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/mohitmishra786/low-level-dev-skillsnpx agentmods add skills/mohitmishra786/low-level-dev-skills/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/mohitmishra786/low-level-dev-skills/fuzzing)<a href="https://agentmods.dev/skills/mohitmishra786/low-level-dev-skills/fuzzing"><img src="https://agentmods.dev/badge/skills/mohitmishra786/low-level-dev-skills/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/mohitmishra786/low-level-dev-skills/fuzzing"><img src="https://agentmods.dev/badge/skills/mohitmishra786/low-level-dev-skills/fuzzing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- 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 | $0.00093 | $0.02371 |
| Opus 5 | $0.00046 | $0.01185 |
| Sonnet 5 | $0.00019 | $0.00474 |
| Haiku 4.5 | $0.00009 | $0.00237 |
Grade A, and why
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 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 — 318 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fuzzing
Purpose
Guide agents through setting up and running coverage-guided fuzz testing: libFuzzer (in-process) and AFL++ (fork-based), with sanitizer integration and CI pipeline setup.
Triggers
- "How do I fuzz-test my parser/deserializer?"
- "What is a fuzz target / how do I write one?"
- "How do I set up libFuzzer?"
- "How do I use AFL++ on my program?"
- "How do I run fuzzing in CI?"
- "Fuzzer found a crash — how do I reproduce it?"
Workflow
1. Write a fuzz target (libFuzzer)
A fuzz target is a function that accepts arbitrary bytes and exercises the code under test.
// fuzz_parser.c
#include <stdint.h>
#include <stddef.h>
#include "myparser.h"
// Entry point called by libFuzzer with random data
int LLVMFuzzerTestOneInput(const uint8_t *data, size_t size) {
// Must not abort/exit on invalid input (that's expected)
// Must not read outside [data, data+size)
MyParser *p = parser_create();
if (p) {
parser_feed(p, (const char *)data, size);
parser_destroy(p);
}
return 0; // Always return 0 (non-zero means discard input)
}
Key rules:
- Never call
abort(),exit(), or use global state that persists across calls - Handle all inputs gracefully (crash = bug found)
- Keep the target fast: the fuzzer calls it millions of times
2. Build with libFuzzer
# Clang (libFuzzer is built into Clang)
clang -fsanitize=fuzzer,address -g -O1 \
fuzz_parser.c myparser.c -o fuzz_parser
# With UBSan too
clang -fsanitize=fuzzer,address,undefined -g -O1 \
fuzz_parser.c myparser.c -o fuzz_parser
-fsanitize=fuzzer links libFuzzer and provides main(). Do not provide your own main() in the fuzz target.
3. Run libFuzzer
# Create corpus directory
mkdir -p corpus
# Seed with known-good inputs (greatly accelerates coverage)
cp tests/inputs/* corpus/
# Run the fuzzer
./fuzz_parser corpus/ -max_len=65536 -timeout=10
# Run for a time limit
./fuzz_parser corpus/ -max_total_time=3600
# Run with specific number of jobs (parallel)
./fuzz_parser corpus/ -jobs=4 -workers=4
# Minimise a corpus (remove redundant inputs)
./fuzz_parser -merge=1 corpus_min/ corpus/
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 318 lines · 93 tokens per session scan A 9693fdd9c32d
fuzzing is a skill published in the GitHub repository mohitmishra786/low-level-dev-skills (203 stars, last pushed 2mo ago), licensed MIT. It adds 93 tokens to every session and 2,371 once invoked, about $0.0005 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 2026-09-03.
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