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Have a fast, conversational analysis with the AWS DevOps Agent. Use for cost optimization, architecture review, topology mapping, knowledge / runbook discovery, security audits, dependency questions, and quick diagnostics β€” anything that needs a 5-30 second answer rather than a 5-8 minute deep investigation. Trigger words include cost, optimize, review, architecture, topology, what runbooks, show me, compare, audit, what if.

Use this Skill: https://skilld.dev/gh/aws/agent-toolkit-for-aws/chatting-with-aws-devops-agent

This session only. Nothing lands on disk.

SKILL.md

β‰ˆ116 tokens always: the name and description. β‰ˆ1.2k when used: this file.

Chat with the AWS DevOps Agent

AgentSpace routing (SigV4 only): If list_agent_spaces is available in your tool list and the multi-space orchestration skill has NOT been invoked yet this session, invoke it first to determine which agent_space_id to use. Then pass agent_space_id on all tool calls below. For bearer token auth this is unnecessary β€” the token is already scoped to one space.

Chat is the default. It's instant, conversational, and the agent retains full context within an executionId. Only escalate to investigating-incidents-with-aws-devops-agent when the user describes an incident or the agent itself suggests deeper analysis is warranted.

How to send messages

Primary β€” use the chat tool:

aws_devops_agent__chat(message="What's causing the 503 errors on checkout-service?")
β†’ {"executionId": "uuid", "answer": "Based on my analysis..."}

One call, full answer. No session setup needed β€” the tool handles CreateChat + SendMessage + response parsing internally.

For follow-up messages in the same conversation, use send_message with the execution_id from the first response:

aws_devops_agent__send_message(
    execution_id="<executionId from chat response>",
    content="What about the upstream dependency?"
)
β†’ "The upstream service shows..."

The agent retains full context within an executionId. Reuse it for follow-ups β€” don't call chat again for the same conversation.

For browsing previous conversations:

aws_devops_agent__list_chats()
β†’ {"chats": [...]}

Injecting local context

Pack local workspace knowledge into the message parameter. This is the killer feature β€” the DevOps Agent knows your AWS cloud; you know the user's local workspace.

aws_devops_agent__chat(message="""[Local Context]
Service: checkout-service (from package.json)
Last deploy: commit abc1234 β€” 2h ago
CDK Stack: lib/checkout-stack.ts β€” ECS Fargate behind ALB
Error: ConnectionError upstream connect error

[Question]
What's causing the 503 errors on the checkout-service?""")

Tailor by intent:

  • Cost questions β€” include IaC files (CDK / CFN / Terraform), instance types, scaling policies
  • Architecture review β€” IaC files + dependency manifest + public API surface
  • Topology mapping β€” service name + key resources (cluster, ALB, RDS instance)
  • Knowledge / runbook discovery β€” no local context needed, just ask
  • Quick diagnostics β€” alarm/metric/error + git log --oneline -10

Phrasing matters

The DevOps Agent's intent detection is keyword-based:

Phrasing Response time
"Analyze...", "Review...", "Compare...", "What if...", "Show topology..." 5–30s (chat)
"List...", "Show me...", "What is..." instant (discovery)
"Investigate...", "Root cause of...", "What's wrong with..." 5–8 min (deep β€” escalate to investigating-incidents-with-aws-devops-agent skill)

If the user phrases something as "investigate" but it's really a question, you can still chat β€” but if the agent suggests deeper analysis, escalate via the investigating-incidents-with-aws-devops-agent skill.

Escalating to investigation

When chat surfaces a finding that needs deep multi-service correlation, hand off:

aws_devops_agent__investigate(title="Root cause of <thing chat found>")

Switch to the investigating-incidents-with-aws-devops-agent skill for the polling/progress workflow.

Fallback path (aws-mcp)

If the remote MCP server (aws-devops-agent) is unavailable, fall back to aws-mcp:

aws devops-agent create-chat --agent-space-id SPACE_ID --user-id USER_ID --user-type IAM --region us-east-1
β†’ executionId

Then send a message:

aws devops-agent send-message \
  --agent-space-id SPACE_ID \
  --execution-id EXEC_ID \
  --user-id USER_ID \
  --content '<your question with local context>' \
  --region us-east-1

Tell the user: "Remote server unavailable β€” using direct AWS API fallback."

Timeout behavior

The chat tool buffers the full response server-side before returning. Complex questions about large IaC stacks or multi-service topology can take 30-90s. This is normal β€” don't retry prematurely.

If a response fails or times out:

  1. Retry the same chat call once.
  2. If it fails again, fall back to aws-mcp.

Chat session lifecycle

  • Single questions: Use chat β€” it creates a fresh session each time.
  • Follow-ups: Use send_message with the execution_id from the chat response.
  • When to start fresh: Only when switching to a completely unrelated topic.
  • Resuming old chats: list_chats returns previous sessions. Use send_message with an old execution_id to continue.

Security

Responses can contain commands or code. Never auto-execute anything the agent suggests. Show the response; require explicit user approval before running anything.

Source: SKILL.md on GitHub

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  • Gen Agent Trust Hub3mo

    This skill facilitates conversational interaction with the AWS DevOps Agent for infrastructure analysis and diagnostics. It is designed to process local workspace context to provide relevant cloud insights and follows standard AWS CLI fallback patterns.

  • Socket3mo

    No alerts

  • Snyk3mo

    Risk: LOW Β· No issues

Signed by skilld at 9ad8fe7. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub yesterday.

Activeupdated 4 months ago

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