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Skill

/incident-response

Diagnose and respond to production incidents. Use when a service is down, errors are spiking, latency is degraded, or the user reports a production issue.

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atlas-agents
58713 skills
Install
$ npx -y skills add agulli/atlas-agents --skill incident-response --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/incident-response

Context preview

The summary Claude sees to decide when to auto-load this skill.

Diagnose and respond to production incidents. Use when a service is down, errors are spiking, latency is degraded, or the user reports a production issue.

SKILL.md

incident-response.SKILL.md
name: incident-response
description: Diagnose and respond to production incidents. Use when a service is down, errors are spiking, latency is degraded, or the user reports a production issue.
license: MIT

Overview

You are an on-call engineer triaging a live incident. Speed matters, but reckless changes make things worse. Follow the process.

Process

1. **Assess severity.** Ask or determine:

  • Is the service fully down, partially degraded, or experiencing elevated errors?
  • How many users are affected?
  • Is data being lost or corrupted?

2. **Gather signals.** Before forming any hypothesis, collect:

  • Recent deployments (`git log --oneline -10`)
  • Error logs (last 100 lines of the relevant log file)
  • Resource utilization (CPU, memory, disk, connections)
  • Recent configuration changes

3. **Form ONE hypothesis.** Based on the signals, state your best guess in one sentence. Do not enumerate multiple possibilities — pick the most likely one.

4. **Test the hypothesis.** Run exactly one diagnostic command or query that would confirm or refute your hypothesis. Read the output.

5. **If confirmed:** Propose a fix. If the fix involves restarting a service or rolling back a deploy, state the exact command. Do not improvise commands.

6. **If refuted:** Return to step 2 with the new information. Form a new hypothesis.

7. **Post-mortem.** After the incident is resolved, write a brief post-mortem with:

  • Timeline (when it started, when it was detected, when it was resolved)
  • Root cause (one sentence)
  • Fix applied
  • Follow-up actions to prevent recurrence

Rationalizations

| Excuse | Rebuttal | |--------|----------| | "Let me just restart the service first" | Restarting without diagnosis destroys evidence. Gather signals first. | | "I have three theories" | Pick one. Test it. If wrong, pick another. Parallel investigation wastes time. | | "It's probably fine now" | Confirm with metrics. "Probably" is not a resolution status. | | "We can skip the post-mortem, it was minor" | Minor incidents reveal systemic issues. Write the post-mortem. |

Verification

  • [ ] Signals were gathered before any remediation was attempted
  • [ ] The root cause was identified (not assumed)
  • [ ] Service health was confirmed after the fix (not assumed)
  • [ ] A post-mortem was written
Read more
Ships withatlas-agents

This repository contains the source code, examples, and project implementations for the book "Hands-on AI Agents" — a practical, code-first guide to building AI agents and, more importantly, putting them into production.

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