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Incident Response

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nahid-sparktales
incident-response

Stabilize a system that is failing right now — name the signal that flagged it, size the blast radius in numbers, keep a timestamped log written as you go, find the last known-good state, then propose the smallest reversible mitigation and confirm recovery against that same signal. Use when production is degraded or down and time to mitigation matters more than a complete explanation. Not for a defect that is not currently failing (systematic-debugging), not for the root-cause investigation or the postmortem that follows, and never authorization to touch production without asking first.

Overview

Publishernahid-sparktales
Repositoryagent-dispatcher
Skill nameincident-response
Stars
58
Forks
5
Bundled files
1
LicenseMIT
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 nahid-sparktales 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.
    https://github.com/nahid-sparktales/agent-dispatcher/tree/main/skills/devops/incident-response
  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/nahid-sparktales/agent-dispatcher.git /tmp/agent-dispatcher
mkdir -p .claude/skills
cp -r /tmp/agent-dispatcher/skills/devops/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

Mitigation comes before explanation. The cause keeps; the outage does not. Every step here exists to stop you from applying an unrecorded change you cannot undo and then calling a coincidence a fix.

When this fires

A production system is failing or degraded now and the harm is ongoing. It stops firing the moment the signal is stable again — then it is a debugging problem and a postmortem, not this.

If the symptom has already passed on its own, do not start mitigating. Preserve evidence and hand it to investigation.

Procedure

  1. Name the flagging signal exactly — which alert, dashboard, query or user report, what threshold, and the first bad timestamp. Everything you do later is judged against this one signal. If nobody can name it, you do not yet know that anything is wrong.
  2. Open the log before investigating. Append-only, UTC timestamps, one line per observation, action and decision, each action recording who ran it and what happened. A timeline written afterwards is a story assembled from memory of a stressful hour.
  3. Size the blast radius in numbers: who (users, tenants, regions), what (endpoints, jobs, features), how much (share and absolute count of affected requests), since when — and whether it is growing, flat or shrinking. "Lots of users" is not a blast radius.
  4. Say the impact out loud early, and leave comms to a human. Status pages, customer notices and broadcast messages are outward-facing: draft them if asked, stop before posting.
  5. Enumerate what changed before the first bad timestamp — deploys, config edits, feature-flag flips, migrations, scaling changes, dependency bumps, credential or certificate rotation, traffic shifts, upstream vendor status. List them before forming a causal theory; a theory formed first will pick whichever change it likes.
  6. Rank mitigations by reversibility and blast radius, not by how likely they are to be the cause. Revert the deploy, flip the flag off, drain or fail over the bad instance, scale out, shed load or rate-limit, restart. A mitigation does not need to know the cause. Writing a forward fix during an outage is the least reversible option available — treat it as the last one.
  7. Before applying anything, state four things: the expected effect on the flagging signal, the window you expect it in, the risk the action itself carries, and exactly how to undo it. Then stop and ask. Every production-affecting action needs its own confirmation, and "while I am in there" changes are never included in it.
  8. Apply one change at a time and record the time it went in. Two simultaneous changes buy an uninterpretable recovery and a mitigation nobody can safely remove later.
  9. Preserve evidence before it ages out. Copy the log excerpts, the metric graph with its time range, traces, failing payloads and the exact error strings into the incident log. Never delete logs, rotate files, drop tables or clear a queue to make a symptom disappear — that is irreversible data loss during the one hour it matters most; stop and ask.
  10. Confirm recovery against the flagging signal, over a window long enough to outlast a coincidental dip and to cover at least one full cycle of whatever failed. A green build, a passing test, a healthy synthetic check and "I am not seeing errors now" are none of them recovery evidence.
  11. If the mitigation did not work, undo it before trying the next one. An incident carrying three abandoned mitigations has become a second incident with no known-good state.
  12. Hand off explicitly: what is mitigated and by what, what is still unexplained, every change still in place and how to remove it, and where the preserved evidence is. Root cause goes to systematic debugging once the bleeding has stopped.

Checklist

  • The flagging signal is named, with its threshold and first bad timestamp
  • A timestamped log exists and was written during, not after
  • Blast radius stated in numbers, with its direction of travel
  • Recent changes enumerated before any causal theory was offered
  • Mitigation options ranked by reversibility, with the undo written down
  • Each production-affecting action confirmed individually before it was applied
  • One change at a time, each with the time it was applied
  • Evidence preserved before retention could drop it
  • Recovery confirmed on the flagging signal over a stated window
  • Handoff names what is mitigated, what is unexplained, and what must still be removed

Failure handling

  • The signal recovered but your change could not have reached it — different region, tenant or code path — it is a coincidence. Do not close the incident on it; keep watching.
  • Nothing changed in the window — not every incident is self-inflicted. Check upstream provider status, expired certificates, exhausted quotas or licences, full disks and volumes, rotated credentials, and the slowly growing resource (connections, queue, retention, ids) that crossed a threshold with no deploy at all.
  • Several things broke at once — look for the shared dependency before treating them as separate incidents.
  • Monitoring itself is degraded — absence of alerts is now unknown, not healthy. Say so and fall back to a signal you can read directly.
  • "Just restart it" — acceptable as a mitigation when it is reversible and recorded, but a restart destroys the process state that would have explained the failure. Capture what you can first, and expect it to return.
  • Pressure to apply a fix without confirmation — urgency is not authorization. State the proposed action and its undo, and wait.

Evidence to report

The timestamped log itself, verbatim — it is the deliverable, not a summary of it. The flagging signal with its query, and its values before and after with timestamps. Each action with the time applied, who confirmed it, and its observed effect on that signal. Then state plainly what is mitigated (the symptom stopped) versus what is fixed (the cause is gone) — conflating these is how a mitigation gets removed a week later and takes the site down again. List what remains unexplained, and every temporary change still holding the system up, each with an owner.

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 Incident Response AI skill do?

Stabilize a system that is failing right now — name the signal that flagged it, size the blast radius in numbers, keep a timestamped log written as you go, find the last known-good state, then propose the smallest reversible mitigation and confirm recovery against that same signal. Use when production is degraded or down and time to mitigation matters more than a complete explanation. Not for a defect that is not currently failing (systematic-debugging), not for the root-cause investigation or the postmortem that follows, and never authorization to touch production without asking first.

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/nahid-sparktales/agent-dispatcher/tree/main/skills/devops/incident-response. 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 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?

Yes. It is published on GitHub by nahid-sparktales under the MIT 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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