Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add marduk191/qwen3_mcp --skill aflppgit clone --depth 1 https://github.com/marduk191/qwen3_mcpWrote 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/marduk191/qwen3_mcp/aflpp)<a href="https://agentmods.dev/skills/marduk191/qwen3_mcp/aflpp"><img src="https://agentmods.dev/badge/skills/marduk191/qwen3_mcp/aflpp/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/marduk191/qwen3_mcp/aflpp"><img src="https://agentmods.dev/badge/skills/marduk191/qwen3_mcp/aflpp.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 | $0.00033 | $0.05238 |
| Opus 5 | $0.00016 | $0.02619 |
| Sonnet 5 | $0.00007 | $0.01048 |
| Haiku 4.5 | $0.00003 | $0.00524 |
Grade A, and why
aflpp scanned grade A with 1 finding 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 10d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -O https://raw.githubusercontent.com/AFLplusplus/AFLplusplus/stable/utils/argv_fuzzing/argv-fuzz-inl.h This is a copy
81% identical to aflpp — 138 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 641 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AFL++
AFL++ is a fork of the original AFL fuzzer that offers better fuzzing performance and more advanced features while maintaining stability. A major benefit over libFuzzer is that AFL++ has stable support for running fuzzing campaigns on multiple cores, making it ideal for large-scale fuzzing efforts.
When to Use
| Fuzzer | Best For | Complexity |
|---|---|---|
| AFL++ | Multi-core fuzzing, diverse mutations, mature projects | Medium |
| libFuzzer | Quick setup, single-threaded, simple harnesses | Low |
| LibAFL | Custom fuzzers, research, advanced use cases | High |
Choose AFL++ when:
- You need multi-core fuzzing to maximize throughput
- Your project can be compiled with Clang or GCC
- You want diverse mutation strategies and mature tooling
- libFuzzer has plateaued and you need more coverage
- You're fuzzing production codebases that benefit from parallel execution
Quick Start
extern "C" int LLVMFuzzerTestOneInput(const uint8_t *data, size_t size) {
// Call your code with fuzzer-provided data
check_buf((char*)data, size);
return 0;
}
Compile and run:
# Setup AFL++ wrapper script first (see Installation)
./afl++ docker afl-clang-fast++ -DNO_MAIN=1 -O2 -fsanitize=fuzzer harness.cc main.cc -o fuzz
mkdir seeds && echo "aaaa" > seeds/minimal_seed
./afl++ docker afl-fuzz -i seeds -o out -- ./fuzz
Installation
AFL++ has many dependencies including LLVM, Python, and Rust. We recommend using a current Debian or Ubuntu distribution for fuzzing with AFL++.
| Method | When to Use | Supported Compilers |
|---|---|---|
| Ubuntu/Debian repos | Recent Ubuntu, basic features only | Ubuntu 23.10: Clang 14 & GCC 13Debian 12: Clang 14 & GCC 12 |
| Docker (from Docker Hub) | Specific AFL++ version, Apple Silicon support | As of 4.35c: Clang 19 & GCC 11 |
| Docker (from source) | Test unreleased features, apply patches | Configurable in Dockerfile |
| From source | Avoid Docker, need specific patches | Adjustable via LLVM_CONFIG env var |
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.
- 10d ago First seen · 641 lines · 33 tokens per session scan A cb4aa4b4f64b
aflpp is a skill published in the GitHub repository marduk191/qwen3_mcp (13 stars, last pushed 7mo ago), licensed MIT. It adds 33 tokens to every session and 5,238 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 81% identical to aflpp, differing in 138 lines, and is treated as a copy.
Other skills, from other repositories
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
tika-eval-compare
Compare extracts from two Tika builds over a corpus to detect regressions in content, encoding, exceptions, and embedded-document handling. Use for "compare before/after extracts", "eval this change against the corpus".
neuron-evaluation-engineer
Create and run AI evaluations with datasets, assertions, and output drivers in Neuron AI. Use this skill whenever the user mentions evaluation, testing AI systems, creating evaluators, dataset-driven testing, assertion-based validation, or wants to measure AI system performance. Also trigger for tasks involving…
jetson-validate-image
Use after jetson-flash-image to run static BSP checks, on-target smoke/regression tests on a flashed DUT, or both. Not for build or flash steps. Triggers: validate bsp, on-target validation.
atmos-validation
Validate Atmos projects, components, arbitrary JSON Schema inputs, EditorConfig, and GitHub Actions; use affected-file selection and native CI annotations.
skill-benchmark
Benchmark AI skill effectiveness by measuring implementation quality against legacy constraints.