Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add trilwu/secskills/plugin install secskills-coreWrote 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/trilwu/secskills/analyzing-rust-binaries)<a href="https://agentmods.dev/skills/trilwu/secskills/analyzing-rust-binaries"><img src="https://agentmods.dev/badge/skills/trilwu/secskills/analyzing-rust-binaries.svg" alt="Measured on agentmods" 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 | $0.00097 | $0.02011 |
| Opus 5 | $0.00048 | $0.01006 |
| Sonnet 5 | $0.00019 | $0.00402 |
| Haiku 4.5 | $0.00010 | $0.00201 |
Grade A, and why
analyzing-rust-binaries 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 — 183 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyzing Rust Binaries
Rust binaries are large, statically linked, and full of inlined generic code —
but they leak more than most people expect. Panic sites embed the source file
path and line, symbols carry crate and module structure, and the standard
library's formatting machinery is instantly recognizable. The job is knowing
which parts are yours and which are the 90% that is core, alloc, and
vendored crates.
When to Use
stringsshowsrustc version,core::panicking,/rustc/<hash>/library/- Symbols begin with
_ZN...17h<hex>E(legacy) or_R(v0 mangling) - A stripped binary is 3–30 MB with minimal dynamic imports
- Analyzing Rust malware, a Rust CLI, or a compiled Rust service
When NOT to Use
- Go binaries — use
analyzing-go-binaries; symbol recovery differs entirely - .NET or Unity — use the matching skill
- Suspected malware, before containment — use
analyzing-malwarefirst - Source is available — use
auditing-code-for-vulnerabilities, which has the Rustunsafechecklist
Confirm and Fingerprint
strings -n 8 target | rg -m8 'rustc version|/rustc/[0-9a-f]{40}|core::panicking|cargo/registry'
Two artifacts do most of the work before you disassemble anything:
Panic strings leak the source tree. Rust embeds the file path and line
number of every panic!, unwrap(), and bounds check. That gives you the
crate layout, the developer's directory structure, and often the project name.
strings -n 10 target | rg 'src/[a-z_/]+\.rs' | sort -u | head -40
# → src/main.rs, src/crypto/aes.rs, /home/dev/projects/implant/src/c2.rs
Registry paths name the dependencies. Vendored crates compiled in leave
their ~/.cargo/registry/src/.../<crate>-<version>/ paths in panic sites,
which is effectively a dependency list.
strings target | rg -o 'cargo/registry/src/[^/]+/([a-z0-9_-]+-[0-9.]+)' -r '$1' | sort -u
That combination — module layout plus dependency list — usually tells you what the binary does before a single instruction is read.
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 · 183 lines · 97 tokens per session scan A e2060fef3b44
analyzing-rust-binaries is a skill published in the GitHub repository trilwu/secskills (137 stars, last pushed 4d ago), licensed MIT. It adds 97 tokens to every session and 2,011 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 2026-08-30.
Other skills, from other repositories
cardputer-buddy
Iterate on the Cardputer-Adv MicroPython app bundle (Claude Buddy, Snake, Hello) after the device is already provisioned via m5-onboard. Use when the user wants to add a new app, push a single changed .py without re-flashing, watch device serial logs, or run a one-shot REPL command. Trigger on "add an app", "push to…
developing-genkit-js
Develop AI-powered applications using Genkit in Node.js/TypeScript. Use when the user asks about Genkit, AI agents, flows, or tools in JavaScript/TypeScript, or when encountering Genkit errors, validation issues, type errors, or API problems.
wear-compose-m3
Expert guidance for working with Wear OS Compose Material3. Use this skill when creating, updating, or migrating Wear OS projects. This includes the androidx.wear.compose.material3, androidx.wear.compose.foundation, and androidx.wear.compose.navigation3 libraries. Also working with core components such as AppScaffold…
new
Create a new project to start development quickly.
developing-genkit-dart
Generates code and provides documentation for the Genkit Dart SDK. Use when the user asks to build AI agents in Dart, use Genkit flows, or integrate LLMs into Dart/Flutter applications.
developing-genkit-go
Develop AI-powered applications using Genkit in Go. Use when the user asks to build AI features, agents, flows, or tools in Go using Genkit, or when working with Genkit Go code involving generation, prompts, streaming, tool calling, or model providers.