No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by hypnguyen1209 · Claude Skill · ★ 367
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is offensive-claude safe to install? View the security audit →
Offensive Security Research Config for Claude Code A spec-driven offensive security framework for Claude Code — structured engagement workflows based on the Cyber Kill Chain, 25 specialized skills, 6 collaborative agents, and 47 vulnerability reference files. Inspired by GitHub's spec-kit methodology. Quick Setup Skills and agents activate automatically — no additional configuration needed. Engagement Workflow Engagements follow the Cyber Kill Chain as a structured 9-phase pipeline with quality gates: Phase 0 Phase 1 Phase 2 Phase 3 Phase 4 Phase 5 Phase 6 Phase 7 Phase 8 SCOPE → RECON
| Stars | 367 |
| Forks | 61 |
| Language | Python |
| Category | Claude Skill |
| License | MIT |
| Quality Score | 71.0119073982902/100 |
| Last Updated | 2026-09-16 |
| Created | 2026-05-19 |
| Platforms | claude-code, python |
| Est. Tokens | ~16k |
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offensive-claude is Offensive security toolkit for Claude Code covering red team, exploit dev, AD attacks, EDR bypass, mobile pentest. It is categorized as a Claude Skill with 367 GitHub stars.
offensive-claude is primarily written in Python. It covers topics such as claude-code, claude-code-skills, exploit-development.
You can find installation instructions and usage details in the offensive-claude GitHub repository at github.com/hypnguyen1209/offensive-claude. The project has 367 stars and 61 forks, indicating an active community.
offensive-claude is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to offensive-claude on Agent Skills Hub include babysitter, LitterBox, getspecstory. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: