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 address-sanitizergit 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/address-sanitizer)<a href="https://agentmods.dev/skills/onfire7777/universal-ai-skills-library/address-sanitizer"><img src="https://agentmods.dev/badge/skills/onfire7777/universal-ai-skills-library/address-sanitizer/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/address-sanitizer"><img src="https://agentmods.dev/badge/skills/onfire7777/universal-ai-skills-library/address-sanitizer.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 | $0.00035 | $0.02777 |
| Opus 5 | $0.00017 | $0.01388 |
| Sonnet 5 | $0.00007 | $0.00555 |
| Haiku 4.5 | $0.00003 | $0.00278 |
Grade A, and why
address-sanitizer 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 12d 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.
This is a copy
95% identical to address-sanitizer — 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 — 341 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AddressSanitizer (ASan)
AddressSanitizer (ASan) is a widely adopted memory error detection tool used extensively during software testing, particularly fuzzing. It helps detect memory corruption bugs that might otherwise go unnoticed, such as buffer overflows, use-after-free errors, and other memory safety violations.
Overview
ASan is a standard practice in fuzzing due to its effectiveness in identifying memory vulnerabilities. It instruments code at compile time to track memory allocations and accesses, detecting illegal operations at runtime.
Key Concepts
| Concept | Description |
|---|---|
| Instrumentation | ASan adds runtime checks to memory operations during compilation |
| Shadow Memory | Maps 20TB of virtual memory to track allocation state |
| Performance Cost | Approximately 2-4x slowdown compared to non-instrumented code |
| Detection Scope | Finds buffer overflows, use-after-free, double-free, and memory leaks |
When to Apply
Apply this technique when:
- Fuzzing C/C++ code for memory safety vulnerabilities
- Testing Rust code with unsafe blocks
- Debugging crashes related to memory corruption
- Running unit tests where memory errors are suspected
Skip this technique when:
- Running production code (ASan can reduce security)
- Platform is Windows or macOS (limited ASan support)
- Performance overhead is unacceptable for your use case
- Fuzzing pure safe languages without FFI (e.g., pure Go, pure Java)
Quick Reference
| Task | Command/Pattern |
|---|---|
| Enable ASan (Clang/GCC) | -fsanitize=address |
| Enable verbosity | ASAN_OPTIONS=verbosity=1 |
| Disable leak detection | ASAN_OPTIONS=detect_leaks=0 |
| Force abort on error | ASAN_OPTIONS=abort_on_error=1 |
| Multiple options | ASAN_OPTIONS=verbosity=1:abort_on_error=1 |
Step-by-Step
Step 1: Compile with ASan
Compile and link your code with the -fsanitize=address flag:
clang -fsanitize=address -g -o my_program my_program.c
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.
- 12d ago First seen · 341 lines · 35 tokens per session scan A 3cd4d95902ad
address-sanitizer is a skill published in the GitHub repository onfire7777/universal-ai-skills-library (16 stars, last pushed yesterday), licensed MIT. It adds 35 tokens to every session and 2,777 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to address-sanitizer, differing in 12 lines, and is treated as a copy.
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