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Agent skill

Skippy Spec Bench

by Mesh-LLM in Mesh-LLM/mesh-llm

A skill your agent uses when testing or benchmarking target/draft GGUF pairs for speculative decoding compatibility, tokenizer agreement, draft acceptance rate, or staged verification behavior.

Apache-2.0Auto-check passedAI & LLM Engineering

Install Skippy Spec Bench

skills CLI
$ npx skills add Mesh-LLM/mesh-llm --skill skippy-spec-bench -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Mesh-LLM/mesh-llm skippy-spec-bench --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/Mesh-LLM/mesh-llm.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/skippy-spec-bench .claude/skills/skippy-spec-bench && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
skippy-spec-bench
GitHub stars
3.5k
Token cost
~260 tokens
SKILL.md length
84 words
Files
1
Skills in repo
25
Repo updated
First seen
Licence
Apache-2.0

At a glance

A skill your agent uses when testing or benchmarking target/draft GGUF pairs for speculative decoding compatibility, tokenizer agreement, draft acceptance rate, or staged verification behavior.

  • Benchmarking target/draft GGUF pairs for speculative decoding compatibility
  • SKILL.md covers What It Checks and Repo Notes
  • Calls cargo and jq
  • Tokenizer agreement

What it does

Skippy Spec Bench is an agent skill from Mesh-LLM/mesh-llm. Use this skill when testing or benchmarking target/draft GGUF pairs for speculative decoding compatibility, tokenizer agreement, draft acceptance rate, or staged verification behavior.

Its SKILL.md is about 260 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in AI & LLM Engineering, covering LLM inference and serving. It works with llama.cpp. The repository describes itself as: Distributed AI/LLM for the people. Share compute privately or publicly to power your agents and chat. The licence is Apache-2.0.

When your agent uses it

  • Benchmarking target/draft GGUF pairs for speculative decoding compatibility
  • Tokenizer agreement
  • Draft acceptance rate
  • Staged verification behavior

Example prompts

  • “/skippy-spec-bench”

What it can do on your machine

Read from SKILL.md and the folder at commit 43ddd24. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • cargo
    • jq

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Skippy Spec Bench loads about 260 tokens when it runs. Until then it costs about 51 tokens; SKILL.md has 84 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~51
When it runs · the whole SKILL.md, loaded when a task matches
~260

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from Mesh-LLM/mesh-llm at commit 43ddd24, republished under its Apache-2.0 licence (© Mesh-LLM). 84 words, ~260 tokens.

Download SKILL.mdSave it as .claude/skills/skippy-spec-bench/SKILL.md (or your agent's skills folder).
name
skippy-spec-bench
description
Use this skill when testing or benchmarking target/draft GGUF pairs for speculative decoding compatibility, tokenizer agreement, draft acceptance rate, or staged verification behavior.
metadata.short-description
Benchmark speculative target/draft pairs

skippy-spec-bench

Use this skill for target/draft speculative compatibility work.

What It Checks

  • Target and draft tokenization agreement.
  • Baseline target decode versus draft-verified decode.
  • Draft acceptance/rejection behavior.
  • Batched verification and checkpoint/restore behavior.
  • Recurrent-state implications for rollback.

Repo Notes

The old source repo used a standalone llama-spec-bench crate. It may not be present in this mesh checkout yet, so verify available packages before running commands:

bash
cargo metadata --no-deps --format-version 1 | jq -r '.packages[].name' | sort

If the spec bench is imported, keep it as a diagnostics/benchmark tool. Do not make normal mesh serving depend on it.

© Mesh-LLM, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .agents/skills/skippy-spec-bench of Mesh-LLM/mesh-llm.

Open the folder on GitHubat commit 43ddd24

Compare with similar skills

Skippy Spec Bench next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Skippy Spec Bench compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Skippy Spec Bench this skillMesh-LLM/mesh-llm3.5k—~260Automated safety check: PassApache-2.0
Qwen Mtp GgufR6410418/Jackrong-llm-finetuning-guide1.7k—~1.7kAutomated safety check: PassMIT
Hugging Face Local Modelshuggingface/skills11k3 repos~945Automated safety check: PassApache-2.0
Add Quantization Datatypeintel/auto-round1.6k—~1.5kAutomated safety check: PassApache-2.0
Quantized Exportwshobson/agents40k—~2kAutomated safety check: PassMIT
Distil Pii RedactorHybridAIOne/hybridclaw159—~1kAutomated safety check: PassMIT

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Works with

Categories

Questions about Skippy Spec Bench

What does Skippy Spec Bench do?

A skill your agent uses when testing or benchmarking target/draft GGUF pairs for speculative decoding compatibility, tokenizer agreement, draft acceptance rate, or staged verification behavior. Skippy Spec Bench is an agent skill from Mesh-LLM/mesh-llm. Use this skill when testing or benchmarking target/draft GGUF pairs for speculative decoding compatibility, tokenizer agreement, draft acceptance rate, or staged verification behavior.

When should I use Skippy Spec Bench?

Skippy Spec Bench fits situations like: benchmarking target/draft GGUF pairs for speculative decoding compatibility; tokenizer agreement; draft acceptance rate; staged verification behavior.

How do I install Skippy Spec Bench in Claude Code?

Run `npx skills add Mesh-LLM/mesh-llm --skill skippy-spec-bench -a claude-code`. Or copy the skill folder (.agents/skills/skippy-spec-bench in Mesh-LLM/mesh-llm) into .claude/skills/skippy-spec-bench in your project. Claude Code loads it when a task matches its description.

How do I install Skippy Spec Bench in Codex?

Run `npx skills add Mesh-LLM/mesh-llm --skill skippy-spec-bench -a codex`. Or copy the skill folder (.agents/skills/skippy-spec-bench in Mesh-LLM/mesh-llm) into .agents/skills/skippy-spec-bench in your project. Codex loads it when a task matches its description.

Can I use Skippy Spec Bench in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Mesh-LLM/mesh-llm --skill skippy-spec-bench -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/skippy-spec-bench, .gemini/skills/skippy-spec-bench, .github/skills/skippy-spec-bench and .opencode/skills/skippy-spec-bench in your project.

What does Skippy Spec Bench need to run?

Going by SKILL.md and its folder, Skippy Spec Bench needs the command-line tools its instructions call (cargo and jq).

Does Skippy Spec Bench access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Skippy Spec Bench safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Skippy Spec Bench use?

Skippy Spec Bench is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Skippy Spec Bench use?

About 260 tokens (SKILL.md is roughly 1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Skippy Spec Bench?

Skills that share tags, products or a category with Skippy Spec Bench: Qwen Mtp Gguf (R6410418/Jackrong-llm-finetuning-guide, 1.7k stars), Hugging Face Local Models (huggingface/skills, 11k stars), Add Quantization Datatype (intel/auto-round, 1.6k stars) and Quantized Export (wshobson/agents, 40k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Skippy Spec Bench?

Mesh-LLM (a GitHub organization) maintains it in Mesh-LLM/mesh-llm, which has 3,489 GitHub stars. The repository holds 25 skills in this directory. The repository was last updated on October 10, 2026.

Source: Mesh-LLM/mesh-llm on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.