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 google-ai-edge/litert-samples --skill litert-runtimegit clone --depth 1 https://github.com/google-ai-edge/litert-samplesWrote 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/google-ai-edge/litert-samples/litert-runtime)<a href="https://agentmods.dev/skills/google-ai-edge/litert-samples/litert-runtime"><img src="https://agentmods.dev/badge/skills/google-ai-edge/litert-samples/litert-runtime/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/google-ai-edge/litert-samples/litert-runtime"><img src="https://agentmods.dev/badge/skills/google-ai-edge/litert-samples/litert-runtime.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00088 | $0.02421 |
| Opus 5.5 | $0.00035 | $0.00968 |
| Sonnet 5.5 | $0.00018 | $0.00484 |
| Haiku 4.5 | $0.00009 | $0.00242 |
Grade A, and why
litert-runtime 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.
How it starts
The opening of the file, as written. The whole thing — 132 lines — stays where its author put it; the contents beside it link to each section on GitHub.
This skill provides step-by-step guidance for building an Android app that runs a .tflite model with the LiteRT CompiledModel API (com.google.ai.edge.litert:litert 2.x; overview: https://ai.google.dev/edge/litert/android, sources: https://github.com/google-ai-edge/litert/blob/main/README.md) on the CPU or the GPU. The Interpreter API is not covered. For language models, use the litert-lm skill: https://github.com/google-ai-edge/litert-samples/blob/main/skills/litert-lm/SKILL.md (LiteRT-LM: https://github.com/google-ai-edge/LiteRT-LM/blob/main/README.md).
Prerequisites
- A Kotlin Android project (Android Studio's Empty Activity template is enough). The LiteRT 2.3.0 AARs declare
minSdk24. - The dependencies in the app-level
build.gradle.kts, from Google Maven:implementation("com.google.ai.edge.litert:litert:2.3.0")andimplementation("com.google.ai.edge.litert:litert-gpu:2.3.0"). From 2.3.0 the GPU accelerator is the second artifact: withlitertalone the app builds, andCompiledModel.createwithAccelerator.GPUthrowsLiteRtExceptionbecause the APK has no GPU accelerator library (logcat:GPU accelerator could not be loaded and registered). No manifest entry is needed:litert-api, whichlitertdepends on, declares the OpenCL driver libraries (uses-native-library) in its own manifest. - A
.tflitemodel and its input requirements (input size, mean/std, channel order). Models with a LiteRT recipe: https://github.com/google-ai-edge/litert-samples/blob/main/models/README.md
Detailed steps
1. Set up the project and place the model
In the app-level build.gradle.kts, set minSdk = 24 in defaultConfig. Put the model at app/src/main/assets/model.tflite (AGP stores .tflite files uncompressed by default). A model too large for the APK is delivered by Play for On-device AI (beta) as an AI pack, or downloaded by the app into context.filesDir: https://developer.android.com/google/play/on-device-ai (its bundletool --local-testing installs the packs without the store). An install-time AI pack is read through the AssetManager, as that page shows. A fast-follow or on-demand pack and a downloaded file are loaded from their path with CompiledModel.create(filePath, CompiledModel.Options(accelerator), environment) in place of the asset call in step 2. The pack's directory comes from the AI Delivery library (com.google.android.play:ai-delivery): AiPackManagerFactory.getInstance(context).getPackLocation(name)?.assetsPath(), null until the pack has downloaded.
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.
- 7d ago Changed c172a36ce0b7
- 9d ago Changed · -2 lines d1582f7b9180
- 15d ago First seen · 134 lines · 88 tokens per session scan A cde47091a5ed
litert-runtime is a skill published in the GitHub repository google-ai-edge/litert-samples (464 stars, last pushed yesterday), licensed Apache-2.0. It adds 88 tokens to every session and 2,421 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 2026-10-02.
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