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 onfire7777/universal-ai-skills-library --skill atherisgit clone --depth 1 https://github.com/onfire7777/universal-ai-skills-libraryWrote 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/onfire7777/universal-ai-skills-library/atheris)<a href="https://agentmods.dev/skills/onfire7777/universal-ai-skills-library/atheris"><img src="https://agentmods.dev/badge/skills/onfire7777/universal-ai-skills-library/atheris/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/onfire7777/universal-ai-skills-library/atheris"><img src="https://agentmods.dev/badge/skills/onfire7777/universal-ai-skills-library/atheris.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 | $0.00032 | $0.03578 |
| Opus 5.5 | $0.00013 | $0.01431 |
| Sonnet 5.5 | $0.00006 | $0.00716 |
| Haiku 4.5 | $0.00003 | $0.00358 |
Grade C, and why
atheris scanned grade C with 2 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.
Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
&& rm -rf /var/lib/apt/lists/* Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
wget \ This is a copy
97% identical to atheris — 12 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 — 515 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Atheris
Atheris is a coverage-guided Python fuzzer built on libFuzzer. It enables fuzzing of both pure Python code and Python C extensions with integrated AddressSanitizer support for detecting memory corruption issues.
When to Use
| Fuzzer | Best For | Complexity |
|---|---|---|
| Atheris | Python code and C extensions | Low-Medium |
| Hypothesis | Property-based testing | Low |
| python-afl | AFL-style fuzzing | Medium |
Choose Atheris when:
- Fuzzing pure Python code with coverage guidance
- Testing Python C extensions for memory corruption
- Integration with libFuzzer ecosystem is desired
- AddressSanitizer support is needed
Quick Start
import sys
import atheris
@atheris.instrument_func
def test_one_input(data: bytes):
if len(data) == 4:
if data[0] == 0x46: # "F"
if data[1] == 0x55: # "U"
if data[2] == 0x5A: # "Z"
if data[3] == 0x5A: # "Z"
raise RuntimeError("You caught me")
def main():
atheris.Setup(sys.argv, test_one_input)
atheris.Fuzz()
if __name__ == "__main__":
main()
Run:
python fuzz.py
Installation
Atheris supports 32-bit and 64-bit Linux, and macOS. We recommend fuzzing on Linux because it's simpler to manage and often faster.
Prerequisites
- Python 3.7 or later
- Recent version of clang (preferably latest release)
- For Docker users: Docker Desktop
Linux/macOS
uv pip install atheris
Docker Environment (Recommended)
For a fully operational Linux environment with all dependencies configured:
# https://hub.docker.com/_/python
ARG PYTHON_VERSION=3.11
FROM python:$PYTHON_VERSION-slim-bookworm
RUN python --version
RUN apt update && apt install -y \
ca-certificates \
wget \
&& rm -rf /var/lib/apt/lists/*
# LLVM builds version 15-19 for Debian 12 (Bookworm)
# https://apt.llvm.org/bookworm/dists/
ARG LLVM_VERSION=19
RUN echo "deb http://apt.llvm.org/bookworm/ llvm-toolchain-bookworm-$LLVM_VERSION main" > /etc/apt/sources.list.d/llvm.list
RUN echo "deb-src http://apt.llvm.org/bookworm/ llvm-toolchain-bookworm-$LLVM_VERSION main" >> /etc/apt/sources.list.d/llvm.list
RUN wget -qO- https://apt.llvm.org/llvm-snapshot.gpg.key > /etc/apt/trusted.gpg.d/apt.llvm.org.asc
RUN apt update && apt install -y \
build-essential \
clang-$LLVM_VERSION \
&& rm -rf /var/lib/apt/lists/*
ENV APP_DIR "/app"
RUN mkdir $APP_DIR
WORKDIR $APP_DIR
ENV VIRTUAL_ENV "/opt/venv"
RUN python -m venv $VIRTUAL_ENV
ENV PATH "$VIRTUAL_ENV/bin:$PATH"
# https://github.com/google/atheris/blob/master/native_extension_fuzzing.md#step-1-compiling-your-extension
ENV CC="clang-$LLVM_VERSION"
ENV CFLAGS "-fsanitize=address,fuzzer-no-link"
ENV CXX="clang++-$LLVM_VERSION"
ENV CXXFLAGS "-fsanitize=address,fuzzer-no-link"
ENV LDSHARED="clang-$LLVM_VERSION -shared"
ENV LDSHAREDXX="clang++-$LLVM_VERSION -shared"
ENV ASAN_SYMBOLIZER_PATH="/usr/bin/llvm-symbolizer-$LLVM_VERSION"
# Allow Atheris to find fuzzer sanitizer shared libs
# https://github.com/google/atheris#building-from-source
RUN LIBFUZZER_LIB=$($CC -print-file-name=libclang_rt.fuzzer_no_main-$(uname -m).a) \
python -m pip install --no-binary atheris atheris
# https://github.com/google/atheris/blob/master/native_extension_fuzzing.md#option-a-sanitizerlibfuzzer-preloads
ENV LD_PRELOAD "$VIRTUAL_ENV/lib/python3.11/site-packages/asan_with_fuzzer.so"
# 1. Skip memory allocation failures for now, they are common, and low impact (DoS)
# 2. https://github.com/google/atheris/blob/master/native_extension_fuzzing.md#leak-detection
ENV ASAN_OPTIONS "allocator_may_return_null=1,detect_leaks=0"
CMD ["/bin/bash"]
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 · 515 lines · 32 tokens per session scan C 0ba43b22ac4b
atheris is a skill published in the GitHub repository onfire7777/universal-ai-skills-library (16 stars, last pushed 1mo ago), licensed MIT. It adds 32 tokens to every session and 3,578 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it C with 2 findings (recursive force delete, makes network calls). It is 97% identical to atheris, differing in 12 lines, and is treated as a copy.
Other skills, from other repositories
verify-work
Prove that code, a script or a page you just produced actually runs — execute it, read the real output, and fix what breaks before reporting. Use after writing or changing anything executable, and whenever you are about to say something is done, working, or fixed.
agent-eval
Use when measuring whether an LLM or agent system actually got better and gating merges on it: golden sets, fixing an inflated LLM-as-judge, scoring RAG (faithfulness, contextual recall) or agent trajectories (tool correctness, completion), or picking an eval framework. NOT building the agent loop, tools or RAG…
agent-browser
Use the host-side agent-browser CLI for local browser smoke tests, screenshots, snapshots, and simple UI validation against forwarded localhost URLs.
browser-qa
Testing web apps through a real browser: reconnaissance before acting, selector discovery, state capture, console errors and screenshot evidence. Use on a web surface end to end, or to reproduce a front-end bug.
test-driven-development
Writing code with tests first: RED-GREEN-REFACTOR, reproduce bugs as failing tests before fixing, and avoid testing anti-patterns. Use when implementing behaviour, fixing a defect, or asked to add tests.
verification-before-completion
Proving work works before claiming it does: exercise the real path, read the actual output, gather fresh evidence per claim, and say what remains unverified. Use before reporting done, handing off, or posting results.