dandye/ai-runbooks/hunt-threat
Claude Code and agent skill by dandye/ai-runbooks
- community
Checking that the SKILL.md file is still in the repository.
Conduct proactive, hypothesis-driven threat hunting. Use when performing advanced hunting based on threat intelligence, TTPs, or anomalies. For Tier 3 analysts or dedicated threat hunters. Supports iterative search, pivoting, and comprehensive documentation.
Install
npx vskill@latest install dandye/ai-runbooks/hunt-threatSKILL.md
Advanced Threat Hunting Skill
Conduct proactive, hypothesis-driven threat hunts based on threat intelligence, observed anomalies, or specific TTPs.
Inputs
HUNT_HYPOTHESIS- Clear statement of the hunt objective (required)- Example: "Suspected DNS tunneling for C2 based on recent actor TTPs"
- Example: "Anomalous PowerShell execution on critical servers"
- Example: "Living-off-the-land techniques bypassing EDR"
- (Optional)
RELEVANT_GTI_REPORTS- GTI Collection IDs or report names - (Optional)
TARGET_SCOPE_QUERY- UDM query to narrow initial scope TIME_FRAME_HOURS- Lookback period (default: 168 = 7 days)- (Optional)
HUNT_CASE_ID- case for tracking the hunt
Workflow
Step 1: Define Hypothesis & Scope
Clearly articulate:
- What threat behavior are we looking for?
- What would evidence of this look like in logs?
- What systems/users are in scope?
- What time period is relevant?
Create or identify HUNT_CASE_ID for documentation.
Step 2: Deep Intelligence Analysis
For each relevant GTI report:
Also:
Step 3: Develop Initial Hunt Queries
Based on hypothesis and intelligence, formulate advanced queries:
SIEM queries:
BigQuery (for large-scale analysis):
Step 4: Iterative Search & Analysis
Hunt Loop:
- Execute queries
- Analyze results for outliers, suspicious patterns
- Identify leads (suspicious hosts, users, processes, connections)
- Refine hypothesis based on findings
- Develop new, more targeted queries
- Repeat until exhausted or time limit reached
Key questions at each iteration:
- Does this match our hypothesis?
- What's the baseline/normal behavior?
- Are these true anomalies or noise?
- What should we pivot on next?
Step 5: Advanced Enrichment
For each promising lead:
GTI enrichment and pivoting:
Check IOC matches:
Step 6: Continuous Documentation
Document throughout in HUNT_CASE_ID:
- Queries used (with results summary)
- Analysis reasoning
- Positive and negative findings
- Pivots and why they were taken
Use /document-in-case for each significant finding.
Step 7: Hunt Report
Use /generate-report with REPORT_TYPE="hunt_summary":
- Hypothesis and scope
- Intelligence sources used
- Queries executed
- Findings (positive and negative)
- Recommendations
Step 8: Action Based on Findings
Confirmed Threat Found: → Escalate to Incident Response immediately → Create incident case, hand over evidence
Suspicious Activity (not confirmed): → Recommend enhanced monitoring → Propose new detection rules to Security Engineering
Valuable Insights (no active threat): → Document for future reference → Propose detection improvements
Inconclusive: → Document process and limitations → Note areas for future investigation
Required Outputs
After completing this skill, you MUST report these outputs:
| Output | Description | |
|---|---|---|
HUNT_QUERIES | UDM queries executed during the hunt | |
INITIAL_FINDINGS | Raw findings from SIEM searches | |
FINDINGS_TYPE | Category: lateral_movement, credential_access, data_exfil, or generic | |
DISCOVERED_IOCS | IOCs extracted from findings (IPs, domains, hashes) | |
HIGH_CONFIDENCE_IOCS | IOCs confirmed malicious via GTI enrichment | |
THREAT_CONFIRMED | Boolean: true if active threat confirmed, false otherwise |
Hunt Hypothesis Templates
TTP-Based:
"Hunt for [MITRE Technique] activity, specifically [observable behavior], targeting [scope] over [timeframe]."
Actor-Based:
"Hunt for [Threat Actor] TTPs including [specific techniques], focusing on [likely targets] based on [intelligence source]."
Anomaly-Based:
"Investigate anomalous [behavior type] observed in [data source], specifically [anomaly description], to determine if malicious."
Example Hunt Queries
DNS Tunneling:
Suspicious PowerShell:
Living-off-the-Land:
Security
- Security scanPassed, 95 of 100Pattern checks on the skill's files.
- Model reviewPassed
Quality evaluation
- Task evaluationNot runNo evaluation yet. Run one in Skill Studio.
Source
- SKILL.md in the repositoryCheckingskills/hunt-threat/SKILL.md
Popularity
- Repository stars
- 127
- Repository forks
- 34
- 7-day trend score
- 24
Stars and forks are for the whole GitHub repository, not this skill alone. Refreshed 1 Apr 2026, 7 months ago.
Works with
Any agent vskill installs to: 56 targets in all. These 39 have a page on this site.
- Claude Code
- Cursor
- GitHub Copilot
- Windsurf
- Codex
- Gemini CLI
- Cline
- Amp
- Roo Code
- Goose
- Continue
- Kilo Code
- Junie
- Trae
- OpenHands
- Qwen Code
- AI Agent
- Tabnine Chat
- Sourcegraph Cody
- Amazon Q
- Firebase Genkit
- Sweep
- Devin
- Phind
- Mentat
- Claude Desktop
- Aider
- GPT Pilot
- Plandex
- bolt.new
- v0
- Replit Agent
- Anse
- TypingMind
- BetterChatGPT
- LibreChat
- Warp AI
- Pieces
- Fig AI
Embed badge
[](https://verified-skill.com/skills/dandye/ai-runbooks/hunt-threat)