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model-pr-history-knowledge

model-pr-history-knowledge is a skill for Claude Code from BBuf/AI-Infra-Auto-Driven-SKILLS. It costs 71 tokens per session (1,208 once invoked), scanned A, original, no licence file.

A reference guide based on earlier pull requests for optimizing models and model-serving systems such as SGLang, vLLM, TensorRT-LLM, and TokenSpeed.

In plain words
What is it for?
Use it for model-serving or optimization work that needs prior examples involving a particular model family, kernel or fusion changes, performance paths, or validation.
Why use it?
It helps an agent use evidence from previous changes when selecting implementation paths, optimization ideas, and checks for possible regressions.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the ai-infra-auto-driven-skills plugin — 12 skills shipped together

not rated 811repo +25 18d ago A scan 71 tokens no licence

Good fit Use it for model-serving or optimization work that needs prior examples involving a particular model family, kernel or fusion changes, performance paths, or validation.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/bbuf/ai-infra-auto-driven-skills/model-pr-optimization-history
View source ↗ BBuf/AI-Infra-Auto-Driven-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 BBuf/AI-Infra-Auto-Driven-SKILLS --skill model-pr-optimization-history
Clone the repo
git clone --depth 1 https://github.com/BBuf/AI-Infra-Auto-Driven-SKILLS

Made for: Claude Code.

Or install ai-infra-auto-driven-skills, the plugin that ships this one along with the rest of its 12 skills.

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 model-pr-history-knowledge

README.md
[![agentmods](https://agentmods.dev/badge/skills/bbuf/ai-infra-auto-driven-skills/model-pr-optimization-history/github.svg)](https://agentmods.dev/skills/bbuf/ai-infra-auto-driven-skills/model-pr-optimization-history)
Your own site
<a href="https://agentmods.dev/skills/bbuf/ai-infra-auto-driven-skills/model-pr-optimization-history"><img src="https://agentmods.dev/badge/skills/bbuf/ai-infra-auto-driven-skills/model-pr-optimization-history/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 model-pr-history-knowledge

Your own site · 80×15
<a href="https://agentmods.dev/skills/bbuf/ai-infra-auto-driven-skills/model-pr-optimization-history"><img src="https://agentmods.dev/badge/skills/bbuf/ai-infra-auto-driven-skills/model-pr-optimization-history.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 71 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,208 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 $0.00071 $0.01208
Opus 5 $0.00036 $0.00604
Sonnet 5 $0.00014 $0.00242
Haiku 4.5 $0.00007 $0.00121

Measured 11d ago against content hash 855a354f8006, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

model-pr-history-knowledge 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 11d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/query.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-pr-optimization-history/SKILL.md · 102 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

60 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. 11d ago First seen · 102 lines · 71 tokens per session scan A 855a354f8006

Subscribe to this mod's changes

model-pr-history-knowledge is a skill published in the GitHub repository BBuf/AI-Infra-Auto-Driven-SKILLS (811 stars, last pushed 18d ago), with no licence file. It adds 71 tokens to every session and 1,208 once invoked, about $0.0004 per session on Opus 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 2026-08-30.

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