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Incident Response

OrganizationPopular
multica-ai
incident-response

How this team handles a production incident issue — what to establish before proposing a fix, and what an agent may and may not do on its own.

Overview

Publishermultica-ai
Repositorymultica
Skill nameincident-response
Stars
50.4K
Forks
6.5K
Bundled files
Instructions only
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 multica-ai on GitHub. Read the source before you install it.

Installation

Install the Incident Response 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/multica-ai/multica.git /tmp/multica
mkdir -p .claude/skills
cp -r /tmp/multica/examples/plugins/deploy-sentinel/skills/incident-response .claude/skills/incident-response
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Incident Response 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 Incident Response 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 Incident Response 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.

Incident response

An incident issue is not a bug report. The goal is to stop the bleeding first and explain it second, and those two are frequently in tension.

Establish, in this order

  1. Blast radius. Who is affected and how badly. An error rate that doubled from 0.01% is not the same incident as one that doubled from 4%.
  2. When it started. Get a timestamp before you get a theory. The first timestamp people offer is usually when somebody noticed, not when it began.
  3. What changed. Call correlate_deploys with the service and a window that starts before the timestamp from step 2. An empty result is informative: it means this is probably not a deploy, and you should stop looking there.

Do not skip to step 3. A deploy that landed in the window is not automatically the cause, and the fastest way to waste an hour is to roll back the first plausible change and watch the incident continue.

Rollback

request_rollback files a change request. It does not roll anything back — a human approves it. So filing one is cheap, and you should file it as soon as you have a specific deploy and a reason, rather than waiting until you are certain.

The reason you write goes into the change record verbatim and is what the approver reads at 3am. Write the evidence, not the conclusion:

  • Good: "error rate on checkout-api went 0.2% → 6% within 90s of deploy d-4821; no other deploy in the window; the diff touches the retry path."
  • Bad: "this deploy broke checkout."

Deploys older than the configured rollback window are refused. That is deliberate: past that point a rollback is usually more dangerous than a fix forward, and it needs a human deciding, not a tool call.

What to write on the issue

Update the issue as you go rather than at the end. Someone else may be reading it to decide whether to escalate, and an issue that goes quiet for twenty minutes reads as "nobody is on this".

Keep the timeline in the issue description and the reasoning in comments. The description is what someone reads six months later during a postmortem; the comments are what your colleagues read right now.

What not to do

  • Do not change issue status to done because the error rate recovered. Recovery and resolution are different; an incident stays open until somebody explains it.
  • Do not close the loop silently. If you correlated deploys and found nothing, say so on the issue — that is a real result and it stops the next person repeating it.

Frequently asked questions

What does the Incident Response AI skill do?

How this team handles a production incident issue — what to establish before proposing a fix, and what an agent may and may not do on its own.

Why use Incident Response on TypingMind?

Because you install it once and use it with any model. Incident Response 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 Incident Response in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/multica-ai/multica/tree/main/examples/plugins/deploy-sentinel/skills/incident-response. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Incident Response?

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 Incident Response?

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

Is the Incident Response AI skill free?

It is published on GitHub by multica-ai. Check the repository for licensing terms. 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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