Skill: Debug Quantized Kernel Accuracy

Test each boundary: - Quant-dequant roundtrip: quantize the fp32 input to INT8, then immediately dequantize back to fp32.

Context cost
3.8k tokensestimated from the bundle, loaded when it triggers
Bundle
2 files · 15.0 kBtext throughout, nothing executable
Licence
MITthe author’s own terms
Last change
—no release on file
Servers it uses
Noneruns standalone

What it does

Test each boundary: - Quant-dequant roundtrip: quantize the fp32 input to INT8, then immediately dequantize back to fp32. Compare to the original. Max error should be approximately 0.5 scale per element. If it exceeds this, the scale computation or the quantization formula is wrong. - INT8 kernel with exact INT8 inputs: if you can construct inputs where the exact INT8 representation is known (e.g., all values are multiples of the scale), run the INT8 kernel and verify that INT32 accumulation produces the expected result before dequantization.

What this skill changes about the agent has not been set out here as a list. Its SKILL.md, shown below, says it in the author’s own words.

data science

The skill itself

A skill is instructions the model reads, so you can see what you are getting: below is the start of its SKILL.md. The front matter loads with every session, the body loads when the skill triggers, and the whole file comes with the install.

SKILL.md14.4 kB · preview
1# Skill: Debug Quantized Kernel Accuracy
2
3## Purpose
4Guide the agent through a systematic process for diagnosing and isolating accuracy degradation in a quantized (INT8, FP8, or low-bit) kernel, from measuring the error to identifying the specific computational step responsible.
5
6## Use this when
7- A quantized kernel produces outputs that differ from the fp32 reference by more than the expected quantization error bound.
8- A model using quantized kernels shows accuracy degradation that exceeds what is expected for the chosen quantization scheme.
9- A quantization refactor introduced a regression and the specific step that broke is not obvious.
10- Debugging a quantized kernel that works correctly on some input shapes or batch sizes but fails on others.
11
12## Do not use this when
13- The error is within the expected quantization error bound (approximately 0.5 * scale per element for well-calibrated INT8) and the downstream task accuracy loss is acceptable.
14- The issue is clearly a non-accuracy bug (segfault, wrong shape, miscompilation) — fix the structural bug first.
15- The degradation is due to model-level quantization sensitivity (certain layers or operators being inherently sensitive to quantization), which requires a quantization-aware training or mixed-precision approach rather than kernel debugging.
16
17## Inputs the agent should gather first
18- The exact mathematical specification of what the quantized kernel is supposed to compute, written in terms of the original unquantized operation.
19- The quantization scheme: per-tensor, per-channel, or per-token; symmetric or asymmetric; INT8, INT4, or FP8; signed or unsigned range.
20- The scale computation method: offline calibration, per-batch dynamic quantization, or per-token dynamic quantization.
This is a preview of SKILL.md. Download or install the skill to get the whole thing.
In the file
SKILL.md2,201 words
Files2
LicenceMIT
Why you can read it

Nothing in a skill executes. The client loads the text and the model follows it, so a skill can be audited the way a runbook is — by reading it.

What it costs in context

Skills are not billed by the call. They are paid for in context: every token the instructions occupy is a token your code, your diff and your conversation cannot use. Here is what this one takes and when it takes it.

≈20
always loaded
The name and description, so the model knows the skill exists and when to reach for it.
3,730
on trigger
The instruction body and 1 supporting file, read only when the skill fires.
1.9%
of a 200k window
Ten skills this size would take about 19% of the window before you open a file.
050k100k150k200k context window

3.8k tokens, estimated from the bundle at four bytes to the token, held for the rest of the session once it triggers. Middling. Fine to keep on in a project where you use it weekly, worth unloading in one where you never do.

Servers bill, skills cost

A server charges by the month. A skill charges once per session, in context, and then keeps charging it for as long as the session lives.

Before and after

The same question, put to the same model twice: once as it comes, and once with these instructions loaded.

No worked example has been published for this skill yet.

The procedure it runs

The procedure has not been set out here step by step. It is in the body of its SKILL.md: the start of it is shown above, and the whole file comes with the install.

Prose, not code

These steps are written for a model to follow, not executed by a runtime. It can still be told to skip one, and it will say so when it does.

Servers it uses

None. This skill calls no MCP servers at all.

Everything it needs is in the instructions, so it works in a project with nothing connected — the model reads the file and changes how it works with what it can already reach.

Its SKILL.md does not declare which tools it asks the agent for, so what it touches is whatever the instructions themselves ask for — read them before you install it.

What it asks for
Writes filesnot declared
Network accessnot declared

This SKILL.md carries no allowed-tools line, so neither is declared. A skill grants no permissions of its own — it can only ask for tools your client already has.

