How do I install the Skippy Cache Family Bench skill?

Run npx skillmds@latest add mesh-llm/skippy-cache-family-bench in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Skippy Cache Family Bench safe to use?

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Which AI agents work with Skippy Cache Family Bench?

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Is Skippy Cache Family Bench free to use?

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Who published Skippy Cache Family Bench?

mesh-llm (@mesh-llm) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Skippy Cache Family Bench by mesh-llm

Name: Skippy Cache Family Bench
Author: mesh-llm

Skippy Cache Family Bench

Use this skill when benchmarking Skippy exact-prefix cache across model families, comparing Skippy against llama-server, producing README benchmark tables, updating crates/skippy-cache/README.md evidence, or diagnosing cache benchmark gaps by family or Hugging Face use case.

mesh-llm Updated Sep 17, 2026

File contents

skippy-cache-family-bench

Use this skill for reproducible Skippy cache benchmark evidence. The goal is to compare production cache payloads only: ResidentKv for dense families and KvRecurrent for recurrent/hybrid families. Do not report FullState as a production cache mode.

Workflow

Run the reproducible wrapper. It builds by default, then runs full-GGUF baselines, Hugging Face use-case prompts, and the README report renderer:
```
evals/skippy-cache-family-bench.sh /tmp/skippy-cache-family-bench
```

For fast iteration after a build, skip the build step:

SKIPPY_CACHE_SKIP_BUILD=1 evals/skippy-cache-family-bench.sh /tmp/skippy-cache-family-bench

If running the pieces manually, keep these matched settings for full-GGUF family baselines:

LLAMA_STAGE_BUILD_DIR=.deps/llama-build/build-stage-abi-cpu \
  python3 evals/skippy-cache-production-bench.py \
    --output-dir /tmp/skippy-cache-family-bench/full-gguf \
    --runtime-lane-count 1 \
    --llama-parallel 1 \
    --prefix-tokens 128

Run the use-case benchmark matrix against the same family set:

LLAMA_STAGE_BUILD_DIR=.deps/llama-build/build-stage-abi-cpu \
  python3 evals/skippy-cache-production-bench.py \
    --output-dir /tmp/skippy-cache-family-bench/use-cases \
    --runtime-lane-count 1 \
    --llama-parallel 1 \
    --prefix-tokens 128 \
    --use-case all

Render README-ready tables from the combined JSON outputs:

python3 evals/skippy-cache-family-report.py \
  --input /tmp/skippy-cache-family-bench/full-gguf/production-cache-bench.json \
  --input /tmp/skippy-cache-family-bench/use-cases/production-cache-bench.json \
  --output /tmp/skippy-cache-family-bench/readme-tables.md

Reporting Rules

Keep rows and columns ordered by related family: Qwen3Next, Falcon-H1, Llama, Qwen3 dense, DeepSeek2, GLM-4.7 Flash, GLM4, Gemma4 A4B, Gemma4 E4B, Gemma3, Gemma2, OLMo, MiniMax M2.7.
Always report Skippy versus llama-server for full-GGUF rows.
Keep DeepSeek3 in package-only evidence unless a machine can run a monolithic full-GGUF llama-server baseline.
Use one generated token and matched prefix tokens for apples-to-apples rows.
If a family fails correctness, leave the benchmark row out of promoted README evidence and explain the failure in crates/skippy-cache/TODO.md.
Preserve raw outputs under /tmp/... or another explicit run directory; do not paste ad hoc numbers without the backing production-cache-bench.json.

---name: skippy-cache-family-benchdescription: Use this skill when benchmarking Skippy exact-prefix cache across model families, comparing Skippy against llama-server, producing README benchmark tables, updating crates/skippy-cache/README.md evidence, or diagnosing cache benchmark gaps by family or Hugging Face use case.---# skippy-cache-family-benchUse this skill for reproducible Skippy cache benchmark evidence. The goal is tocompare production cache payloads only: `ResidentKv` for dense families and`KvRecurrent` for recurrent/hybrid families. Do not report `FullState` as aproduction cache mode.## Workflow1. Run the reproducible wrapper. It builds by default, then runs full-GGUF baselines, Hugging Face use-case prompts, and the README report renderer: ```bash evals/skippy-cache-family-bench.sh /tmp/skippy-cache-family-bench ```2. For fast iteration after a build, skip the build step: ```bash SKIPPY_CACHE_SKIP_BUILD=1 evals/skippy-cache-family-bench.sh /tmp/skippy-cache-family-bench ```3. If running the pieces manually, keep these matched settings for full-GGUF family baselines: ```bash LLAMA_STAGE_BUILD_DIR=.deps/llama-build/build-stage-abi-cpu \ python3 evals/skippy-cache-production-bench.py \ --output-dir /tmp/skippy-cache-family-bench/full-gguf \ --runtime-lane-count 1 \ --llama-parallel 1 \ --prefix-tokens 128 ```4. Run the use-case benchmark matrix against the same family set: ```bash LLAMA_STAGE_BUILD_DIR=.deps/llama-build/build-stage-abi-cpu \ python3 evals/skippy-cache-production-bench.py \ --output-dir /tmp/skippy-cache-family-bench/use-cases \ --runtime-lane-count 1 \ --llama-parallel 1 \ --prefix-tokens 128 \ --use-case all ```5. Render README-ready tables from the combined JSON outputs: ```bash python3 evals/skippy-cache-family-report.py \ --input /tmp/skippy-cache-family-bench/full-gguf/production-cache-bench.json \ --input /tmp/skippy-cache-family-bench/use-cases/production-cache-bench.json \ --output /tmp/skippy-cache-family-bench/readme-tables.md ```## Reporting Rules- Keep rows and columns ordered by related family: Qwen3Next, Falcon-H1, Llama, Qwen3 dense, DeepSeek2, GLM-4.7 Flash, GLM4, Gemma4 A4B, Gemma4 E4B, Gemma3, Gemma2, OLMo, MiniMax M2.7.- Always report Skippy versus llama-server for full-GGUF rows.- Keep DeepSeek3 in package-only evidence unless a machine can run a monolithic full-GGUF llama-server baseline.- Use one generated token and matched prefix tokens for apples-to-apples rows.- If a family fails correctness, leave the benchmark row out of promoted README evidence and explain the failure in `crates/skippy-cache/TODO.md`.- Preserve raw outputs under `/tmp/...` or another explicit run directory; do not paste ad hoc numbers without the backing `production-cache-bench.json`.

Frequently asked questions