operator-patching

operator-patching is a skill for Claude Code, Codex from qualcomm/qai-appbuilder. It costs 0 tokens per session (1,583 once invoked), scanned A, original, no licence file.

A model-conversion repair tool for replacing operations that Qualcomm's QNN runtime does not support with combinations of simpler supported operations. The changes are made in memory, so the original model files are not edited.

In plain words
What is it for?
Use it to replace unsupported model operations with equivalents built from operations such as MatMul, Reshape, Transpose, Concat, and Where.
Why use it?
It addresses conversion or execution failures caused by operations such as Einsum, GridSample, or ScatterND. This can let a model continue through the processing stage where the unsupported operation failed.

Skill for Claude CodeCodex

Which agent this was written for is unclear — body not stored (licence); the path alone says nothing.

Good fit Use it to replace unsupported model operations with equivalents built from operations such as MatMul, Reshape, Transpose, Concat, and Where.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/qualcomm/qai-appbuilder/operator-patching
Install

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.

Any agent
npx skills add qualcomm/qai-appbuilder --skill operator-patching
Clone the repo
git clone --depth 1 https://github.com/qualcomm/qai-appbuilder

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for operator-patching

README.md
[![agentmods](https://agentmods.dev/badge/skills/qualcomm/qai-appbuilder/operator-patching/github.svg)](https://agentmods.dev/skills/qualcomm/qai-appbuilder/operator-patching)
Your own site
<a href="https://agentmods.dev/skills/qualcomm/qai-appbuilder/operator-patching"><img src="https://agentmods.dev/badge/skills/qualcomm/qai-appbuilder/operator-patching/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.

agentmods 80×15 button for operator-patching

Your own site · 80×15
<a href="https://agentmods.dev/skills/qualcomm/qai-appbuilder/operator-patching"><img src="https://agentmods.dev/badge/skills/qualcomm/qai-appbuilder/operator-patching.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 0 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,583 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin unknown No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00000 $0.01583
Opus 5.5 $0.00000 $0.00633
Sonnet 5.5 $0.00000 $0.00317
Haiku 4.5 $0.00000 $0.00158

Measured 1mo ago against content hash a84196ac6c3c, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

operator-patching 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 1mo 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.

tools/qaiappbuilder/factory/chat_features/model-builder/troubleshooting/operator-patching/SKILL.md · 126 lines

The source is not reproduced here

A licence we could not identify

The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.

Read it on GitHub

Changes

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.

  1. 1mo ago First seen · 126 lines · 0 tokens per session scan A a84196ac6c3c

Subscribe to this mod's changes

operator-patching is a skill published in the GitHub repository qualcomm/qai-appbuilder (253 stars, last pushed today), with no licence file. It costs nothing until one of its globs matches a file; then it loads 1,583 tokens. 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.

Related

Other skills, from other repositories

ml-kit-genai-prompt-api

Analyzes Android codebases to implement ML Kit GenAI Prompt API. Use this skill to send natural language requests on-device to Gemini Nano, use structured output with Prompt API, implement prefix caching, optimize the current prompt, or apply best practices.".

android/skills · 57 tokens

model-authoring

Empirical rules for authoring PyTorch models for on-device execution on Apple platforms, covering energy-efficient inference, scalable compute, and correctness testing. Use this skill whenever the user is writing, debugging, or reviewing PyTorch model code intended for on-device execution — even if they don't…

apple/coreai-models · 95 tokens

working-with-coreai

Use this skill whenever the user mentions coreai-torch, TorchConverter, coreai-build, AIModel, AIProgram, .aimodel, or wants to export/compile/run a PyTorch model on Apple silicon (iPhone, iPad, Mac). Also triggers for "deploy on device", "optimize for on-device performance", onboarding new models to Core AI, or…

apple/coreai-models · 91 tokens

litert-runtime

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 helpers, TensorBuffer reuse, CPU memory optimization, Bitmap…

android/skills · 150 tokens

foundation-models-on-device

Apple FoundationModels framework for on-device LLM — text generation, guided generation with @Generable, tool calling, and snapshot streaming in iOS 26+.

hashgraph-online/awesome-codex-plugins · 38 tokens

litert-lm

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 maintenance-only mode. Use this skill to build a new chat, summarization or…

google-ai-edge/litert-samples · 142 tokens