ak-add-capabilities

Add Capabilities

Use this skill to enhance your Agent Kernel project with additional capabilities.

Instructions for the Agent

When the user wants to add a capability, follow this workflow:

Step 1: Identify the Project

Check for an existing Agent Kernel project with pyproject.toml and agent definition file.

Step 2: Ask Which Capability

Which capability would you like to add?

1. Guardrails — Content safety filters for input and/or output
2. Tracing — Observability and monitoring (Langfuse, OpenLLMetry, or Pydantic Logfire)
3. Session Persistence — Durable conversation state (Redis, DynamoDB, Cosmos DB, Firestore)
4. Knowledge Base — Durable cross-session knowledge tools (ChromaDB, Neo4j, Starburst, or custom backend)
5. MCP Server — Expose agents as Model Context Protocol tools
6. A2A Server — Agent-to-Agent communication protocol
7. Hooks — Custom pre/post processing (RAG, logging, prompt modification)
8. Multimodal — Image and file attachment support
9. Conversation Threads — Persistent, named conversation history keyed by session_id
10. Sandbox — Isolated code/command execution with pluggable providers, workload profiles, policy, and per-user identity
11. AG-UI Server — Stream any agent to an AG-UI-compliant frontend (text, tool calls, reasoning, shared state)
12. Scheduled Tasks — Deferred and recurring chat execution (a schedule block on a chat request, management routes, agent tools)

Step 3: Generate Changes

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Guardrails

Ask: Input guardrails, output guardrails, or both? Which provider — OpenAI, AWS Bedrock, or Walled AI?

For OpenAI Guardrails:

1. Update pyproject.toml:

dependencies = [
    "agentkernel[openai,api]>=0.9.0",
    # OpenAI guardrails use the openai extra — already included if using OpenAI framework
]

1. Create guardrails_input.json:

[
  {
    "type": "moderation",
    "moderation_config": {
      "content_type": ["violence", "sexual", "harassment", "self-harm"],
      "threshold": 0.5
    }
  },
  {
    "type": "jailbreak",
    "jailbreak_config": {}
  }
]