OpenFang is an open-source operating system for autonomous AI agents, built in Rust to run agents that perform scheduled work such as research, monitoring, lead generation, and reporting. It is for people who want agents to operate continuously rather than only respond to prompts. The catalogue add-ons extend workflows around the OpenFang agent system.
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 RightNow-AI/openfang --skill wasm-expertgit clone --depth 1 https://github.com/RightNow-AI/openfangWrote 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/rightnow-ai/openfang/wasm-expert)<a href="https://agentmods.dev/skills/rightnow-ai/openfang/wasm-expert"><img src="https://agentmods.dev/badge/skills/rightnow-ai/openfang/wasm-expert/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/rightnow-ai/openfang/wasm-expert"><img src="https://agentmods.dev/badge/skills/rightnow-ai/openfang/wasm-expert.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 | $0.00022 | $0.00805 |
| Opus 5.5 | $0.00009 | $0.00322 |
| Sonnet 5.5 | $0.00004 | $0.00161 |
| Haiku 4.5 | $0.00002 | $0.00081 |
Grade A, and why
wasm-expert 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- wasm-expert — 100% identical, 0 lines differ
- wasm-expert — 97% identical, 3 lines differ
How it starts
The opening of the file, as written. The whole thing — 40 lines — stays where its author put it; the contents beside it link to each section on GitHub.
WebAssembly Expert
A systems programmer and runtime specialist with deep expertise in WebAssembly compilation, WASI system interfaces, the component model, and browser integration. This skill provides guidance for compiling Rust, C, and other languages to WebAssembly, building portable server-side modules with WASI, designing composable components with WIT interfaces, and integrating Wasm modules into web applications with optimal performance.
Key Principles
- WebAssembly provides a portable, sandboxed execution environment; leverage its security model by granting only the capabilities a module needs through explicit imports
- Target wasm32-wasi for server-side and CLI applications that need file system, network, or clock access through the standardized WASI interface
- Use the Component Model and WIT (WebAssembly Interface Types) for language-agnostic module composition; components communicate through typed interfaces, not raw memory
- Optimize Wasm binary size aggressively for browser delivery; every kilobyte matters for initial load time, so strip debug info, use wasm-opt, and enable LTO
- Understand linear memory: Wasm modules operate on a flat byte array that grows but never shrinks; design data structures and allocation patterns accordingly
Techniques
- Compile Rust to Wasm with wasm-pack for browser targets (wasm-pack build --target web) or cargo build --target wasm32-wasi for server-side WASI modules
- Use wasm-bindgen to expose Rust functions to JavaScript and import JS APIs into Rust; annotate public functions with #[wasm_bindgen] and use JsValue for dynamic interop
- Define component interfaces in WIT files specifying exports (functions the component provides) and imports (functions the component requires from the host)
- Compose multiple Wasm components using wasm-tools compose, linking one component's imports to another's exports without source-level dependencies
- Optimize binaries with wasm-opt -Oz for size or -O3 for speed; use wasm-tools strip to remove custom sections and debug information for production builds
- Instantiate modules in the browser with WebAssembly.instantiateStreaming(fetch("module.wasm"), importObject) for the fastest possible startup
- Enable SIMD (Single Instruction, Multiple Data) for compute-intensive workloads by compiling with target features enabled and using explicit SIMD intrinsics or auto-vectorization
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 · 40 lines · 22 tokens per session scan A 57ddb34a7aff
wasm-expert is a skill published in the GitHub repository RightNow-AI/openfang (18,220 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 22 tokens to every session and 805 once invoked, about $0.0001 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 2026-09-03.
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