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Incident Review

Community
davekilleen
incident-review

Use when a service or operational incident needs a cited, timezone-aware timeline, blameless learning review, or prevention actions with accountable follow-up.

Overview

Publisherdavekilleen
RepositoryDex
Skill nameincident-review
Stars
481
Forks
130
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 davekilleen on GitHub. Read the source before you install it.

Installation

Install the Incident Review 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/davekilleen/Dex.git /tmp/Dex
mkdir -p .claude/skills
cp -r /tmp/Dex/packages/dex-agent-plugin/skills/_available/engineering/incident-review .claude/skills/incident-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Incident Review 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 Review 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 Review 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 review

When to use

Use this skill after or during a contained incident when people need a trustworthy timeline, impact record, contributing-conditions analysis, and prevention follow-up.

Do not use it as a substitute for live containment, paging, emergency escalation, or security/legal reporting. Not for attributing personal blame, filling gaps with a plausible root cause, or reviewing an event without cited evidence.

Inputs and source discipline

Collect an incident identifier, review scope, start and end bounds, affected systems, and the review as-of date/time. Use a named IANA timezone when available and preserve each source's original timestamp and timezone or offset.

Create an evidence ledger for logs, alerts, deploy records, traces, tickets, support reports, communications, and interviews. For every event or impact claim record the source, source date, as-of date/time, locator, timestamp precision, and freshness. Record missing sources and access limits as unknown rather than compensating with memory. Keep raw evidence separate from analysis.

Method

  1. Define the incident boundary and affected service or users from cited evidence. Do not invent severity, duration, counts, percentages, or business impact.
  2. Build a timezone-aware timeline. Preserve the reported timestamp and timezone, add a normalized timestamp only when conversion is valid, and cite the source for every row. If two credible sources disagree, retain both rows and mark the point contradictory instead of forcing an order.
  3. Separate facts (directly observed or cited), hypotheses (inferred explanations), unknowns, stale evidence, and contradictions. Test a hypothesis against available evidence; do not call it a cause or root cause until the evidence supports that wording.
  4. Use a blameless method: describe system conditions, incentives, interfaces, safeguards, detection, response, and recovery. Name actions and controls, not people as the cause. Explain what was reasonable to know at the time.
  5. Define prevention and detection actions with the failure mode they address, a proof or acceptance measure, a follow-up date only when evidenced, and an owner. Use TBD for an unassigned owner or date; never guess an owner or make a status claim from an unchecked task list.
  6. Present recommendations and follow-up choices to the human authority. A recommendation is not a human decision, and prevention work is not complete until its proof is read back.

Truth and uncertainty rules

Use the labels observed, inferred, unknown, stale, and contradictory on the timeline and analysis. A fact is not a hypothesis; a hypothesis is not a confirmed cause. When evidence is missing or timestamps cannot be reconciled, say so, preserve the conflict, and state what would resolve it.

Never invent dates, metrics, owners, intent, money, percentages, causes, status, or evidence. Do not turn a plausible narrative into a fact, and do not use silence in a log as proof that an event did not happen. Make confidence proportional to cited evidence.

Output contract

Return a review containing:

  • incident scope, as-of time, timezone, systems, and impact claims with citations;
  • a timezone-aware fact timeline with original timestamps, source/date provenance, confidence, and contradictory rows retained;
  • separate Facts, Hypotheses, Unknowns, Stale evidence, and Contradictions sections;
  • a blameless account of contributing system conditions and detection/response gaps;
  • prevention and detection actions with owner or TBD, proof measure, follow-up, and current status only when observed; and
  • source links, confidence, unknowns, contradictions, and a recommendation clearly labelled as non-decision guidance.

Safety and write boundaries

The default is read-only. Preview the exact report or task changes, obtain explicit confirmation from the human authority, and only then write or initiate an action. Do not page people, change production systems, close incident records, assign owners, or send external communications from an unconfirmed recommendation. Preserve raw incident evidence and avoid exposing sensitive details beyond the review need.

Verification and recovery

Read back the completed review and reconcile every timeline row's timezone, ordering, source citation, impact statement, action owner, proof measure, and status with the source ledger. Re-check that hypotheses remain labelled and that no unsupported cause or date entered the document.

If a read or write fails, stop, report the failure and any partial output, and retain the raw evidence. Re-read the destination before retrying; do not overwrite a partial review or silently drop contradictions. Recovery consists of an append-only correction or follow-up update after human confirmation, with the failed check and new evidence recorded.

Frequently asked questions

What does the Incident Review AI skill do?

Use when a service or operational incident needs a cited, timezone-aware timeline, blameless learning review, or prevention actions with accountable follow-up.

Why use Incident Review on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/davekilleen/Dex/tree/main/packages/dex-agent-plugin/skills/_available/engineering/incident-review. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Incident Review?

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 Review?

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

Is the Incident Review AI skill free?

It is published on GitHub by davekilleen. 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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