agentmods.dev

npu-adaptation

npu-adaptation is a skill for Claude Code, Codex from wamcs/skills. It costs 119 tokens per session (3,524 once invoked), scanned A, original, no licence file.

A workflow for adapting machine-learning models to run on NPUs, specialised chips for neural-network workloads. It covers the environment, model weights, inference code, smoke tests, accuracy checks, and out-of-memory diagnosis.

In plain words
What is it for?
Use it to bring a new model to an NPU, obtain and manage its weights, write an inference script, compare adapted results with reference values, or investigate inference memory errors.
Why use it?
It gives a defined path for finding out whether a model runs correctly on NPU hardware and whether its results still match the original version. It also helps distinguish setup problems from memory limits.

Skill for Claude CodeCodex

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

not rated 5repo +1 1mo ago A scan 119 tokens no licence

Good fit Use it to bring a new model to an NPU, obtain and manage its weights, write an inference script, compare adapted results with reference values, or investigate inference memory errors.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/wamcs/skills/00_adaptation
View source ↗ wamcs/skills
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 wamcs/skills --skill 00_adaptation
Clone the repo
git clone --depth 1 https://github.com/wamcs/skills

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 npu-adaptation

README.md
[![agentmods](https://agentmods.dev/badge/skills/wamcs/skills/00_adaptation/github.svg)](https://agentmods.dev/skills/wamcs/skills/00_adaptation)
Your own site
<a href="https://agentmods.dev/skills/wamcs/skills/00_adaptation"><img src="https://agentmods.dev/badge/skills/wamcs/skills/00_adaptation/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 npu-adaptation

Your own site · 80×15
<a href="https://agentmods.dev/skills/wamcs/skills/00_adaptation"><img src="https://agentmods.dev/badge/skills/wamcs/skills/00_adaptation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 119 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,524 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 $0.00119 $0.03524
Opus 5.5 $0.00048 $0.01410
Sonnet 5.5 $0.00024 $0.00705
Haiku 4.5 $0.00012 $0.00352

Measured 1mo ago against content hash 27333a96ab1d, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

npu-adaptation 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.

The scan reads SKILL.md. This mod also ships 2 executable files (scripts/compare_baseline.py, scripts/weight_manager.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

model_opt/00_adaptation/SKILL.md · 154 lines

The source is not reproduced here

No licence file

A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.

Read it on GitHub

Files

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.

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 · 154 lines · 119 tokens per session scan A 27333a96ab1d

Subscribe to this mod's changes

npu-adaptation is a skill published in the GitHub repository wamcs/skills (5 stars, last pushed 1mo ago), with no licence file. It adds 119 tokens to every session and 3,524 once invoked, about $0.0005 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 2026-09-04.

Data and AIHardware and IoT
Related

Other skills, from other repositories

spark-environment-setup

Set up a working ML training/inference environment on NVIDIA DGX Spark (GB10, aarch64, CUDA 13). Use when installing PyTorch/Unsloth/TRL/vLLM on DGX Spark, hitting libcudart or wheel-ABI errors on aarch64, or choosing between NGC containers and bare pip installs.

wshobson/agents · 76 tokens

spark-memory-thermal-ops

Manage unified memory and thermals during long-running ML jobs on NVIDIA DGX Spark. Use when planning memory headroom for a training run on GB10, when a job OOMs on unified memory, or when monitoring temperature and power during multi-hour training.

wshobson/agents · 59 tokens

spark-training-gotchas

Preflight and diagnose the ten known failure modes for ML training on NVIDIA DGX Spark. Use when a training run on DGX Spark fails to start, OOMs below the 128GB limit, slows down mid-run, or before any multi-hour training job on GB10.

wshobson/agents · 63 tokens

llama-cpp

Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.

davila7/claude-code-templates · 76 tokens

amc-run-rtsp-calibration

Calibrate a new dataset from live RTSP camera streams via the AutoMagicCalib REST API. Use when the user provides RTSP URLs or asks to calibrate live cameras; VIOS records clips, AMC ingests them, then runs calibration.

NVIDIA/skills · 59 tokens

deepstream-generate-pipeline

Build DeepStream GStreamer pipelines interactively. Use when the user asks about pipelines for video/image inference, detection, tracking, or streaming — including natural phrases like 'pipeline to infer on image', 'run inference on video', 'detect objects in stream', 'save inference output', 'deepstream pipeline'…

NVIDIA/skills · 108 tokens