analyzing-threat-actor-ttps-with-mitre-attack
Systematically map threat actor behavior and observed IOCs to the MITRE ATT&CK framework.
What this skill does for you
Systematically map threat actor behavior and observed IOCs to the MITRE ATT&CK framework, build technique coverage heatmaps with the ATT&CK Navigator, identify detection gaps, and produce actionable threat intelligence reports across the Enterprise, Mobile, and ICS matrices. Use when analyzing threat actor TTPs, correlating IOCs to specific ATT&CK techniques, or assessing defensive detection coverage against adversary behavior.
Quoted from the skill description in the pinned sourceWho made it
Use it with Edge
Set up Edge for me: read getedge.cc/SKILL.md and follow it. Then use Edge to load the analyzing-threat-actor-ttps-with-mitre-attack skill from mukul975/anthropic-cybersecurity-skills.
Paste it into Claude, ChatGPT, Codex or Cursor.
Or read the skill source first.
Security evidence
| Scanner | Date | Result | Findings |
|---|---|---|---|
| Edge static checks | 2026-09-27 | Pass | 0 |
| Cisco skill-scanner | 2026-09-27 | Pass | 0 |
| Semgrep Edge rules | 2026-09-27 | Pass | 0 |
Static scans check the code, not how well the skill works.
Scanner scope, raw findings and mirrored provider records
Edge static checks (Edge-run): edge-static/1.0.1. Scope: Static patterns for remote execution, credentials with network sends, obfuscation, prompt overrides, hidden Unicode, binaries and persistence. Revision: 54a798831d2266a3ca61ce68a7acb80b81160d57. Raw findings: 1. Counted findings: .
Cisco skill-scanner (Edge-run): cisco-skill-scanner/2.1.0. Scope: Local static, YARA, pipeline and behavioral analyzers; no LLM or AI Defense analysis. Revision: 54a798831d2266a3ca61ce68a7acb80b81160d57. Raw findings: 2. Counted findings: .
Semgrep Edge rules (Edge-run): semgrep/1.178.0+edge-rules.c15016a36fd3+offline-v1. Scope: Six Edge-authored static rules with Semgrep CE; offline execution and metrics disabled. Semgrep-maintained rules excluded. Revision: 54a798831d2266a3ca61ce68a7acb80b81160d57. Raw findings: . Counted findings: .
Source and licence
- Repository
- https://github.com/mukul975/anthropic-cybersecurity-skills
- Pinned skill file
- View source at revision
- Revision
- 54a798831d2266a3ca61ce68a7acb80b81160d57
- Package hash
- sha256:61c49aff5d7f10b2f0bff0ae400c5ee8820e28f96a903c508ee2600822f0e6aa
- Licence
- Apache-2.0