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-tv-interactgit 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-tv-interact)<a href="https://agentmods.dev/skills/software-mansion/argent/argent-tv-interact"><img src="https://agentmods.dev/badge/skills/software-mansion/argent/argent-tv-interact/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-tv-interact"><img src="https://agentmods.dev/badge/skills/software-mansion/argent/argent-tv-interact.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Prompt Injection · line 46 This pattern attempts to override system instructions or ignore safety constraints. Without LLM analysis, manual review is recommended.Fix: Remove or rewrite any text that instructs the agent to ignore prompts, override safety rules, or trust unverified content. Ensure skill content cannot be injected to alter agent behavior.
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 | $0.00107 | $0.01985 |
| Opus 5.5 | $0.00043 | $0.00794 |
| Sonnet 5.5 | $0.00021 | $0.00397 |
| Haiku 4.5 | $0.00011 | $0.00198 |
Grade A, and why
argent-tv-interact 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 8d 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 — 71 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Argent TV (Apple TV + Android TV + Fire TV)
Critical
- A TV is focus-driven, not touch-driven. Drive every interaction with
describe+tv-remote+keyboard; never usegesture-*/ coordinate taps; they don't apply on any TV platform. - Always
describebefore navigating to find the live cursor and your target; never guess focus from a screenshot. The cursor is the focused element; on Vega the toolkit often leavesfocusedfalse and marks the highlighted item[selected], so treat[selected]as the cursor when nothing reports[focused]. - Pass the
udidfromlist-devices: an Apple TV simulator UDID or an Android TV / Vegaserial. Dispatch is automatic from the id; the same tools drive all three.
The navigation loop
describe: find the cursor and your target (returns the focused element + all focusable ones, not a tap tree).tv-remote: move focus toward the target. Prefer one call with a path ending inselect, e.g.{button:["down","right","select"]}; count rows/columns from the frames to build the path.describeagain to confirm. On a miss, repeat.
Tools
describe {udid}: focus view: the focused /[selected]element + focusable elements with labels and normalized frames. Call before and after navigating. Empty tree → see the per-platform notes.tv-remote {udid, button}: D-pad / remote.buttonis one key or a whole path (run in one call). Keys:up/down/left/right,select,back,menu,home,playPause, plus media keysrewind/fastForward/next/previous/volumeUp/volumeDown/mute. Single:{button:"down"}; repeat:{button:"down", repeat:3}; path:{button:["up","right","select"]}.keyboard {udid, text}: type into the focused field (focus it withtv-remotefirst). One call carriestextorkey, never both; to type and then press a key, send twokeyboardsteps in onerun-sequence. Namedkeypresses (e.g.{key:"enter"}) work on Vega; on Apple TV / Android TV move focus withtv-remoteinstead.launch-app/restart-app/reinstall-app {udid, bundleId}:bundleIdfrom the app manifest. Vegareinstall-apptakesappPath= a.vpkg.screenshot {udid, scale?}: Apple TV viaxcrun simctl io(downscaled); Android TV / Vega host-side viaadb/screencap.
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.
- 8d ago Changed 0097e258e12c
- 1mo ago Changed 0348bb321196
- 1mo ago First seen · 71 lines · 107 tokens per session scan A c510bd082a7d
argent-tv-interact is a skill published in the GitHub repository software-mansion/argent (3,069 stars, last pushed yesterday), licensed Apache-2.0. It adds 107 tokens to every session and 1,985 once invoked, about $0.0004 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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