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-compiled-model-migrationgit 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-compiled-model-migration)<a href="https://agentmods.dev/skills/google-ai-edge/litert-samples/litert-compiled-model-migration"><img src="https://agentmods.dev/badge/skills/google-ai-edge/litert-samples/litert-compiled-model-migration.svg" alt="Measured on agentmods" 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 | $0.00076 | $0.03019 |
| Opus 5 | $0.00038 | $0.01510 |
| Sonnet 5 | $0.00015 | $0.00604 |
| Haiku 4.5 | $0.00008 | $0.00302 |
Grade A, and why
litert-compiled-model-migration 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 — 195 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: LiteRT Compiled Model Migration SKILL
Description
This skill guides an AI agent to rapidly migrate an Android application from legacy TensorFlow Lite (TFLite) to the modern LiteRT CompiledModel API v2.1.6 in Open Source GitHub repositories. It prioritizes a high-speed, 1st-pass "Like for Like" baseline migration with automated self-testing, and encourages advanced performance upgrades including True Asynchronous Execution (runAsync), Zero-Copy I/O Buffer Management, and NPU JIT compilation.
0. Automatic Discovery & Upfront Planning
Before writing code, the agent MUST inspect the project workspace and present the upfront planning interview to align migration parameters:
A. Automatic Workspace & Toolchain Discovery
The agent must automatically inspect the repository structure:
- Ecosystem & Build Engine:
- Gradle Build System: Detected by
build.gradle,build.gradle.kts, orsettings.gradle. -> Enable Gradle & GitHub PR Workflow.
- Gradle Build System: Detected by
- Language & JNI Toolchain:
- Native C++ / NDK: Detected if
CMakeLists.txt,Android.mk, or*.cppfiles exist. -> Enable C++ / JNI Migration Rules. - Pure Kotlin / Java: Default to JVM / Android SDK Migration Rules.
- Native C++ / NDK: Detected if
[!TIP] Speed Optimization (Subagent Routing): When orchestrating subagents, the agent MUST default to
DeepCoderLite(orDeepInvestigatorLite) to guarantee a 2–5 minute migration turnaround. Do NOT invoke heavy multi-layerDeepCodersynthesis unless the codebase features complex custom C++ NDK/CMake build systems.
B. Upfront User Interview (Questions Asked Prior to Migration)
The agent must present the following review options to the user:
Before initiating the LiteRT Compiled Model Migration, please confirm your project preferences:
1. Model Workload & Domain:
What type of data does this application process?
- [A] Vision (Images / Video / Camera Feeds) -> Enables Zero-Copy AHardwareBuffer / direct ByteBuffer recipes.
- [B] Audio (Speech / Sound Classification) -> Enables streaming FloatArray or ByteBuffer recipes.
- [C] Text / NLP / GenAI -> Enables tokenized tensor buffer recipes.
2. LiteRT Runtime Target SDK:
Which SDK distribution target should the project use?
- [A] Standalone / Bundled LiteRT V2 (com.google.ai.edge.litert:litert) [Default]
-> Bundles LiteRT runtime inside the APK for offline self-contained operation.
- [B] LiteRT-in-GMSCore (com.google.android.gms:play-services-litert) [Experimental / Future Release]
-> Dynamically requests runtime from Google Play Services, saving ~5 MB APK binary bloat.
3. Hardware Acceleration & Conditional INT8 Quantization:
Do you want to enable NPU hardware acceleration via JIT on-device compilation?
- [A] Yes (Recommended - replaces deprecated NNAPI) [Default]
* If the app uses a Float32 model: Would you like to generate an INT8 integer-quantized model via AI Edge Quantizer for peak NPU speed, or run the original Float32 model?
-> Option A.1: Convert to INT8 (Generates model_int8.tflite for NPU matrix engines) [Default]
-> Option A.2: Keep Float32 (Runs baseline float model directly on NPU)
- [B] No (GPU and CPU acceleration only)
4. Encouraged Performance Upgrades:
Should the agent upgrade the calling code to use LiteRT's advanced features?
- [A] Yes (Enable True Async Execution runAsync & Zero-Copy I/O Buffers) [Default]
- [B] No (Keep strict 1-to-1 synchronous baseline execution)
5. Automated Pull Request Provisioning:
Should the agent automatically stage, commit, and create a GitHub PR when self-testing passes?
- [A] Yes [Default] (Attaches verification test logs and before/after summary diff)
- [B] No (Keep changes local in current working branch)
What ships with it
2 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.
- 8d ago First seen · 195 lines · 76 tokens per session scan A a62d67083a9c
litert-compiled-model-migration is a skill published in the GitHub repository google-ai-edge/litert-samples (423 stars, last pushed 4d ago), licensed Apache-2.0. It adds 76 tokens to every session and 3,019 once invoked, about $0.0004 per session on Opus 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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