Chatting With Aws Devops Agent logo

Chatting With Aws Devops Agent

OrganizationPopular
aws
chatting-with-aws-devops-agent

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 — 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.

Overview

Publisheraws
Repositoryagent-toolkit-for-aws
Skill namechatting-with-aws-devops-agent
Stars
2.7K
Forks
311
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by aws on GitHub. Read the source before you install it.

Installation

Install the Chatting With Aws Devops Agent AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/aws/agent-toolkit-for-aws.git /tmp/agent-toolkit-for-aws
mkdir -p .claude/skills
cp -r /tmp/agent-toolkit-for-aws/plugins/aws-agents-for-devsecops/skills/chatting-with-aws-devops-agent .claude/skills/chatting-with-aws-devops-agent
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Chatting With Aws Devops Agent in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Chatting With Aws Devops Agent on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Chatting With Aws Devops Agent is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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 — 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 — use the chat tool:

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

One call, full answer. No session setup needed — 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?"
)
→ "The upstream service shows..."

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

For browsing previous conversations:

aws_devops_agent__list_chats()
→ {"chats": [...]}

Injecting local context

Pack local workspace knowledge into the message parameter. This is the killer feature — 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 — 2h ago
CDK Stack: lib/checkout-stack.ts — 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 — include IaC files (CDK / CFN / Terraform), instance types, scaling policies
  • Architecture review — IaC files + dependency manifest + public API surface
  • Topology mapping — service name + key resources (cluster, ALB, RDS instance)
  • Knowledge / runbook discovery — no local context needed, just ask
  • Quick diagnostics — alarm/metric/error + git log --oneline -10

Phrasing matters

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

PhrasingResponse 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 — 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 — 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
→ executionId

Then send a message:

bash
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 — 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 — 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 — 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.

Frequently asked questions

What does the Chatting With Aws Devops Agent AI skill do?

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 — 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.

Why use Chatting With Aws Devops Agent on TypingMind?

Because you install it once and use it with any model. Chatting With Aws Devops Agent is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Chatting With Aws Devops Agent in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/aws/agent-toolkit-for-aws/tree/main/plugins/aws-agents-for-devsecops/skills/chatting-with-aws-devops-agent. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Chatting With Aws Devops Agent?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Chatting With Aws Devops Agent?

As many as you like. As long as a model supports skills, you can use Chatting With Aws Devops Agent with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Chatting With Aws Devops Agent AI skill free?

Yes. It is published on GitHub by aws under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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