Hunt Apt

Community

dandye

hunt-apt

Hunt for a specific APT/threat actor in your environment. Use when you have a threat actor name or GTI collection ID and want to search for their TTPs and IOCs. Gathers intelligence from GTI, searches SIEM for IOCs and TTP-based indicators, and documents findings.

Overview

Publisher	dandye
Repository	`ai-runbooks`
Skill name	`hunt-apt`
Stars	126
Forks	34
Bundled files	Instructions only
License	Apache-2.0
Links	Skill source Repository

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 dandye on GitHub. Read the source before you install it.

Installation

Install the Hunt Apt AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
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.

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/dandye/ai-runbooks.git /tmp/ai-runbooks
mkdir -p .claude/skills
cp -r /tmp/ai-runbooks/skills/hunt-apt .claude/skills/hunt-apt

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Hunt Apt 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 Hunt Apt 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 Hunt Apt 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.

APT Threat Hunt Skill

Proactively hunt for TTPs and IOCs associated with a specific Advanced Persistent Threat (APT) group based on threat intelligence.

Inputs

THREAT_ACTOR_ID - GTI Collection ID or name of the target APT group
HUNT_TIMEFRAME_HOURS - Lookback period (default: 168 = 7 days)
(Optional) TARGET_SCOPE_QUERY - UDM query to narrow scope
(Optional) HUNT_HYPOTHESIS - Specific hypothesis guiding the hunt
(Optional) HUNT_CASE_ID - SOAR case for tracking

Workflow

Step 1: Identify Actor & Gather Intelligence

If starting with a name:


gti-mcp.search_threat_actors(query="APT_NAME")

Then gather comprehensive intelligence:


gti-mcp.get_collection_report(id=THREAT_ACTOR_ID)
gti-mcp.get_collection_mitre_tree(id=THREAT_ACTOR_ID)
gti-mcp.get_collection_timeline_events(id=THREAT_ACTOR_ID)

Extract associated IOCs:


gti-mcp.get_entities_related_to_a_collection(id=THREAT_ACTOR_ID, relationship_name="files")
gti-mcp.get_entities_related_to_a_collection(id=THREAT_ACTOR_ID, relationship_name="domains")
gti-mcp.get_entities_related_to_a_collection(id=THREAT_ACTOR_ID, relationship_name="urls")

Store as GTI_IOC_LIST.

Step 2: Check SIEM IOC Matches


secops-mcp.get_ioc_matches(hours_back=HUNT_TIMEFRAME_HOURS)

Correlate results with GTI_IOC_LIST.

Step 3: IOC-Based SIEM Search

For each IOC type in GTI_IOC_LIST, construct and execute UDM queries:


secops-mcp.search_security_events(
    text="UDM query for IOC",
    hours_back=HUNT_TIMEFRAME_HOURS
)

Document both positive and negative results → IOC_SEARCH_FINDINGS.

Step 4: TTP-Based SIEM Search

Based on MITRE techniques from Step 1:

Use gti-mcp.get_threat_intel(query="MITRE technique details") for detection ideas
Formulate TTP-specific UDM queries
Execute searches over the timeframe
Combine with TARGET_SCOPE_QUERY if provided

Document results → TTP_SEARCH_FINDINGS.

Step 5: Enrich Findings

If hits found (IOC_SEARCH_FINDINGS or TTP_SEARCH_FINDINGS):

For each found IOC or entity:


secops-mcp.lookup_entity(entity_value=FOUND_ITEM)
gti-mcp.get_..._report(identifier=FOUND_ITEM)

Step 6: Check Related Cases

Use /find-relevant-case with found IOCs and entities.

Step 7: Document & Report

Use /document-in-case (if HUNT_CASE_ID provided).

Use /generate-report with REPORT_TYPE="apt_hunt":

Hunt objective and hypothesis
Threat actor summary
TTPs investigated
IOCs searched
SIEM queries used
Findings (positive AND negative)
Recommendations

Step 8: Escalate or Conclude

Confirmed threat found: → Escalate to Incident Response → Create incident case

No threat found: → Document negative findings → Conclude hunt

Required Outputs

After completing this skill, you MUST report these outputs:

Output	Description
`ACTOR_IOCS`	IOCs associated with threat actor from GTI
`ACTOR_TTPS`	TTPs from threat actor profile (MITRE techniques)
`HUNT_RESULTS`	SIEM search results for actor indicators
`DISCOVERED_INDICATORS`	IOCs found in environment matching actor profile
`CONFIRMED_IOCS`	IOCs confirmed malicious via GTI enrichment

Key Intelligence Sources

Source	Tool
Actor Profile	`get_collection_report`
TTPs	`get_collection_mitre_tree`
Timeline	`get_collection_timeline_events`
Related IOCs	`get_entities_related_to_a_collection`
Technique Details	`get_threat_intel`

Critical Requirements

Document ALL queries used (for reproducibility)
Report negative findings (no hits is valuable intel)
Don't report false positives as confirmed threats

Frequently asked questions

What does the Hunt Apt AI skill do?

Why use Hunt Apt on TypingMind?

Because you install it once and use it with any model. Hunt Apt 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 Hunt Apt in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/dandye/ai-runbooks/tree/main/skills/hunt-apt. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Hunt Apt?

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 Hunt Apt?

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

Is the Hunt Apt AI skill free?

Yes. It is published on GitHub by dandye under the Apache-2.0 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.

View all

Caveman Compress

JuliusBrussee

CommunityPopular

Compress a memory file such as CLAUDE.md or a todo list into caveman format to save input tokens, keeping a readable backup. Trigger: /caveman-compress.

Caveman Discover

Find and label every LLM workflow in the repository so Caveman Cloud groups spend by workflow instead of one bucket. Use for "discover workflows" or breaking LLM spend down by workflow.

Caveman Manage

Inspect Caveman Cloud's experiment lifecycle and block unsafe execution. Use when asked to start, approve, cancel, promote or roll back a Caveman experiment.

Caveman Setup

Wire a repository through the Caveman Cloud gateway so every LLM request is measured, with no behavior change. Use for "set up caveman" or adding LLM spend observability.

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

APT Threat Hunt Skill

Inputs

Workflow

Step 1: Identify Actor & Gather Intelligence

Step 2: Check SIEM IOC Matches

Step 3: IOC-Based SIEM Search

Step 4: TTP-Based SIEM Search

Step 5: Enrich Findings

Step 6: Check Related Cases

Step 7: Document & Report

Step 8: Escalate or Conclude

Required Outputs

Key Intelligence Sources

Critical Requirements

Frequently asked questions

Related AI Skills

Set up your own AI workspace now