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Comprehensive toolkit for detecting and eliminating "AI slop" - generic, low-quality AI-generated patterns in natural language, code, and design. Use when reviewing or improving content quality, preventing generic AI patterns, cleaning up existing content, or enforcing quality standards in writing, code, or design work.

Automatically discover agentic workflow skills when building AI agents, implementing tool use patterns, managing context windows, decomposing complex tasks, or designing multi-step autonomous workflows. Activates for agentic AI development.

Automatically discover API design skills when working with REST APIs, GraphQL schemas, API authentication, OAuth, JWT, rate limiting, API versioning, error handling, or endpoint design. Activates for backend API development tasks.

Automatically discover CI/CD and automation skills when working with GitHub Actions, Jenkins, GitLab CI, pipelines, continuous integration, continuous deployment, or automated testing. Activates for CI/CD development tasks.

Automatically discover cryptography skills when working with encryption, TLS, certificates, PKI, and security

Automatically discover data pipeline and ETL skills when working with ETL, data pipelines, streaming, batch processing, data validation, or pipeline orchestration. Activates for data development tasks.

Automatically discover database skills when working with SQL, PostgreSQL, MongoDB, Redis, database schema design, query optimization, migrations, connection pooling, ORMs, or database selection. Activates for database design, optimization, and implementation tasks.

Automatically discover debugging and profiling skills when working with GDB, LLDB, breakpoints, profiling, stack traces, memory leaks, core dumps, or performance profiling. Activates for debugging development tasks.

Core orchestration for disciplined AI-assisted development. Auto-invokes when starting new features, implementing tasks, or working on any development that should follow the spec-first, test-driven process. Triggers on task planning, feature implementation, bug fixes, refactoring work, or when beads/bd tasks are referenced.

Use when working from decomposed task files or Beads issues created by spec-decompose. Guides proper task pickup, context loading, implementation, and handoff. Triggers when working with decomposed tasks, task files in docs/tasks/, or Beads issues with hole labels.

Architecture Decision Record format and process. Use when making architectural decisions, documenting technical choices, evaluating alternatives, or when asked about past decisions. Triggers on ADR creation, architecture discussions, or "why did we choose" questions.

Specification authoring with traceable paragraph IDs. Use when writing specifications, updating specs, adding new features to specs, or when implementing code that needs to reference spec paragraphs. Triggers on spec creation, implementation traceability, or coverage verification.

Test-driven development methodology with four test types - unit, integration, property, and e2e. Use when writing tests, planning test strategy, implementing features test-first, or verifying test coverage. Triggers when tests are mentioned, test files are being created, or test-first approach is needed.

Step-by-step tutorial for adding a new lightweight JIT CUDA kernel to sglang's jit_kernel module