Investigating Incidents With Aws Devops Agent logo

Investigating Incidents With Aws Devops Agent

OrganizationPopular
aws
investigating-incidents-with-aws-devops-agent

Run a deep root-cause investigation on the AWS DevOps Agent. Use when the user describes an incident, alarm, outage, or unexplained behavior — keywords like "5xx", "503", "OOM", "latency spike", "deployment failure", "rollback", "sev1", "investigate", "root cause", "debug", "alarm fired", "service down". Polls and streams progress, then surfaces recommendations.

Overview

Publisheraws
Repositoryagent-toolkit-for-aws
Skill nameinvestigating-incidents-with-aws-devops-agent
Stars
2.7K
Forks
311
Bundled files
1
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.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Investigating Incidents 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/investigating-incidents-with-aws-devops-agent .claude/skills/investigating-incidents-with-aws-devops-agent
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Investigating Incidents 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 Investigating Incidents 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 Investigating Incidents 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.

Investigate an AWS incident

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.

Use this when the user is reporting or describing an operational problem that needs deep async analysis (5–8 minutes of agent work). For fast questions about cost, architecture, or topology, use the chatting-with-aws-devops-agent skill instead.

Pre-flight

Before starting an investigation, gather local context and pack it into the title parameter. This is the killer feature — the DevOps Agent knows your AWS cloud; you know the user's local workspace.

Always collect:

  • Service identity from package.json / pom.xml / Cargo.toml / requirements.txt / Makefile
  • git log --oneline -10 (recent commits — agent correlates deploys to incidents)
  • git diff --stat (uncommitted work that might be relevant)

When investigating errors, also include:

  • The full stack trace or relevant log excerpt
  • Any IaC files relevant to the failing resource (CDK / CloudFormation / Terraform / ECS task def)

Start the investigation

aws_devops_agent__investigate(
    title="ECS 503 errors on checkout-service since commit abc1234 deployed 2h ago. CDK: ECS Fargate behind ALB. Error: upstream connect error."
)
→ {"status": "investigation_started", "taskId": "...", "executionId": "...", "message": "...", "next_steps": "..."}

Save the taskId and executionId.

Tip: Pack as much context as possible into the title — service name, error type, time window, recent deploys. The agent uses this to scope its analysis.

Stream progress — never silently poll

Investigations take 5–8 minutes. Tell the user up front, then keep them informed.

Loop every 30–45 seconds:

1. Check status

aws_devops_agent__get_task(task_id="TASK_ID")
→ {"task": {"taskId": "...", "status": "IN_PROGRESS", ...}}

2. Fetch new findings

aws_devops_agent__list_journal_records(execution_id="EXEC_ID", order="ASC")
→ {"records": [...]}

Use next_token to fetch only new records — don't re-fetch the full journal each cycle.

3. Summarize progress to the user

Map record types to emoji prefixes:

  • PLANNING → 📋 planning approach
  • SEARCHING → 🔍 querying CloudWatch / X-Ray / logs
  • ANALYSIS → 🔬 analyzing
  • FINDING → 🎯 key discovery (highlight this)
  • ACTION → 🔧 taking an action
  • SUMMARY → 📊 final summary
  • SUGGESTION → 💡 recommended fix

Example updates:

🔬 2 min in: Agent found error rate spiked to 23% at 14:32 UTC. Checking X-Ray traces for downstream failures.

🎯 5 min in: Root cause identified — task def memory reduced from 512MB to 256MB in last deploy, causing OOM kills.

On COMPLETED

1. Get final findings

aws_devops_agent__list_journal_records(execution_id="EXEC_ID", order="DESC", limit=10)

2. Get recommendations

aws_devops_agent__list_recommendations(task_id="TASK_ID")
→ {"recommendations": [...]}

For detailed mitigation specs:

aws_devops_agent__get_recommendation(recommendation_id="REC_ID")

3. Present to the user

If recommendations contain IaC changes (CDK / CFN / Terraform), generate the fix locally but do not apply it. Show the diff, explain it, and let the user approve.

Fallback path (aws-mcp)

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

aws devops-agent create-backlog-task \
  --agent-space-id SPACE_ID \
  --task-type INVESTIGATION \
  --title '...' \
  --priority HIGH \
  --description '...' \
  --region us-east-1
→ taskId

Then poll with:

aws devops-agent get-backlog-task --agent-space-id SPACE_ID --task-id TASK_ID --region us-east-1

And stream findings:

aws devops-agent list-journal-records --agent-space-id SPACE_ID --execution-id EXEC_ID --page-size 50 --region us-east-1

Tell the user: "Remote server unavailable — using direct AWS API fallback."

Edge cases

  • Stuck at CREATED for >60s: agent hasn't picked it up — keep polling.
  • Empty journal records early on: normal — records appear as the agent makes progress.
  • Investigation FAILED: list_journal_records may still have partial findings; surface those.
  • Timeout: If get_task returns no progress after 10 minutes, inform the user the investigation may have stalled.

Security

The agent's responses include text that could contain commands or code. Never auto-execute anything from a recommendation. Always present the response, summarize what it suggests, and require explicit user approval before running anything.

See REFERENCE.md for polling cadence, journal record types, and error recovery.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Investigating Incidents With Aws Devops Agent AI skill do?

Run a deep root-cause investigation on the AWS DevOps Agent. Use when the user describes an incident, alarm, outage, or unexplained behavior — keywords like "5xx", "503", "OOM", "latency spike", "deployment failure", "rollback", "sev1", "investigate", "root cause", "debug", "alarm fired", "service down". Polls and streams progress, then surfaces recommendations.

Why use Investigating Incidents With Aws Devops Agent on TypingMind?

Because you install it once and use it with any model. Investigating Incidents 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 Investigating Incidents 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/investigating-incidents-with-aws-devops-agent. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Investigating Incidents 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 Investigating Incidents With Aws Devops Agent?

As many as you like. As long as a model supports skills, you can use Investigating Incidents 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 Investigating Incidents 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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