agent-kernel

Agent Kernel architectural principles, core abstractions, and design patterns. Use this skill when you need to understand the codebase structure, how components interact, or before making changes to core functionality. Covers Session, Agent, Runner, Module, Runtime, AgentService, AKConfig, tools, hooks, multimodal, conversation threads, the adapter pattern, and the AWS ECS containerized deployment classes (ECSIOHandler, ECSOutputConsumer, ECSAgentRunner, ECSSQSConsumer, QueueConsumer, ThreadRunner).

Step-by-step guide for adding a new multimodal attachment storage backend to Agent Kernel. Use this skill when you need to integrate a new storage service (beyond in-memory, Redis, and DynamoDB) for persisting image and file attachments. Covers implementing the AttachmentStore interface, factory registration, configuration, and testing.

Testing conventions, patterns, and automation for Agent Kernel development. Use this skill when writing tests for new features, debugging test failures, or understanding the test infrastructure. Covers pytest patterns, async testing, mocking, the built-in Test framework, and CI/CD test workflows.

Review a GitHub pull request against Agent Kernel's architecture, design principles, code quality standards, and testing conventions, then post the findings back to the PR as review comments. When the PR contains spec documents (design.md, spec.md, or plan.md under docs/specs/), they are reviewed first — each at its own stage's altitude — and any implementation is checked against them. Use this skill when given a PR number or URL to review, e.g. "review PR 342" or "run a review on https://github.com/yaalalabs/agent-kernel/pull/342".

Write the spec documents for a planned Agent Kernel change under docs/specs/<ticket>/ in three ordered stages: a concise point-form design spec (design.md) that a maintainer reviews first, then a detailed implementation spec (spec.md) once the design is approved, then a concise implementation plan (plan.md). Use this skill when asked to write or update a design, spec, or plan for a feature, refactor, or fix before it is implemented, e.g. "write a design spec for AK-52" or "spec out the shared driver extraction".

Add capabilities to an existing Agent Kernel project. This skill guides you through adding guardrails, tracing/observability, session persistence, knowledge bases, MCP server, A2A server, pre/post hooks, multimodal support, and conversation thread support. Session persistence supports Redis, DynamoDB (AWS), Cosmos DB (Azure), and Firestore (GCP). Conversation threads support in-memory, Redis, DynamoDB (AWS), Firestore (GCP), and Cosmos DB (Azure) backends. Generates configuration and code changes needed.

Deploy an Agent Kernel project to AWS, Azure, or GCP using Terraform modules. Supports serverless and containerized modes for all three clouds. AWS supports execution modes (rest_sync, rest_async, async, stream), queue-based scalable processing, and custom API Gateway authorizers. GCP supports Cloud Run serverless (scale-to-zero) and containerized (always-on) with Redis or Firestore session backends.

Code quality standards, formatting, commit conventions, and PR workflow for Agent Kernel development. Use this skill when making contributions, formatting code, writing commit messages, or preparing pull requests.

Add tools, agents, and handoffs to an existing Agent Kernel project. This skill guides you through reading the current project, understanding its framework and structure, then making context-aware additions — new tools, new agents, agent-to-agent handoffs, and Module wiring. The workhorse skill for iterative agent development.

Scaffold a new Agent Kernel project from scratch. This skill guides you through choosing an agent framework, defining tools, selecting a deployment target, and generating a complete project with all necessary files. Designed for users who want to build a new AI agent using the Agent Kernel library.

Step-by-step guide for adding a new agent framework adapter to Agent Kernel. Use this skill when you need to integrate a new agent framework (beyond OpenAI, CrewAI, LangGraph, Google ADK, Smolagents). Covers creating the adapter module, implementing Agent/Runner/Module subclasses, adding optional dependencies, exports, and tests.

Step-by-step guide for adding a new guardrail provider to Agent Kernel. Use this skill when you need to integrate a new content safety or guardrail service (beyond OpenAI Guardrails, AWS Bedrock Guardrails, and Walled AI). Covers implementing input/output guardrails, factory registration, configuration, and testing.

Step-by-step guide for adding a new knowledge base backend to Agent Kernel. Use this skill when you need to integrate a new storage system with the KnowledgeBase interface and expose it through KnowledgeBuilder tools.

Step-by-step guide for adding a new messaging platform integration to Agent Kernel. Use this skill when you need to add support for a new chat platform (beyond Slack, WhatsApp, Messenger, Instagram, Telegram, Teams, Gmail). Covers creating the integration handler, webhook routes, message parsing, configuration, and examples.

Step-by-step guide for adding a new observability/tracing provider to Agent Kernel. Use this skill when you need to integrate a new tracing backend (beyond Langfuse and OpenLLMetry/Traceloop). Covers implementing the BaseTrace interface, creating framework-specific traced runners, configuration, and testing.

Add a messaging platform integration to an existing Agent Kernel project. This skill guides you through adding Slack, WhatsApp, Messenger, Instagram, Telegram, Teams, or Gmail integration by generating configuration, updating dependencies, and setting up webhook handlers. Designed for users extending their agents.

Set up testing and debug common issues in Agent Kernel projects. This skill guides you through configuring the built-in test framework, writing agent tests, choosing test modes (fuzzy, judge, fallback), and troubleshooting common errors.

Sync Agent Kernel documentation from branch changes before a feature or bugfix is merged. Use this skill when implementation is ready and you need to inspect new commits plus uncommitted changes, then update root docs, ak-py docs, deployment READMEs, example READMEs, and the docs website so the documentation matches the implemented behavior.

Sync Agent Kernel skills and documentation from a specific commit hash. Use this skill in automation or maintenance flows to analyze code changes introduced by a commit, then update developer skills, user skills, and docs surfaces so the repository guidance stays aligned with implementation.

Sync Agent Kernel skills from branch changes before a feature or bugfix is merged. Use this skill when implementation is ready and you need to inspect new commits plus uncommitted changes, then update developer skills under .agents and user skills under ak-py/src/agentkernel/skills to match the actual capability set.