Argent is a toolkit that lets an AI assistant control and inspect iOS, Android, TV, Electron, and Chromium applications through simulators, emulators, physical devices, or desktop connections. It is for developers who want an agent to interact with interfaces, reproduce issues, test features, debug applications, or run profiling tasks. The catalogue add-ons expose Argent's device-control and development workflows to coding agents.
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 software-mansion/argent --skill argent-react-native-optimizationgit clone --depth 1 https://github.com/software-mansion/argentWrote 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/software-mansion/argent/argent-react-native-optimization)<a href="https://agentmods.dev/skills/software-mansion/argent/argent-react-native-optimization"><img src="https://agentmods.dev/badge/skills/software-mansion/argent/argent-react-native-optimization/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/software-mansion/argent/argent-react-native-optimization"><img src="https://agentmods.dev/badge/skills/software-mansion/argent/argent-react-native-optimization.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- 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 | $0.00071 | $0.01146 |
| Opus 5.5 | $0.00028 | $0.00458 |
| Sonnet 5.5 | $0.00014 | $0.00229 |
| Haiku 4.5 | $0.00007 | $0.00115 |
Grade A, and why
argent-react-native-optimization 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 3d 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 — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Rules
- Do not apply shotgun optimizations. Measure first, define what "good enough" looks like (target metric + threshold), fix the top offender, re-measure honestly.
- Quick scan:
react-profiler-rendersfor a live render count table. Identifies hot components instantly. - Deep measure: load
argent-react-native-profilerskill.react-profiler-start→ interact →react-profiler-stop→react-profiler-analyze. - Inspect:
react-profiler-component-sourceper finding.react-profiler-fiber-treeto trace component ancestry and render cost. - Verify correctness - before fixing, recollect information from steps above and make a logical conclusion whether the approach is worth undertaking.
- Fix: apply one fix. Validate with
debugger-evaluatebefore committing. - Re-measure: report whether the target metric improved, regressed, or stayed flat. Check for regressions in other areas. If no net benefit or unacceptable tradeoffs, revert.
- Profile for discovery, not only verification. Use the profiler to find issues static analysis missed, not only to confirm fixes.
- One fix per cycle for architectural changes. Mechanical batch fixes (inline styles, index keys) can be grouped; re-profile once after the batch. When the measurement involves device interaction, record it as a flow (
argent-create-flowskill) before the first run so all subsequent cycles replay identical steps. - React Compiler: if
react-profiler-analyzereportsreactCompilerEnabled: true, do NOT proposeuseCallback/useMemo/React.memounless you confirmed compiler bail-out viareact-profiler-fiber-tree(absentuseMemoCache). - Sub-agents: Phases 1–2 dispatch sub-agents: one per file for lint results, one per checklist item for semantic. Sub-agents CANNOT touch the device - all profiling and E2E verification must happen in the main agent.
Pipeline
Lint and semantic sweeps catch deterministic issues cheaply. Profiling finds runtime bottlenecks that static analysis misses. Do both.
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.
- 3d ago Changed 5f6aa1a00d25
- 1mo ago First seen · 65 lines · 71 tokens per session scan A e4ed682f1501
argent-react-native-optimization is a skill published in the GitHub repository software-mansion/argent (3,042 stars, last pushed today), licensed Apache-2.0. It adds 71 tokens to every session and 1,146 once invoked, about $0.0003 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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