Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/vllm-project/vllm-skillsnpx agentmods add skills/vllm-project/vllm-skills/vllm-prefix-cache-benchWrote 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/vllm-project/vllm-skills/vllm-prefix-cache-bench)<a href="https://agentmods.dev/skills/vllm-project/vllm-skills/vllm-prefix-cache-bench"><img src="https://agentmods.dev/badge/skills/vllm-project/vllm-skills/vllm-prefix-cache-bench/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.
<a href="https://agentmods.dev/skills/vllm-project/vllm-skills/vllm-prefix-cache-bench"><img src="https://agentmods.dev/badge/skills/vllm-project/vllm-skills/vllm-prefix-cache-bench.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00064 | $0.01420 |
| Opus 5 | $0.00032 | $0.00710 |
| Sonnet 5 | $0.00013 | $0.00284 |
| Haiku 4.5 | $0.00006 | $0.00142 |
Grade A, and why
vllm-prefix-cache-bench scanned grade A with 1 finding 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.
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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json How it starts
The opening of the file, as written. The whole thing — 133 lines — stays where its author put it; the contents beside it link to each section on GitHub.
vLLM Prefix Caching Benchmark
Benchmark the efficiency of vLLM's automatic prefix caching (APC) feature. The offline script benchmarks/benchmark_prefix_caching.py runs directly against the vLLM engine (no server required). For online/serving tests, use vllm bench serve with the prefix_repetition dataset.
When to use
- User wants to measure the performance impact of prefix caching for repeated or partially-shared prompts.
- User wants to compare throughput/latency with and without
--enable-prefix-caching. - User wants to test prefix caching using a fixed synthetic prompt, a real dataset (e.g. ShareGPT), or a synthetic prefix/suffix repetition pattern.
Option 1 (default). Fixed Prompt with Prefix Caching
Runs a synthetic benchmark with a fixed prompt repeated multiple times to directly measure cache hit efficiency. No dataset download required.
python3 benchmarks/benchmark_prefix_caching.py \
--model Qwen/Qwen3-8B \
--enable-prefix-caching \
--num-prompts 1 \
--repeat-count 100 \
--input-length-range 128:256
To compare against the baseline without caching:
python3 benchmarks/benchmark_prefix_caching.py \
--model Qwen/Qwen3-8B \
--no-enable-prefix-caching \
--num-prompts 1 \
--repeat-count 100 \
--input-length-range 128:256
Option 2. ShareGPT Dataset with Prefix Caching
Uses real-world conversational data from ShareGPT to evaluate prefix caching with naturally occurring prompt sharing.
First, download the dataset:
wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
Then run the benchmark:
python3 benchmarks/benchmark_prefix_caching.py \
--model Qwen/Qwen3-8B \
--dataset-path ShareGPT_V3_unfiltered_cleaned_split.json \
--enable-prefix-caching \
--num-prompts 20 \
--repeat-count 5 \
--input-length-range 128:256
Option 3. Prefix Repetition Dataset (Online)
Uses vllm bench serve with the synthetic prefix_repetition dataset to test caching via the serving API. This requires a running vLLM server.
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.
- 11d ago First seen · 133 lines · 64 tokens per session scan A 6e7c9f96b7d4
vllm-prefix-cache-bench is a skill published in the GitHub repository vllm-project/vllm-skills (98 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 64 tokens to every session and 1,420 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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