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 meltedinhex/analyst-ai-pack --skill analyzing-mach-o-binaries-on-macosgit clone --depth 1 https://github.com/meltedinhex/analyst-ai-packWrote 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/meltedinhex/analyst-ai-pack/analyzing-mach-o-binaries-on-macos)<a href="https://agentmods.dev/skills/meltedinhex/analyst-ai-pack/analyzing-mach-o-binaries-on-macos"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/analyzing-mach-o-binaries-on-macos/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/meltedinhex/analyst-ai-pack/analyzing-mach-o-binaries-on-macos"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/analyzing-mach-o-binaries-on-macos.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 | $0.00075 | $0.00780 |
| Opus 5 | $0.00037 | $0.00390 |
| Sonnet 5 | $0.00015 | $0.00156 |
| Haiku 4.5 | $0.00007 | $0.00078 |
Grade A, and why
analyzing-mach-o-binaries-on-macos 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 11d 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 — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyzing Mach-O Binaries on macOS
When to Use
- You have a macOS sample (Mach-O) and need a static capability and trust read.
- You must handle a fat/universal binary containing multiple architecture slices.
- You need to inspect linked dylibs, entitlements, and code-signing status.
Do not use Windows PE tooling on Mach-O — the formats differ entirely; use Mach-O-aware parsers.
Prerequisites
- A Mach-O parser (Python stdlib
struct, ormacholib/LIEF); the sample handled inertly. - For signing/entitlements on macOS,
codesign/otoolare authoritative.
Safety & Handling
- Parse statically; never execute the sample, especially on a real macOS host.
- Keep the sample password-protected at rest and reference it by hash.
Workflow
Step 1: Detect fat vs. thin and architecture
Check the magic: 0xCAFEBABE (fat/universal) vs. 0xFEEDFACE/0xFEEDFACF (Mach-O 32/64). For
fat binaries, enumerate and analyze each slice.
python scripts/analyst.py header sample.macho
Step 2: Parse load commands
Read load commands for linked dylibs (LC_LOAD_DYLIB), entry point (LC_MAIN), and signing
(LC_CODE_SIGNATURE). The dylib list hints at capability (networking, crypto).
Step 3: Inspect entitlements and signing
On macOS, use codesign/otool to read entitlements and verify the signature. Ad-hoc or absent
signatures and suspicious entitlements are risk indicators.
Step 4: Infer capability and route
Map linked frameworks/symbols to behaviors and route to disassembly/RE for deeper analysis.
Validation
- Fat binaries are decomposed and each slice is analyzed, not just the first.
- Load commands, dylibs, and signing status are enumerated correctly.
- Capability inferences are corroborated by linked frameworks/symbols.
Pitfalls
- Analyzing only one slice of a universal binary.
- Trusting a present signature without verifying it (ad-hoc signatures verify but aren't trusted).
- Applying PE assumptions (sections/imports) to Mach-O structures.
What ships with it
3 files 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.
- 11d ago First seen · 91 lines · 75 tokens per session scan A 17c43800c083
analyzing-mach-o-binaries-on-macos is a skill published in the GitHub repository meltedinhex/analyst-ai-pack (22 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 75 tokens to every session and 780 once invoked, about $0.0004 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 2026-08-30.
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