What it will not do

Every skill is narrow, and the useful ones say where they stop. These are the jobs this one is the wrong tool for.

What this skill is not for has not been published here. Nothing is implied by that: it is a section the author has not filled in.

What is in the bundle

2 files, 15.0 kB on disk. A bundle is text throughout: the instructions the model reads, plus the templates it fills in.

  • SKILL.md14.4 kB
  • skill.json0.6 kB
What is not in it

No dependencies and nothing executable: a skill is text the agent reads, so the bundle is 2 files you can review in full before installing. The MIT licence covers the templates and examples as well as the instructions.

Install

Installing copies the bundle into your project. Nothing runs at install time — the files sit on disk until the model reads them.

# Skill: Debug Quantized Kernel Accuracy · 3.8k tokens when loaded · macOS, Linux mkdir -p .claude/skills && t=$(mktemp -d) && curl -fsSL -o "$t/bundle.tar.gz" https://mcprush.com/api/skills/skill-debug-quantized-kernel-accuracy/bundle.tar.gz && tar -xzf "$t/bundle.tar.gz" -C "$t" && [ -d "$t/skill-debug-quantized-kernel-accuracy" ] && { [ ! -e .claude/skills/skill-debug-quantized-kernel-accuracy ] || { mv .claude/skills/skill-debug-quantized-kernel-accuracy "$t/.previous" && echo "Previous copy moved to $t/.previous"; }; } && mv "$t/skill-debug-quantized-kernel-accuracy" .claude/skills/ && echo "Installed .claude/skills/skill-debug-quantized-kernel-accuracy"
On Windows, or any OS with Node 20+
npx mcprush@latest skill add tensormux/skill-debug-quantized-kernel-accuracy

Windows PowerShell says 'running scripts is disabled on this system'? Run Set-ExecutionPolicy -Scope CurrentUser RemoteSigned once, or type npx.cmd instead of npx (codex.cmd, gemini.cmd, grok.cmd likewise). PowerShell 7 needs neither. Writes to .claude/skills/skill-debug-quantized-kernel-accuracy/ in the current project. For every project instead, run mkdir -p ~/.claude/skills && t=$(mktemp -d) && curl -fsSL -o "$t/bundle.tar.gz" https://mcprush.com/api/skills/skill-debug-quantized-kernel-accuracy/bundle.tar.gz && tar -xzf "$t/bundle.tar.gz" -C "$t" && [ -d "$t/skill-debug-quantized-kernel-accuracy" ] && { [ ! -e ~/.claude/skills/skill-debug-quantized-kernel-accuracy ] || { mv ~/.claude/skills/skill-debug-quantized-kernel-accuracy "$t/.previous" && echo "Previous copy moved to $t/.previous"; }; } && mv "$t/skill-debug-quantized-kernel-accuracy" ~/.claude/skills/ && echo "Installed ~/.claude/skills/skill-debug-quantized-kernel-accuracy" (macOS, Linux) — or add --global to the npx line. The curl line replaces .claude/skills/skill-debug-quantized-kernel-accuracy/ only after the download succeeds, so files left there by another skill of the same name, or by an older release, do not mix in. A copy already there is moved aside, not deleted, and the line prints where. Its SKILL.md had no name and description header; the download adds one, so clients load it.

Which clients pick it up on their own

A skill is a folder of text. Most of these read a skills folder and pick it up without being told; the rest take the same folder another way — an upload in the app, a parameter on an API call — and then choose it by its description just the same.

Claude Code.claude/skills/
Claude DesktopCustomize › Skills
ChatGPTSkills › Create › Upload
Cursor.cursor/skills/
VS Code.github/skills/
Codex CLI.agents/skills/
Gemini CLI.gemini/skills/
Grok Build.grok/skills/
Microsoft CopilotSkills › Upload a skill
PerplexityComputer › Skills › Upload a skill
DeepSeek Harness.dsh/skills/
Zed.agents/skills/
Devin Desktop.devin/skills/
Windsurf (legacy).windsurf/skills/
OpenAI Agents SDKInstructions or sandbox Skills
HTTP / APIa request parameter
This release
Versionnot versioned
Publishedno release date on file
PriceFree
Referencetensormux/skill-debug-quantized-kernel-accuracy

Versions

Its author publishes no version number, so there is nothing here to pin to: what you install is the folder as it stands today. Instructions change more often than APIs do — a skill can be rewritten entirely without anything it depends on moving.

Pinning

Nothing to pin to: this skill carries no version number of its own. What you install is what the folder holds on the day you install it.

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Who wrote it

TE
tensormux

Publishes on mcprush.

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