claude-red is a library of structured skills that give Claude specialized offensive-security methods for areas such as web vulnerabilities, shellcode, exploit development, and identity systems. It is intended for authorized red-team work, bug-bounty triage, security research, CTF preparation, and operator training. Its catalogue contains the project's skills for loading these security specializations into Claude.
Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/SnailSploit/Claude-Rednpx agentmods add skills/snailsploit/claude-red/offensive-fuzzingWrote 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/skills/snailsploit/claude-red/offensive-fuzzing)<a href="https://agentmods.dev/skills/snailsploit/claude-red/offensive-fuzzing"><img src="https://agentmods.dev/badge/skills/snailsploit/claude-red/offensive-fuzzing/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/skills/snailsploit/claude-red/offensive-fuzzing"><img src="https://agentmods.dev/badge/skills/snailsploit/claude-red/offensive-fuzzing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00091 | $0.03545 |
| Opus 5.5 | $0.00036 | $0.01418 |
| Sonnet 5.5 | $0.00018 | $0.00709 |
| Haiku 4.5 | $0.00009 | $0.00354 |
Grade A, and why
offensive-fuzzing 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 1mo 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- offensive-fuzzing — 94% identical, 40 lines differ
How it starts
The opening of the file, as written. The whole thing — 339 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Offensive Fuzzing
Fuzzer Types
| Type | Coverage | Speed | Tools |
|---|---|---|---|
| BlackBox | Poor | Fast | Peach, Boofuzz |
| GreyBox | Good | Fast | AFL++, Honggfuzz, libFuzzer, WinAFL |
| Snapshot | Good | Fastest | Nyx, wtf, Snapchange |
| WhiteBox | Best | Slow | KLEE, QSYM, SymSan |
| Ensemble | Best | Fast | AFL++ + Honggfuzz + libFuzzer |
GreyBox sub-variants: Directed (AFLGo, UAFuzz), Grammar (AFLSmart, Tlspuffin), Concolic (QSYM, Driller), Kernel (syzkaller, kAFL, wtf).
Core Workflow
Research target → Choose analyses → Build harness → Seed corpus → Instrument → Fuzz → Triage crashes → Report
1. Research Target
- Map all input surfaces (files, network, IPC, syscalls, IOCTL)
- Identify high-value areas: previously patched code, complex parsers, newly added code, input ingestion points
- For kernel modules: look beyond
copy_from_user— DMA-BUF ops, page fault handlers, VM operation structs, allocation callbacks
2. Instrument and Build
# AFL++ (preferred for GreyBox)
CC=afl-clang-fast CXX=afl-clang-fast++ cmake -DCMAKE_BUILD_TYPE=Release .. && make -j
# libFuzzer + ASan/UBSan (C/C++)
cmake -DCMAKE_CXX_FLAGS="-fsanitize=fuzzer,address,undefined -O1 -g" ..
# CmpLog build for hard compares
AFL_LLVM_CMPLOG=1 CC=afl-clang-fast CXX=afl-clang-fast++ make clean all
Windows (MSVC): Project Properties → C/C++ → Address Sanitizer: Yes (/fsanitize=address)
3. Write Harness
libFuzzer (C++):
#include <cstdint>
#include <cstddef>
extern "C" int LLVMFuzzerTestOneInput(const uint8_t* data, size_t size) {
parse_or_process(data, size);
return 0;
}
Honggfuzz HF_ITER (persistent mode — preferred for large targets):
#include "honggfuzz.h"
int main(int argc, char** argv) {
initialize_target(); // runs once
for (;;) {
size_t len; uint8_t *buf;
HF_ITER(&buf, &len);
FILE* s = fmemopen(buf, len, "r");
target_function(s);
fclose(s);
reset_target_state();
}
}
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.
- 1mo ago First seen · 339 lines · 91 tokens per session scan A fcd129b42cdf
offensive-fuzzing is a skill published in the GitHub repository SnailSploit/Claude-Red (7,709 stars, last pushed 9d ago), licensed MIT. It adds 91 tokens to every session and 3,545 once invoked, about $0.0004 per session on Opus 5.5. 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.
Other skills, from other repositories
verification-contract
Internal contract: the acceptance artifact per delivery unit — legacy (pre- feature 61) units may carry a frozen ACCEPTANCE.md (binding if present); post-feature 61 units use the unit doc's ## Acceptance criteria section as the canonical acceptance source; the unit doc's ## Evidence table serves as the execution…
user-story-writer
Escribir historias de usuario bien estructuradas con criterios de aceptación y casos extremos. Úsalo cuando te pidan escribir historias de usuario, crear tickets a partir de un resumen de características, convertir un PRD en historias o redactar criterios de aceptación. Produce historias listas para estimar en formato…
prompt-regression-suite
Design a regression test suite that catches an LLM feature getting worse when the prompt, model, or context changes. Use when asked to stop prompt changes breaking production, set up golden tests or CI gates for an LLM feature, or test a model/prompt upgrade before shipping it. Produces a golden case set, per-case…
ai-agent-reliability
Make an AI agent or automation reliable enough to trust — the tests, checks, and guardrails that catch its failures before they reach anything real. Use when asked how do I test my AI agent, make my automation reliable, my agent works sometimes, or how do I trust an AI workflow in production. Produces a map of where…
create-test-plan
Analyze what changed and generate a structured test plan at .turbo/test-plans/ .md covering four escalating levels: basic functionality, complex operations, adversarial testing, and cross-cutting scenarios. Use when the user asks to "create a test plan", "plan tests", "what should I test", "generate test scenarios"…
exploratory-test
Execute multi-level exploratory testing of the app covering basic functionality, complex operations, adversarial testing, and cross-cutting scenarios, plus usability observations through a UX lens reported separately from defects. Deeper than /smoke-test. Use when the user asks to "exploratory test", "test…