TORCH: Karpathy LLM based claude harness for PenetrationTesting / Bugbounty using obsidian

by Encod3d-Sec · Claude Skill · ★ 330

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Security audit verdict: SAFE · quality 66/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

bugbountyclaude-aiclaude-codeclaude-skillsctfharness-aioffensiveoffensive-securitypentestingpentesting-tools

Quick Facts

Stars330
Forks48
LanguagePython
CategoryClaude Skill
LicenseMIT
Quality Score66/100
Last Updated2026-09-01
Created2026-07-14
Platformsclaude-code, python
Est. Tokens~16k

About TORCH

TORCH An AI-powered penetration testing and bug-bounty knowledge base and automation harness for Claude Code. It turns an Obsidian vault into an opinionated offensive-security workflow: autonomous campaign drivers (, , ) that run a whole engagement end to end, a searchable wiki of 500+ hacking technique pages, per-vulnerability "hunt" skills, deterministic hooks that fire the right skill at the...

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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:

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