Coordinating Multi Space Devops Agent logo

Coordinating Multi Space Devops Agent

OrganizationPopular
aws
coordinating-multi-space-devops-agent

Coordinate the AWS DevOps Agent across multiple AgentSpaces from one Claude Code session — route questions to the right space (prod vs staging vs knowledge), query several spaces in parallel and synthesize, or compare findings across accounts. Use whenever the user has more than one AgentSpace configured, mentions multiple AWS accounts, or asks something like "check both prod and staging", "compare across accounts", or "ask the knowledge space".

Overview

Publisheraws
Repositoryagent-toolkit-for-aws
Skill namecoordinating-multi-space-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 Coordinating Multi Space 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/coordinating-multi-space-devops-agent .claude/skills/coordinating-multi-space-devops-agent
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Coordinating Multi Space 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 Coordinating Multi Space 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 Coordinating Multi Space 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.

Querying multiple AgentSpaces

Pre-flight

If aws_devops_agent__list_agent_spaces is not in your available tools, the remote MCP server is not connected. Tell the user to ask "help me set up the AWS DevOps Agent" so the setup-devops-agent skill auto-loads.

Prerequisite: SigV4 auth required

Multi-space routing requires SigV4 authentication — Bearer tokens are scoped to a single AgentSpace and cannot route to other spaces.

Many real teams run more than one AgentSpace — typically a production space, a staging space, and a dedicated "knowledge" space that holds runbooks shared across accounts. Each space has its own set of associated AWS accounts, runbooks, and history.

This skill is the routing brain. Use it when the user has multiple spaces configured, or when a question genuinely spans accounts.

Discovering spaces

aws_devops_agent__list_agent_spaces()
→ {"agentSpaces": [{"agentSpaceId": "as-abc123", "name": "prod"}, ...]}

If only one space is returned, this skill doesn't apply — use chatting-with-aws-devops-agent or investigating-incidents-with-aws-devops-agent directly (no agent_space_id needed).

If more than one is returned, decide whether the user's question is:

Question shapeStrategy
Scoped to one environment ("prod is broken")Single space — pick the matching one
Spans environments ("compare prod vs staging")Parallel — query each, synthesize
Generic knowledge ("what runbooks do we have for ECS?")Route to the knowledge space if one is named that way
Ambiguous ("our service is slow")Ask the user which environment, don't guess

Per-session routing memory

If the user has a routing guide stored locally (e.g. .claude/aws-agents-for-devsecops.md, AGENTS.md, or per-project notes), read it once at the start of the session and use it as the routing table for the rest of the conversation. Format expected:

markdown
| Space | AWS Profile | Agent Space ID | Purpose |
|-------|-------------|----------------|---------|
| prod  | acme-prod   | as-abc123      | Production incidents, customer-facing services |
| stage | acme-stage  | as-def456      | Pre-prod validation, integration testing |
| kb    | acme-shared | as-ghi789      | Shared runbooks, cross-account knowledge |

If no guide exists, run discovery:

  1. aws_devops_agent__list_agent_spaces() → get all spaces.
  2. For each space: aws_devops_agent__chat(message="Summarize the AWS accounts, services, and runbooks you have access to.", agent_space_id="<SPACE_ID>") → get a one-paragraph summary.
  3. Offer to write the routing guide to the project (e.g. .claude/aws-agents-for-devsecops.md, AGENTS.md, or per-project notes) so future sessions skip discovery.

Pattern A — Parallel queries, one synthesized answer

Use when the user wants a comparison: "compare prod and staging error rates", "is this issue happening in both accounts?", "audit costs across all our environments".

# 1. Query each space in parallel with environment-specific context
aws_devops_agent__chat(message="<question> | env=prod | <prod IaC context>", agent_space_id="PROD_ID")
→ {"executionId": "...", "answer": "..."}

aws_devops_agent__chat(message="<question> | env=stage | <stage IaC context>", agent_space_id="STAGE_ID")
→ {"executionId": "...", "answer": "..."}

# 2. Synthesize locally — present a side-by-side summary, not two separate dumps

Don't just paste both responses. Read both, identify what's the same vs. different, and tell the user the delta — that's the value.

Pattern B — Knowledge lookup, then per-space action

Use when one space holds runbooks/knowledge that informs work in another space.

# 1. Ask the knowledge space first
aws_devops_agent__chat(
    message="What's our standard runbook for ECS 503 errors?",
    agent_space_id="KB_ID"
)
→ {"answer": "<runbook text>"}

# 2. Apply that runbook in the target environment
aws_devops_agent__investigate(
    title="ECS 503 errors on checkout-service. [Runbook from knowledge space] <runbook text> [Local context] ...",
    agent_space_id="PROD_ID",
    priority="HIGH"
)

The DevOps Agent doesn't share state between spaces — you bridge it by quoting the knowledge space's response into the investigation's title.

Pattern C — Targeted single-space query

Use when the user explicitly names a space or environment.

# Pick the matching agentSpaceId from your routing memory, pass it on the call
aws_devops_agent__chat(message="<question>", agent_space_id="<matched_space_id>")

If the routing is ambiguous and the user doesn't say, ask once — better than firing into the wrong account.

Pattern D — Investigations don't share state

Investigations are per-space. If an issue spans accounts, you may need two investigations:

aws_devops_agent__investigate(title="Latency spike — prod side", agent_space_id="PROD_ID", priority="HIGH")
aws_devops_agent__investigate(title="Latency spike — stage side", agent_space_id="STAGE_ID", priority="HIGH")

Track both taskIds. Poll both. Surface findings together.

This is rare — usually one space owns the problem. Don't fan out by default.

What NOT to do

  • Don't blast every space with every question. It's slow, expensive, and the user has to read 3× as much output.
  • Don't fan out without verifying scope. If a space's description or recorded coverage doesn't mention the relevant service, skip it — sending a question into a scope-mismatched space typically hangs rather than returning "I don't know."
  • Don't fire investigations in parallel by default. They take 5–8 minutes each. Pick the one space that owns the incident.
  • Don't silently switch spaces mid-conversation. If a follow-up needs a different space, tell the user: "Switching to the knowledge space to look up the runbook."

Timeout guidance

The chat tool buffers the full response server-side before returning. Complex cross-account queries can take 30-90s per space. If a space doesn't respond within 90s, it's likely a scope mismatch — surface a message like "Space X did not respond within 90s — skipping (likely scope mismatch)" and move on rather than hanging.

See also

  • examples/multi-space-walkthrough.md for a fully worked scenario (prod incident with staging comparison and knowledge-space runbook lookup).
  • The setup-devops-agent skill for first-time configuration of multiple AgentSpaces, AWS profiles, and shell wrappers.

Frequently asked questions

What does the Coordinating Multi Space Devops Agent AI skill do?

Coordinate the AWS DevOps Agent across multiple AgentSpaces from one Claude Code session — route questions to the right space (prod vs staging vs knowledge), query several spaces in parallel and synthesize, or compare findings across accounts. Use whenever the user has more than one AgentSpace configured, mentions multiple AWS accounts, or asks something like "check both prod and staging", "compare across accounts", or "ask the knowledge space".

Why use Coordinating Multi Space Devops Agent on TypingMind?

Because you install it once and use it with any model. Coordinating Multi Space 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 Coordinating Multi Space 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/coordinating-multi-space-devops-agent. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Coordinating Multi Space 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 Coordinating Multi Space Devops Agent?

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

Is the Coordinating Multi Space 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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