Claude Skills by a5c-ai

Use when completing tasks, implementing major features, or before merging to verify work meets requirements.

Use when executing implementation plans with independent tasks in the current session. Dispatches fresh subagent per task.

Use when starting feature work that needs isolation from current workspace or before executing implementation plans.

Use when starting any conversation. Establishes how to find and use skills, requiring skill invocation before any response.

Use when you have a spec or requirements for a multi-step task, before touching code. Creates bite-sized TDD implementation plans with dependency tracking.

Use when creating new skills, editing existing skills, or verifying skills work before deployment.

Drive feature development using Outside-In TDD with Hexagonal Architecture. Design emerges through inline code, in-memory fakes, interface extraction, and deferred I/O. Use when building features, writing tests, or structuring backend services. Triggers on: TDD, outside-in, hexagonal, ports and adapters, emergent design, acceptance test, component test, walking skeleton, in-memory fakes, component, contract test, adapter, fast tests, sub-second feedback. Language-agnostic (Go, Rust, Python, T...

Generates DrawIO XML diagrams for Amazon Web Services architectures from text descriptions or images. Analyzes existing .drawio files to extract AWS components. Use for AWS architecture diagrams, cloud infrastructure documentation, or when converting AWS diagram images to editable DrawIO format.

Generates DrawIO XML diagrams for Google Cloud Platform architectures from text descriptions or images. Analyzes existing .drawio files to extract GCP components. Use for GCP architecture diagrams, cloud infrastructure documentation, or when converting GCP diagram images to editable DrawIO format.

This skill should be used when the user asks to "find skills in the wild", "assimilate popular workflows", "discover SKILL.md files in repos", "research external skills", "find workflow patterns", "survey the skill landscape", "what skills exist out there", or wants to investigate public repositories for extractable processes, babysitter plugins, and reusable procedural insights. Searches GitHub for SKILL.md files, classifies repos by archetype, and maintains structured research under docs/re...

This skill should be used when the user asks to "babysit issues", "work on assigned issues", "check a5c-agent issues", "process babysitter issues", or wants to find and work on open GitHub issues assigned to a5c-agent in the babysitter repo.

Discover public GitHub repositories that import defineTask from @a5c-ai/babysitter-sdk and maintain a deduplicated catalog of those repositories in docs/repo-with-babysitter-processes.md. Excludes any repo named "babysitter" (to filter out forks of this monorepo). Invoke when asked to find, discover, catalog, or refresh "repos using babysitter", "babysitter in the wild", or "who else is using babysitter".

This skill should be used when the user asks to "fix pipelines", "fix CI", "check staging pipelines", "fix failing workflows", "fix failing actions", or wants to find and fix failing GitHub Actions workflows on the staging branch of the babysitter repo.

For a repository in the babysitter-users catalog, locate its babysitter processes and any committed runs (.a5c/runs/<runId>/) and perform a retrospective on a chosen run -- what went well, what failed, process suggestions, quality of effect design, breakpoint patterns -- mirroring the /babysitter:retrospect workflow but applied to an external repo. Invoke when asked to "retrospect on repo X's run", "analyze how someone else used babysitter", or "review an external babysitter run".

Orchestrate via @babysitter. Use this skill when asked to babysit a run, orchestrate a process or whenever it is called explicitly. (babysit, babysitter, orchestrate, orchestrate a run, workflow, etc.)

Execute Vitest and Playwright test suites with result collection and failure analysis.

Interactive PRD brainstorming and drafting with quality-gated refinement.

Context window monitoring and budget management. Keeps orchestrator at 15-30% context usage while subagents get full 200k tokens. Provides warnings at thresholds, context-aware summarization triggers, and wave-level budget planning.

Architect code review with DRY, YAGNI, abstraction, and test coverage principle enforcement

Git worktree management for safe, isolated feature development. Creates, manages, and cleans up worktrees with branch naming and dependency setup.

Maintain progress.md with session logs, test results, error records, and phase status indicators.

Structured code quality assessment with Conventional Comments format, scaled review depth, and soft-gating verdicts preserving user autonomy.

Test-first development practice where test specifications are written before production code, integrated into plan tasks as mandatory first sub-steps.

Convert technical plans into actionable development tasks with dependency graphs, effort estimates, and parallelization opportunities.

Use when starting any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes. Requires root cause investigation first.

Use when implementing any feature or bugfix, before writing implementation code. Enforces RED-GREEN-REFACTOR cycle.

Use when about to claim work is complete, fixed, or passing, before committing or creating PRs. Evidence before assertions.

Microsoft AutoGen multi-agent configuration for conversational AI systems

Autonomously engage with complex coding tasks — multi-step planning, tool use, self-correction, and solution verification without human intervention.

Autonomous research engineering — web search orchestration, source synthesis, hypothesis formation, and structured research report generation.

Chain-of-thought and step-by-step reasoning prompts for complex problem solving

Chroma local vector database setup and operations for development and production

Perform closed-book frontier reasoning — complex problem solving from internalized knowledge without external retrieval or tool use.

Constitutional AI and safety guardrail prompts for aligned LLM behavior

Content moderation API integration using OpenAI Moderation, Perspective API, and others

CrewAI multi-agent orchestration setup for collaborative AI systems

Entity and fact extraction for user profiling and personalization

Few-shot example generation and optimization for improved LLM performance

Apply general knowledge reasoning across diverse domains — trivia, commonsense inference, analogy, and factual question answering.

Guardrails AI validation framework setup for LLM applications. Implement input/output validation, safety checks, and structured output enforcement.

Haystack NLP pipeline configuration for document processing and QA

Hugging Face transformer model fine-tuning and inference for intent classification

LangChain chain composition including SequentialChain, RouterChain, and LCEL patterns

LangChain memory integration including ConversationBufferMemory, ConversationSummaryMemory, and vector-based memory

LangChain ReAct agent implementation with tool binding for reasoning and action loops

LangChain retriever implementation with various retrieval strategies for RAG applications

LangChain tool creation and integration utilities for agent systems

LangFuse LLM observability integration for tracing, analytics, and cost tracking

LangGraph checkpoint and persistence configuration for stateful workflow management