupts, or coordinating multi-agent subgraphs.

Use when building or debugging apps that call the Claude API — implementing tool use, streaming, vision, prompt caching, batch processing, extended thinking, or an agentic loop with the Anthropic SDK.

Use when installing or configuring memory_map, writing CLAUDE.md session-setup instructions, choosing what to save in memory vs history, managing or pruning history chunks, using cross-project memory, tuning compression, or troubleshooting why Claude isn't loading context at session start.

Use when writing complex PostgreSQL queries, diagnosing slow queries with EXPLAIN ANALYZE, designing indexes (B-tree, GIN, partial, composite), handling concurrent writes and lock contention, or running safe live ALTER TABLE on large tables. For schema design, normalization, or relationship modelling, use database-design.

Use when writing, reviewing, or adapting a Claude Code skill for sale on PromptBase — checking scope, audience breadth, rejection risk, description quality, examples, and setup instructions.

Use when using async SQLAlchemy 2.0 — defining models, writing queries, managing async sessions, loading relationships without N+1, or setting up and debugging Alembic migrations.

Use when building production LLM applications — designing RAG pipelines, choosing vector databases, implementing agent orchestration, optimizing cost, or adding AI safety guardrails.

Use when diagnosing a slow HTTP endpoint or high-latency service — profiling, adding an application-level cache, offloading CPU-bound work to threads or workers, or defining a latency budget. For slow queries or missing indexes, use database-design or postgresql.

Use when designing or debugging an event-driven system — choosing Kafka partitioning strategies, implementing the outbox pattern, handling dead-letter queues, ensuring idempotent consumers, or making event sourcing decisions.

Use when writing unit tests for a function or module, mocking external dependencies, practising TDD on new business logic, or enforcing a coverage target across a codebase in Python, TypeScript, or Go.

Use when building or reviewing UI components for keyboard and screen reader compatibility, adding ARIA to custom widgets, auditing a page for WCAG AA conformance, or preparing for a formal accessibility review.

Use when adding feature flag support to a service, designing a percentage-based rollout, setting up A/B experiments or multivariate tests, choosing between LaunchDarkly, Unleash, and OpenFeature, writing tests for flag-gated code, or managing flag lifecycle and cleanup.

Use when decomposing a monolith, designing inter-service communication, implementing circuit breakers or sagas, reasoning about data ownership across services, or setting up an API gateway for a distributed system.

Use when adding structured logging, instrumenting Prometheus metrics, wiring up distributed tracing with OpenTelemetry, writing alerting rules, defining SLOs and error budgets, or building a Grafana golden-signals dashboard.

Use when building or debugging OpenAI Agents SDK workflows — defining agents with tools and handoffs, wiring typed context, streaming responses, adding guardrails, or integrating with the Agentex ADK.

Use when verifying a fresh deployment, gating a CI pipeline before full test runs, checking that core user flows are reachable after a release, or building a minimal health-check suite for a new service.

Use when designing a new service from scratch, writing a tech spec or RFC, selecting a database or communication pattern, estimating capacity, or reviewing a design for scalability and reliability gaps.

Use when choosing a testing model for a new project, auditing a test suite that is slow or provides low confidence, setting coverage targets, or writing a QA test plan for a release.

Use when building or debugging data pipelines with Airflow or Prefect, writing dbt models or tests, designing incremental loads, implementing idempotent ETL/ELT jobs, validating data quality, or orchestrating multi-step data workflows.

Use when designing error hierarchies, propagating errors across service boundaries, implementing retry and backoff logic, writing structured error responses for APIs, or making error handling consistent across a Python, TypeScript, or Go codebase.

Use when building real-time features with WebSockets or SSE — choosing between the two, implementing connection management and heartbeats, scaling broadcasts across workers with Redis, handling backpressure, writing tests for streaming endpoints, or debugging connection drops and missed events.

Use when building a RAG pipeline that ingests PDFs, Excel, CSV, or images — especially when debugging silent data loss, choosing between OCR tools, or handling edge cases like scanned pages, merged cells, or embedded charts.

Use when designing a relational schema — normalization (1NF–3NF), primary key strategy, modelling relationships, naming conventions, or planning a zero-downtime migration with the expand-contract pattern. For writing and tuning queries, indexes, or EXPLAIN ANALYZE, use postgresql.

Use when installing claude-spellbook for the first time, setting up skills/agents/commands globally or into a specific project, registering the memory_map MCP server, enabling lifecycle hooks, or migrating an existing install to a new machine.

Use when reviewing code for quality, naming a class or function, choosing a design pattern, refactoring a code smell, or establishing coding conventions for a new project.

Use when configuring Kubernetes resources (Deployment, Service, Ingress, ConfigMap, Secret, HPA), sizing pod resource requests and limits, setting securityContext, or packaging a service with Helm charts. Not for writing Dockerfiles or docker-compose files — use docker.

Use when building or debugging standalone Temporal workers in Python outside of Agentex — structuring workflows and activities, enforcing determinism, handling retries and timeouts, managing state across replays, or diagnosing workflow failures. For Temporal-based Agentex agents, use temporal.

Use when writing Playwright E2E tests for critical user journeys, setting up post-deployment smoke tests, debugging flaky browser automation, or implementing BDD feature files with Gherkin.

Use when implementing reliable message processing with Azure Service Bus — choosing between queues and topics, configuring peek-lock settlement, handling dead-lettered messages, or enforcing ordered processing with sessions.

Use when triaging a production alert, writing a postmortem, creating or updating a runbook, classifying incident severity, or setting up on-call escalation paths.

Use when writing tests that hit a real database or broker, setting up Testcontainers for a project, testing HTTP endpoints end-to-end within the service boundary, or implementing contract tests between two services.

Use when load testing a service before launch or after a significant traffic change — writing k6 or Locust scripts, setting SLO-based pass/fail thresholds, diagnosing bottlenecks under load, or integrating performance tests into CI.

Use when choosing a branching strategy, writing a commit message, opening or reviewing a pull request, setting up commit linting, or tagging a versioned release.

Use when choosing a deployment strategy for a release, setting up canary or blue/green rollouts, adding feature flags to decouple deployment from release, coordinating a zero-downtime database migration, or defining rollback criteria and procedures.

Use when writing a PRD, drafting user stories with acceptance criteria, breaking an epic into sprint-sized vertical slices, story pointing in planning poker, or defining a team's Definition of Done.

Use when writing Terraform for cloud resources, setting up remote state, structuring modules for reuse, managing multiple environments, reviewing a plan before apply, or importing and resolving state drift.

Use when writing a README, documenting an API with OpenAPI, drafting a runbook for on-call engineers, authoring a technical spec or ADR, or setting up docs-as-code with auto-deploy to GitHub Pages.