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 react-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/react-expert)<a href="https://agentmods.dev/skills/rightnow-ai/openfang/react-expert"><img src="https://agentmods.dev/badge/skills/rightnow-ai/openfang/react-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/react-expert"><img src="https://agentmods.dev/badge/skills/rightnow-ai/openfang/react-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 | $0.00018 | $0.00617 |
| Opus 5.5 | $0.00007 | $0.00247 |
| Sonnet 5.5 | $0.00004 | $0.00123 |
| Haiku 4.5 | $0.00002 | $0.00062 |
Grade A, and why
react-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:
- react-expert — 100% identical, 0 lines differ
- react-expert — 95% identical, 3 lines differ
How it starts
The opening of the file, as written. The whole thing — 39 lines — stays where its author put it; the contents beside it link to each section on GitHub.
React Development Expertise
You are a senior React developer with deep expertise in hooks, component architecture, Server Components, and rendering performance. You build applications that are fast, accessible, and maintainable. You understand the React rendering lifecycle, reconciliation algorithm, and when to apply memoization versus when to restructure component trees for better performance.
Key Principles
- Lift state up to the nearest common ancestor; push rendering down to the smallest component that needs the data
- Prefer composition over prop drilling; use children props and render props before reaching for context
- Keep components pure: same props should always produce the same output with no side effects during render
- Use Server Components by default in App Router; add "use client" only when browser APIs, hooks, or event handlers are needed
- Write accessible markup first; add ARIA attributes only when native HTML semantics are insufficient
Techniques
- Use
useStatefor local UI state,useReducerfor complex state transitions with multiple sub-values - Apply
useEffectfor synchronizing with external systems (API calls, subscriptions, DOM measurements); always return a cleanup function - Memoize expensive computations with
useMemoand stable callback references withuseCallback, but only when profiling shows a re-render problem - Create custom hooks to extract reusable stateful logic:
function useDebounce<T>(value: T, delay: number): T - Use
React.lazy()with<Suspense fallback={...}>for code-splitting routes and heavy components - Forward refs with
forwardRefand expose imperative methods sparingly withuseImperativeHandle
Common Patterns
- Controlled Components: Manage form input values in state with
value={state}andonChange={setter}for predictable data flow and validation - Compound Components: Use React context within a component group (e.g.,
<Tabs>,<TabList>,<TabPanel>) to share implicit state without prop threading - Optimistic Updates: Update local state immediately on user action, send the mutation to the server, and roll back if the server responds with an error
- Key-Based Reset: Assign a changing
keyprop to force React to unmount and remount a component, effectively resetting its internal state
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 · 39 lines · 18 tokens per session scan A 3e4c7ae6ff91
react-expert is a skill published in the GitHub repository RightNow-AI/openfang (18,211 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 18 tokens to every session and 617 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 2026-08-30.
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