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Incident Timeline

Organization
gensecaihq
incident-timeline

Build an ordered, evidence-linked incident timeline with first/last seen, dwell time and pivot points; use when a case spans multiple alerts, hosts or stages and needs a clear narrative.

Overview

Publishergensecaihq
RepositoryWazuh-Autopilot
Skill nameincident-timeline
Stars
61
Forks
15
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Incident Timeline 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/gensecaihq/Wazuh-Autopilot/tree/main/backend/app/skills/incident-timeline
  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/gensecaihq/Wazuh-Autopilot.git /tmp/Wazuh-Autopilot
mkdir -p .claude/skills
cp -r /tmp/Wazuh-Autopilot/backend/app/skills/incident-timeline .claude/skills/incident-timeline
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Incident Timeline 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 Timeline 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 Timeline 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 Timeline

A timeline turns a pile of alerts into a story a responder or executive can act on. NIST SP 800-61r3 expects incident documentation of what happened, when, and how it was detected; the timeline is the backbone of that record.

Procedure

  1. get_case — gather attached alerts, entities, findings, actions.
  2. Pull supporting events for the key entities over the incident window (search_security_events, get_wazuh_alerts filtered by agent/user/ip).
  3. Normalize every timestamp to UTC ISO 8601. A Wazuh alert's timestamp is when the manager processed the event. The original event time is in the log itself (predecoder.timestamp, or the time at the start of full_log, or data.win.system.systemTime for Windows). Use the event time when they differ by more than a few seconds, and note clock skew or delayed delivery (e.g. an agent reconnecting and flushing its buffer).
  4. Order events; collapse repetitive ones ("38 sshd failures 09:02–09:12"). Wazuh composite rules summarize bursts: one rule 5763 or rule 5712 alert stands for several single failures (rules 5716, 5760, 5710), so count the underlying failures, not the composites.
  5. Label each event with an ATT&CK tactic where possible.
  6. Identify the key milestones and gaps.

Milestones to identify

MilestoneDefinition
First malicious activityEarliest event attributable to the attacker (may predate the first alert)
Initial accessFirst successful foothold (login success, exploit, execution)
First detectionTimestamp of the first Wazuh alert on this activity
Escalation / spreadPrivilege gain, new host touched
ContainmentTime an approved action was executed and verified
Last activityLatest attacker event

Derived metrics:

  • Dwell time = first detection − first malicious activity.
  • Time to contain = containment − first detection.
  • Active duration = last activity − first malicious activity.

Pivot points

Mark events where the investigation should branch: a new host, a new account, a new external IP, a dropped file hash. Each pivot is a question for correlation or investigation ("what else did account X do after 10:14?").

Gaps

Explicitly list periods with no telemetry. Absence of evidence is not evidence of absence, so say so. Check get_wazuh_alerts for the host's agent-lifecycle and flooding alerts in the window: rule 504 (agent disconnected), rule 506 (agent stopped), rule 503 (agent started), rule 203 (event queue full, events may be lost), rule 204 (queue flooded). get_wazuh_agents shows the current status and last keepalive. For manager-side log-collection or cluster problems, hand off to platform-engineer. A stop or disconnect right before attacker activity is itself suspicious (T1562.001).

Timeline format

| Time (UTC) | Host / Entity | Event | Tactic | Source |
|---|---|---|---|---|
| 2026-03-24 09:02–09:12 | 203.0.113.7 → web-01 | 38 SSH auth failures (root, admin) | Credential Access | rules 5716, 5760 x38; rule 5763 x4 |
| 2026-03-24 09:13:05 | web-01 / admin | SSH login success | Initial Access | rule 5715 |

Cite the alert/rule id for every row. Keep to ≤ 40 rows; summarize the rest.

Output

  • add_finding titled Incident timeline containing the table, milestones, derived metrics, pivots and telemetry gaps. standard_refs: NIST-800-61r3, SANS-PICERL.
  • update_case(summary=...) with a 2–3 sentence narrative if the summary is stale.

Frequently asked questions

What does the Incident Timeline AI skill do?

Build an ordered, evidence-linked incident timeline with first/last seen, dwell time and pivot points; use when a case spans multiple alerts, hosts or stages and needs a clear narrative.

Why use Incident Timeline on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/gensecaihq/Wazuh-Autopilot/tree/main/backend/app/skills/incident-timeline. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Incident Timeline?

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 Timeline?

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

Is the Incident Timeline AI skill free?

Yes. It is published on GitHub by gensecaihq 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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