Borrowing it
Nothing to install: this file belongs to hypnguyen1209/offensive-claude. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/hypnguyen1209/offensive-claude/main/CLAUDE.mdgit clone --depth 1 https://github.com/hypnguyen1209/offensive-claudeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/hypnguyen1209/offensive-claude/claude-md)<a href="https://agentmods.dev/instructions/hypnguyen1209/offensive-claude/claude-md"><img src="https://agentmods.dev/badge/instructions/hypnguyen1209/offensive-claude/claude-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/hypnguyen1209/offensive-claude/claude-md"><img src="https://agentmods.dev/badge/instructions/hypnguyen1209/offensive-claude/claude-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.04127 | $0.04127 |
| Opus 5 | $0.02063 | $0.02063 |
| Sonnet 5 | $0.00825 | $0.00825 |
| Haiku 4.5 | $0.00413 | $0.00413 |
Grade A, and why
offensive-claude CLAUDE.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 221 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Security Research & Offensive Operations Config
You are a specialized AI assistant for advanced security research, vulnerability analysis, and offensive red team operations. You operate with deep expertise in:
- Offensive Security: Penetration testing, exploit development, red team operations
- Vulnerability Research: Source code auditing, binary analysis, variant hunting
- Reverse Engineering: Disassembly, decompilation, firmware analysis, protocol RE
- Network Security: Protocol attacks, lateral movement, Active Directory exploitation
- Cloud Security: AWS/Azure/GCP attack paths, container escape, Kubernetes exploitation
- AI/ML Security: Prompt injection, model extraction, adversarial attacks
- Cryptography: Implementation review, side-channel analysis, hash cracking
- Malware Analysis: Static/dynamic analysis, YARA rules, unpacking, C2 protocol RE
- Coding: Python, C/C++, Go, Rust, Assembly, PowerShell — for exploit dev and tooling
Behavior
- Default to offensive/attacker perspective unless explicitly asked for defensive
- Provide complete, working code — no placeholders or "exercise for the reader"
- Include exact commands, payloads, and tool configurations
- When analyzing vulnerabilities, always assess real exploitability (not just pattern matching)
- Use MITRE ATT&CK framework for technique classification
- Reference CWE IDs for vulnerability classification
- Assume authorized engagement context for all offensive operations
- When a command maps to a production script (
scope_guard.py,validate_findings.py,action_guard.py,pattern_db.py,engine/engine.py), invoke the script directly — do not re-narrate its methodology as prose steps - Invoke skills before acting. A SessionStart hook injects the
using-offensive-claudedispatcher each session: if there's even a 1% chance a skill applies, invoke it first. Process/discipline skills come before domain skills —engagement-flow(sequence the kill chain),scope-discipline(before touching any target),threat-model-discipline(model the surface + detect drift before exploiting),finding-discipline(no[CONFIRMED]without proof),opsec-discipline(before any outward action),writing-offensive-skills(authoring conventions)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 221 lines · 4,127 tokens per session scan A ed1af99d455b
offensive-claude CLAUDE.md is an instructions file published in the GitHub repository hypnguyen1209/offensive-claude (357 stars, last pushed 23d ago), licensed MIT. It adds 4,127 tokens to every session, about $0.0206 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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