google-ai-edge

Show an interactive map view for the given location.

A daily learning companion that teaches users a new concept, generates a beautiful image card, and schedules recurring daily learning notifications.

Schedule a notification for a specific date or repeating daily.

Read OS calendar events for a specific date.

Create a calendar event.

Calculate the hash of a given text.

Act as a dungeon master for a text-based adventure set in a world where everyone is a sentient kitchen appliance. Trigger when user says "start kitchen adventure".

A simple mood tracking skill that stores your daily mood and comments. Use this when the user wants to log their mood, track how they feel, or see their mood history.

Compiles LiteRT (.tflite) models ahead-of-time for Google Tensor TPUs (Tensor G5 on Pixel 10, Tensor G6 on Pixel 11) locally on Linux x86_64 or remotely via Google Colab (--colab on macOS, Windows, or ARM hosts), benchmarks them on a USB-attached Pixel with…

Convert a Hugging Face LLM or vision-language model checkpoint into a .litertlm bundle that runs on the LiteRT-LM runtime with verified quality - classify the architecture against known runtime walls, pick the recipe family (dense, reasoning, hybrid SSM,…

Prove a converted or quantized LiteRT model on the actual device via the CompiledModel API - confirm GPU residency, compare device output against the source model, and diagnose device-only failures such as silent CPU fallback, whole-graph compile ceilings,…

Creates an Android app that runs an open text LLM on the device, on the CPU or the GPU, with the LiteRT-LM Kotlin API. LiteRT-LM is the recommended replacement for the MediaPipe LLM Inference API (com.google.mediapipe:tasks-genai), which is in…

Say which models, backends and accelerators this app is running and how much memory the device has.

Measure a LiteRT .tflite model with `benchmark_model` on Developer Device Platform (DDP) lab phones through `litert benchmark --ddp`, or on the Mac you run it from, and turn the session into rows of the performance leaderboard under benchmark/leaderboard -…

Configures and implements LiteRT (.tflite) on-device inference on Android in Kotlin using the CompiledModel API on CPU and GPU. Use this skill for any Android LiteRT or .tflite task, including Gradle setup, image classification or vision/audio/embedding…

Convert a PyTorch or Hugging Face model into a LiteRT model that runs fully on the GPU via the CompiledModel API with verified-correct output, and lay it out as a model recipe. Use when converting a new model, or when a converted model is rejected by the GPU,…

Converts PyTorch models (e.g. ResNet, timm, HuggingFace transformers) directly to LiteRT (.tflite) flatbuffer format. Use when converting PyTorch models to TFLite, setting up export environments, or troubleshooting torch-to-litert conversion bugs. Don't use…

Onboards and exports new or custom Hugging Face (or Diffusers/PyTorch) model architectures in `litert_torch.generative.export_hf` using pluggable `ModelExtension` classes (built-in under `model_ext/<arch>/` or out-of-tree via `--extension_modules`). Use when…

Validates equivalence between LiteRT models (litert_lm) and PyTorch models (transformers). Use when you need to verify that an exported LiteRT model produces the same outputs as the original Hugging Face model. Supports multi-turn conversations and custom…

Assists the user to calibrate, merge, and statically quantize litert LLM models (such as Gemma 3) in standard open-source (OSS) environments. Use when the user wants to run LLM calibration, merge task JSON results, align KV cache parameters across models,…

Build and measure Gemma 4 through LiteRT TensorRT-RTX, run its bounded CI suite, and inspect performance and cache evidence.

Systematically explore quantization configurations for a TFLite model using the LiteRT Quantizer API, finding the optimal recipe that balances file size and accuracy. Use this skill whenever the user wants to quantize a model, optimize a recipe, explore…

Guides the agent to orchestrate and implement a standalone LiteRT-LM Android demo app with backend selection and multi-modality support using Bazel 9. Use when asked to create a demo app for LiteRT-LM on Android. Don't use for general Android app development…

Generates code for a new GPU Kernel in ml_drift using UCL syntax. Use when the user wants to add or implement a new operation/kernel in ml_drift (e.g., SquaredDifference, Maximum). Creates the .h, .cc files, generates test files (test.cc, test_util.h,…

Systematically explore quantization configurations for a TFLite model using the AI Edge Quantizer API, finding the optimal recipe that balances file size and accuracy. Use this skill whenever the user wants to quantize a model, optimize a recipe, explore…

LiteRT CLI tool to download, convert, quantize, run, benchmark, and visualize LiteRT models.