Meshtastic-Android is an Android and Compose Desktop app for communicating through open-source mesh radios, which relay messages between nearby devices instead of relying on a conventional network. It is used with Meshtastic devices for messaging, node and channel discovery, location features, telemetry, and device management.
Borrowing it
Nothing to install: this file belongs to meshtastic/Meshtastic-Android. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/meshtastic/Meshtastic-Android/main/.skills/code-review/SKILL.mdgit clone --depth 1 https://github.com/meshtastic/Meshtastic-AndroidWrote 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/meshtastic/meshtastic-android/code-review)<a href="https://agentmods.dev/skills/meshtastic/meshtastic-android/code-review"><img src="https://agentmods.dev/badge/skills/meshtastic/meshtastic-android/code-review/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/meshtastic/meshtastic-android/code-review"><img src="https://agentmods.dev/badge/skills/meshtastic/meshtastic-android/code-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 System Prompt Leakage · line 123 Skill contains instructions that could directly expose system prompts, internal rules, or hidden instructions to users or external parties.Fix: Remove any instructions that reveal, print, or output system prompts or internal rules. System instructions should never be exposed to end users.
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 | $0.00072 | $0.04591 |
| Opus 5.5 | $0.00029 | $0.01836 |
| Sonnet 5.5 | $0.00014 | $0.00918 |
| Haiku 4.5 | $0.00007 | $0.00459 |
Grade A, and why
code-review 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 7d 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.
The source is not reproduced here
Licensed GPL-3.0
The repository is licensed GPL-3.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.
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.
- 7d ago Changed 59f5c7789fac
- 29d ago Changed 7bd04532c730
- 1mo ago Changed · +5 lines · +72 tokens per session 42ba385bebad
- 1mo ago First seen · 147 lines · 0 tokens per session scan A 82309e39c3e7
code-review is a skill published in the GitHub repository meshtastic/Meshtastic-Android (1,894 stars, last pushed today), licensed GPL-3.0. It adds 72 tokens to every session and 4,591 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 2026-09-04.
Other skills, from other repositories
android-compose-pro
Deep-reviews Jetpack Compose code for recomposition stability, side-effect correctness, lazy-list performance, and state modeling. Use when reading, writing, or reviewing non-trivial Compose UI — especially when chasing jank, runaway recomposition, or effect bugs.
app-store-compliance
Run an enterprise pre submission compliance audit on a mobile app project before uploading to the Apple App Store or Google Play. Use when the user is about to submit, ship, or release an iOS or Android app, when an app was rejected and needs a fix plan, when reviewing App Store Review Guidelines or Google Play policy…
cmp-audit
Adversarial audit of one subsystem of a Kotlin/Compose Multiplatform app against its spec AND against platform semantics — the class of defect desktop-tier tests cannot see (alarms, notifications, PendingIntents, reboot, process death, DST). Use this when the user says "audit the notifications", "double check X for…
compose-validate
Scan a Kotlin / Compose / Android codebase for anti-patterns: findViewById, GlobalScope, LiveData, Material 2 imports, lowercase composables, LazyColumn without key, kapt, kotlinCompilerExtensionVersion, force-unwrap !!, Log.d, string nav routes, hardcoded dependency versions.
android-compose-review
Review and improve an existing Android Jetpack Compose codebase using modern Compose patterns, architecture guardrails, migration guidance, and incremental fixes. Use when auditing, modernizing, or cleaning up a project that already exists.
solopi-ai
A command-line framework for testing Android apps and devices with SoloPi, including on-device or cloud AI decision models. It manages devices, test cases, recorded interactions, replays, performance history, and evidence.