SkillsBench is a benchmark for measuring how effectively AI agents use modular skills—folders containing instructions, scripts, and resources—to complete specialized tasks. It helps researchers and developers evaluate both skill quality and agent behavior, including tasks that require combining multiple skills. The catalogue’s skills and instructions are evaluated as part of this workflow.
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 skills add benchflow-ai/skillsbench --skill prefix-cache-replaygit clone --depth 1 https://github.com/benchflow-ai/skillsbenchWrote 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/benchflow-ai/skillsbench/prefix-cache-replay)<a href="https://agentmods.dev/skills/benchflow-ai/skillsbench/prefix-cache-replay"><img src="https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/prefix-cache-replay/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/benchflow-ai/skillsbench/prefix-cache-replay"><img src="https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/prefix-cache-replay.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00107 | $0.02353 |
| Opus 5 | $0.00053 | $0.01177 |
| Sonnet 5 | $0.00021 | $0.00471 |
| Haiku 4.5 | $0.00011 | $0.00235 |
Grade A, and why
prefix-cache-replay 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 9d 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 — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Overview
Modern LLM serving systems — vLLM, SGLang, Mooncake — pack the KV tensors of a prompt into fixed-size blocks of block_size tokens (typically 512). The cache is keyed by a block hash where each hash encodes both the block's own token content and the content of every block before it in the prompt. Two requests that share the first K conversation turns therefore share the first K block hashes, and the cache can reuse those blocks without recomputing attention.
This is block-level prefix caching. It is not the same thing as full-prompt prompt caching (Anthropic, OpenAI), where the cache stores whole prompts and looks them up by exact match. Prefix caching reuses partial prompts; prompt caching does not.
Longest-prefix hit semantics (policy-independent)
Let a request have hash_ids = [h_0, h_1, ..., h_{n-1}] and input_length = L.
The prefix hit length is the largest integer k such that h_0, h_1, ..., h_{k-1} are all resident in the cache at the time the request arrives.
kmust start at index 0. Reuse ofh_2whenh_0is absent does not count.- The scan stops at the first miss. No skip-ahead, no set intersection.
- Hit tokens for the request =
min(k * block_size, L). Theminhandles the last partial block (whenLis not a multiple ofblock_size). Always apply it — do not returnk * block_sizeunclamped.
After the prefix scan, every block in hash_ids — hit or miss — is accessed against the eviction policy in order. Hits update policy state (recency / frequency); misses admit the block and may trigger evictions. What "resident" means is policy-specific, as spelled out below.
S3FIFO (Yang et al., SOSP 2023 — "FIFO queues are all you need")
S3FIFO replaces LRU / LFU with three static FIFO queues plus a per-block saturating frequency counter. Three things to know:
- Three queues, all FIFO (tail = most recently added):
- Small (S) — sized at
round(capacity * small_ratio). Newly admitted blocks land here. Defaultsmall_ratio = 0.1. - Main (M) — sized at
capacity - small_cap. Holds the working set promoted from S. - Ghost (G) — same size as main; metadata only. Remembers recently evicted block hashes so a re-access can fast-track to M. Ghost entries are NOT resident — a prefix that lands in G is a miss for hit-token accounting.
- Small (S) — sized at
- Each block carries a saturating frequency counter
freqclamped to[0, max_freq]. Defaultmax_freq = 3. Whenever a resident block is accessed, incrementfreqand clamp. - Admission and eviction differ between queues, described below.
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.
- 9d ago First seen · 111 lines · 107 tokens per session scan A 28ced940ec27
prefix-cache-replay is a skill published in the GitHub repository benchflow-ai/skillsbench (1,764 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 107 tokens to every session and 2,353 once invoked, about $0.0005 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 2026-09-03.
Other skills, from other repositories
rag-eval
NVIDIA RAG Blueprint evaluation guidance for measuring retrieval and answer quality with stable datasets, baselines, and reproducible scoring workflows.
local-llm-ops
Local LLM operations with Ollama on Apple Silicon, including setup, model pulls, chat launchers, benchmarks, and diagnostics.
fixed-tensor-testing
Test ML functions with fixed input tensors for reproducibility.
dbscan-custom-metric
Run DBSCAN clustering with a custom distance metric using sklearn, including how to define weighted Euclidean metrics and extract cluster centroids.
parallel-grid-search
Parallelize hyperparameter grid search using joblib for efficient multi-core execution.
json-data-extraction
Extract, parse, and query JSON data from large enterprise files efficiently.