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 gmh5225/awesome-llvm-security --skill binary-liftinggit clone --depth 1 https://github.com/gmh5225/awesome-llvm-securityWrote 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/gmh5225/awesome-llvm-security/binary-lifting)<a href="https://agentmods.dev/skills/gmh5225/awesome-llvm-security/binary-lifting"><img src="https://agentmods.dev/badge/skills/gmh5225/awesome-llvm-security/binary-lifting/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/gmh5225/awesome-llvm-security/binary-lifting"><img src="https://agentmods.dev/badge/skills/gmh5225/awesome-llvm-security/binary-lifting.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 | $0.00053 | $0.01160 |
| Opus 5.5 | $0.00021 | $0.00464 |
| Sonnet 5.5 | $0.00011 | $0.00232 |
| Haiku 4.5 | $0.00005 | $0.00116 |
Grade A, and why
binary-lifting 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.
How it starts
The opening of the file, as written. The whole thing — 150 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Binary Lifting Skill
This skill covers techniques and tools for lifting binary executables to LLVM IR, enabling advanced analysis, transformation, and recompilation of existing binaries.
Core Concepts
What is Binary Lifting?
Binary lifting is the process of translating low-level machine code (x86, ARM, etc.) into a higher-level intermediate representation (LLVM IR), enabling:
- Static and dynamic analysis
- Deobfuscation and vulnerability research
- Code recompilation and optimization
- Cross-architecture translation
Lifting Pipeline
Binary → Disassembly → IR Generation → Optimization → Analysis/Recompilation
Major Lifting Frameworks
Production-Grade Tools
- RetDec (Avast): Full decompiler with C output, multi-architecture support
- McSema (Trail of Bits): x86/x64 to LLVM IR, function recovery
- revng: Based on QEMU, supports multiple architectures
- reopt (Galois): Focus on correctness and formal methods
Research/Specialized Tools
- Rellume: Fast x86-64 to LLVM lifting for JIT scenarios
- fcd: Pattern-based decompiler with optimization passes
- bin2llvm: QEMU-based binary to LLVM translator
- llvm-mctoll: Microsoft's machine code to LLVM lifter
Language-Specific Lifters
- llvm2c/IR->C: Convert LLVM IR back to C code
- llvm2cranelift: LLVM IR to Cranelift IR
- Leaven: LLVM IR to Go language
- masxinlingvonta: JVM bytecode to LLVM IR
Implementation Techniques
Instruction Semantics Translation
// Example: Translating x86 ADD to LLVM IR
Value* translateADD(IRBuilder<> &builder, Value* op1, Value* op2) {
Value* result = builder.CreateAdd(op1, op2, "add_result");
// Update flags (CF, OF, SF, ZF, etc.)
updateCarryFlag(builder, op1, op2, result);
updateOverflowFlag(builder, op1, op2, result);
updateSignFlag(builder, result);
updateZeroFlag(builder, result);
return result;
}
Control Flow Recovery
- Linear Sweep: Simple but misses code with embedded data
- Recursive Descent: Follow control flow, better coverage
- Speculative Disassembly: Handle indirect jumps/calls
- Machine Learning: Use ML to identify function boundaries
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 · 150 lines · 53 tokens per session scan A b3b7ac1c01cc
binary-lifting is a skill published in the GitHub repository gmh5225/awesome-llvm-security (888 stars, last pushed 22d ago), licensed MIT. It adds 53 tokens to every session and 1,160 once invoked, about $0.0002 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.
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