auto_quant SKILL.md skillA

auto_quant from google-ai-edge/litert is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.

Install it, pinned
mdr add google-ai-edge/litert/auto_quant@git:20260930.7278fe8

Pins git:20260930.7278fe8 in mdr.lock. Pin the fingerprint instead.

Audit grade
A, 17 of 17 checks passed
Kind
SKILL.md skill
Repository
google-ai-edge/litert
Path
litert_quantizer/litert_quantizer/experimental/skills/auto_quant/SKILL.md
Versions recorded
1
Latest version
git:20260930.7278fe8, committed Sep 30, 2026
Size
22,432 bytes
Repository stars
3,513
License
Apache-2.0

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 quantization tradeoffs, minimize size bounds, or perform selective quantization using LiteRT Quantizer framework. Applies to any model scale and modality: CNNs, segmentation nets, classifiers, embedding models, audio models, and LLMs.

Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.

What the file says

# Agent Contribution Guide: LiteRT Quantizer Recipe Exploration

Welcome, AI Agent! This skill governs how you autonomously explore and generate
the optimal selective quantization recipe for any given LiteRT FlatBuffer
(`.tflite`) model using the LiteRT Quantizer framework. Strict adherence
to these protocols ensures code is high-quality, memory safe, and meets
verification protocols.

Your goal is to build an iterative search loop in Python that actively
interrogates the baseline model for numerical degradation, updates the recipe on
the fly to protect highly sensitive tensors, and produces a bounding-box of
Pareto-optimal models for the user.

## Skill Index

Leverage these project-specific skill guides to navigate and execute tasks
effectively.

**Core guides (read ALL of these before proceeding):**

| File | Contents |
| --- | --- |
| [`README.md`](../../README.md) | Overview of the LiteRT Quantizer framework and the repo's structure |
| [`README.md`](../../../../README.md#operator-coverage) | What configurations are supported for each operator |
…

Read the whole file at its exact version.

How to install

The exact contents, by fingerprint
mdr add google-ai-edge/litert/auto_quant@sha256:bdaf690894880fcf

The line at the top of this page pins the label git:20260930.7278fe8, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.

Badge

mdr badge

[![mdr](https://markdownregistry.com/badge/art_dz5xhpdohvwq6exl.svg)](https://markdownregistry.com/a/art_dz5xhpdohvwq6exl)

2 badge image fetches in 30 days. What the badge shows.

Versions

The registry has recorded 1 version of this file, committed on Sep 30, 2026.

versioncommittedcommitsizeaudit
git:20260930.7278fe8 latest2026-09-30 7278fe8 22,432 BA view

Audit of the latest version

A  17 of 17 checks passed. Fixed automatic checks with no AI involved, so the same checks give the same answer every run.
  • pass: Frontmatter block present
  • pass: Frontmatter declares a name
  • pass: Frontmatter declares a description
  • pass: Size between 200 bytes and 200 KB (22432 bytes)
  • pass: No zero-width or bidi control characters
  • pass: No instruction hidden inside an HTML comment
  • pass: No link to an exfiltration or paste host
  • pass: No credential-shaped string
  • pass: No instruction to send local credentials anywhere
  • pass: No text hidden with inline styles
  • pass: No prompt-injection phrasing
  • pass: No curl or wget piped into a shell
  • pass: No recursive delete of root, home or parent
  • pass: No instruction to read or print local credentials
  • pass: No base64 blob over 200 characters
  • pass: No link to a raw IP address
  • pass: No script tag

Source

GitHub

google-ai-edge/litert · 3,513 stars · license Apache-2.0 · pushed 2026-10-21 · branch main

API

GET https://markdownregistry.com/api/v1/artifacts/art_dz5xhpdohvwq6exl
GET https://markdownregistry.com/api/v1/resolve?ref=google-ai-edge/litert/auto_quant
GET https://markdownregistry.com/api/v1/blob/bdaf690894880fcf5cdcbc21fb7f8ca4601b8a3fbcb7ce33967a1e4028df4107

Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.

Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.

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