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/cyber-threat-hunting

Conducts hypothesis-driven, evidence-preserving threat hunts with bounded negative conclusions and telemetry-gap handling. Use for hunt hypotheses, scoped offline analysis, or analytic follow-up; not unscoped scanning, malware execution, or generic intelligence summaries.

From plugin
agent-skills-standard
570200 skills21 agents33 commands1 MCP
Install
$ npx -y skills add hoangnguyen0403/agent-skills-standard --skill cyber-threat-hunting --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/cyber-threat-hunting

Context preview

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

Conducts hypothesis-driven, evidence-preserving threat hunts with bounded negative conclusions and telemetry-gap handling. Use for hunt hypotheses, scoped offline analysis, or analytic follow-up; not unscoped scanning, malware execution, or generic intelligence summaries.

SKILL.md

cyber-threat-hunting.SKILL.md
name: cyber-threat-hunting
guardrail: true
description: Conducts hypothesis-driven, evidence-preserving threat hunts with bounded negative conclusions and telemetry-gap handling. Use for hunt hypotheses, scoped offline analysis, or analytic follow-up; not unscoped scanning, malware execution, or generic intelligence summaries.
metadata:
  triggers:
    files: []
    keywords:
      - threat hunting
      - hunt hypothesis
      - hunting query
      - negative conclusion
      - suspicious activity hunt
      - telemetry gap

Cyber Threat Hunting

**Priority: P0 (CRITICAL)**

Preserve provenance and bound every conclusion to authorized evidence.

Structure

Input: falsifiable hypothesis, behavior, entities, scope, sources, window, expected signal, stop condition. Process: define positive/negative observations; normalize without overwriting originals; test narrowest evidence; correlate by entity/time; record gaps and alternatives. Output: status, cited evidence, bounded conclusion, limitations, owner.

Rules

  • Active collection or network operations require `cyber-authorization`, documented scope, and runtime-proven controls; unsupported or missing scope blocks execution.
  • Use controlled synthetic/offline fixtures. Preserve query/version, fixture provenance, retention, clock, identity, and source limitations.
  • Status is `confirmed`, `suspected`, `false-positive`, `blocked`, or `not-tested`; only cited evidence supports a status.
  • “No evidence found in the examined dataset/time window” is bounded negative evidence, never proof of no compromise.
  • Include engagement/scope reference; skill/version/source; observation time; status; evidence references; limitations; accountable owner. Reuse shared skill IDs.

Anti-Patterns

  • Never call an empty or incomplete dataset clean.
  • Never treat one ambiguous hit as confirmed compromise.
  • Never collect live telemetry before authorization or invent hunt metrics.

References (lazy, primary)

  • MITRE ATT&CK data sources: https://attack.mitre.org/datasources/
  • NIST SP 800-86 evidence handling: https://doi.org/10.6028/NIST.SP.800-86
  • Reuse `cyber-authorization`, `cyber-evidence`, `cyber-framework-mapping`.
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