Cache-DiT is a PyTorch inference engine for DiT models, with caching, parallel processing, quantization, and CPU offloading. It is for running diffusion-model pipelines with support for systems and hardware such as Diffusers, SGLang Diffusion, vLLM-Omni, ComfyUI, NVIDIA GPUs, Ascend NPUs, and AMD GPUs.
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.
npx agentmods add skills/vipshop/cache-dit/ptq-workflow-integrationnpx skills add vipshop/cache-dit --skill ptq-workflow-integrationgit clone --depth 1 https://github.com/vipshop/cache-ditWrote 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.
[](https://agentmods.dev/skills/vipshop/cache-dit/ptq-workflow-integration)<a href="https://agentmods.dev/skills/vipshop/cache-dit/ptq-workflow-integration"><img src="https://agentmods.dev/badge/skills/vipshop/cache-dit/ptq-workflow-integration.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00075 | $0.02380 |
| Opus 5 | $0.00037 | $0.01190 |
| Sonnet 5 | $0.00015 | $0.00476 |
| Haiku 4.5 | $0.00007 | $0.00238 |
Grade A, and why
ptq-workflow-integration 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 5d 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.
How it starts
The opening of the file, as written. The whole thing — 296 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PTQ Workflow Integration for cache-dit
Goal
Integrate one PTQ workflow in a way that feels native to cache-dit:
- public API stays simple
- backend-specific logic stays localized
- save/load UX is predictable
- fast tests and slow tests cover different risks
- docs show only the public workflow
This skill is based on lessons from the SVDQ PTQ integration, but SVDQ PTQ is a reference only.
Core Rule
Do not mechanically copy SVDQ PTQ files.
Use the SVDQ PTQ integration to learn:
- what belongs in public API vs private backend code
- where config validation should live
- where backend serialization and load logic should live
- how tests should be split by cost and purpose
- what documentation shape is acceptable for cache-dit
Do not reuse SVDQ PTQ by blind copy-paste.
Specifically do not copy without redesigning first:
- private helper structure
- class names or helper names
- logging layout
- exact file decomposition
- prompts, thresholds, or benchmark constants
- test bodies that only happen to fit SVDQ
Treat SVDQ as a style reference, not a template to replay.
When to Use
Use this skill when you need to:
- add a new PTQ backend or algorithm into cache-dit
- extend an existing PTQ backend with save/load support
- decide where PTQ files and tests should live
- review whether a PTQ integration follows cache-dit API style
- plan coverage for a PTQ feature before coding
Do not use this skill for:
- generic quantization work that does not involve PTQ workflow design
- blind upstream porting
- one-off benchmark scripts with no repository integration
Reference Style Rule
Use repo-relative references only.
- For cache-dit files, use paths like
src/cache_dit/quantization/config.py. - For docs, use paths like
docs/user_guide/QUANTIZATION.md. - For tests, use paths like
tests/quantization/test_svdquant_ptq.py. - Do not write machine-local absolute paths into the skill.
Design Principles to Keep
1. Public API symmetry matters
Prefer a user-facing flow like:
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.
- 5d ago First seen · 296 lines · 75 tokens per session scan A 323f68112017
ptq-workflow-integration is a skill published in the GitHub repository vipshop/cache-dit (1,270 stars, last pushed 2d ago), licensed Apache-2.0. It adds 75 tokens to every session and 2,380 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.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…