What is inferencex-optimize?

A guided workflow for benchmarking and profiling an InferenceX model or configuration key. Benchmarking measures how a configuration performs, while profiling examines where time or resources are used.

Starting the setup from a model or configuration name, choosing benchmark options, checking whether GEAK is available, and running the profiling workflow.

It helps compare configuration choices using a consistent setup instead of relying on guesswork.

inferencex-optimize — Skill for Claude Code and Codex by AMD-AIM

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 AMD-AIM/inference-skill --skill inferencex-optimize

Clone the repo

git clone --depth 1 https://github.com/AMD-AIM/inference-skill

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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/amd-aim/inference-skill/inferencex-optimize/github.svg)](https://agentmods.dev/skills/amd-aim/inference-skill/inferencex-optimize)

Your own site

<a href="https://agentmods.dev/skills/amd-aim/inference-skill/inferencex-optimize"><img src="https://agentmods.dev/badge/skills/amd-aim/inference-skill/inferencex-optimize/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/amd-aim/inference-skill/inferencex-optimize"><img src="https://agentmods.dev/badge/skills/amd-aim/inference-skill/inferencex-optimize.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 42 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,380 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 original 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.

Model	Per session	Once invoked
Fable 5.1	$0.00042	$0.01380
Opus 5.5	$0.00017	$0.00552
Sonnet 5.5	$0.00008	$0.00276
Haiku 4.5	$0.00004	$0.00138

Measured 2mo ago against content hash d4cc05c784dd, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

inferencex-optimize 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 2mo ago.

The scan reads SKILL.md. This mod also ships 10 executable files (scripts/analyze_fusion_inferencex.py, scripts/classify_kernel.py, scripts/generate_problems_inferencex.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.

skills/inferencex-optimize/SKILL.md · 107 lines

How it starts

The opening of the file, as written. The whole thing — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.

InferenceX Optimize

Default user experience

Treat a bare model/config key as enough to start. Do not require the user to spell out a full command.
If the user says use inferencex-optimize skill for <model-or-config-key>, start guided setup immediately.
Do not dump raw parameter names in the first reply; translate to a short setup conversation.
Prefer the native question tool for multiple-choice prompts when the runtime provides it.
If the runtime does not provide a question tool, ask concise numbered choices in normal chat.
Ask questions in grouped batches, not as a drip-feed of one question at a time.
Keep explicit progress updates so the user always knows current stage and next step.
Inform user about GEAK availability during setup when running in optimize or optimize-only mode.

First-turn latency rule

Do not read any other file before the first visible reply unless the model/config name is ambiguous.
The first visible reply should happen immediately:
- send one short kickoff status update
- ask the first grouped setup form
Do not read INTAKE.md, RUNTIME.md, or EXAMPLES.md before the first grouped form.
Only after the user answers Round 1 should you read deeper reference files.

Guided setup flow

Resolve the target config key from the user's model/config name.
Start with one short high-level question round. The first question groups should be exactly:
- Run plan
- Output
- GPUs
After Round 1 answers, read INTAKE.md and follow its deeper intake flow.
Read RUNTIME.md only when you are about to do discovery or execution bootstrap.
Ask high-level setup questions first, then do lightweight config discovery, then ask filter-specific questions.
Do not ask TP / sequence length / concurrency until discovery has produced concrete options.
For smoke runs, offer a fast-path Filters choice:
- Use recommended smoke defaults
- Review each filter
- Use full discovered sweep
Summarize the final plan and get a clear go/no-go from the user with a Confirm question before executing.
Only after confirmation, read the needed phase docs and start execution.

Read the full file on GitHub · 107 lines

Files

What ships with it

28 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.

2mo ago First seen · 107 lines · 42 tokens per session scan A d4cc05c784dd

Subscribe to this mod's changes

inferencex-optimize is a skill published in the GitHub repository AMD-AIM/inference-skill (5 stars, last pushed 5mo ago), licensed MIT. It adds 42 tokens to every session and 1,380 once invoked, about $0.0002 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 2026-08-31.

Analytics and metrics Scientific

inferencex-optimize next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
inferencex-optimizeAMD-AIM/inference-skill	Skill	5	5mo ago	MIT	A	42	Claude Code, Codex
instrument-data-to-allotropeanthropics/knowledge-work-plugins	Skill	35k	today	Apache-2.0	A	123	Claude Code
exploratory-data-analysisK-Dense-AI/scientific-agent-skills	Skill	56k	5d ago	MIT	A	83	Claude Code
matlabK-Dense-AI/scientific-agent-skills	Skill	56k	5d ago	MIT	A	44	Claude Code
phylogeneticsK-Dense-AI/scientific-agent-skills	Skill	56k	5d ago	MIT	A	71	Claude Code, Codex
research-engineerdavila7/claude-code-templates	Skill	33k	today	MIT	A	43	Claude Code, Codex
mapping-to-snomedmaziyarpanahi/openmed	Skill	5.8k	today	Apache-2.0	A	205	Claude Code