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Agent skill

Skippy Serving

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

A skill your agent uses when running, configuring, debugging, or embedding skippy-serving, binary stage transport, OpenAI frontend integration, activation wire dtype settings, stage configs…

Apache-2.0Auto-check passedAI & LLM Engineering

Install Skippy Serving

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

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

GitHub CLI
$ gh skill install Mesh-LLM/mesh-llm skippy-serving --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-server .claude/skills/skippy-serving && 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-serving
GitHub stars
3.5k
Token cost
~380 tokens
SKILL.md length
100 words
Files
1
Skills in repo
25
Repo updated
First seen
Licence
Apache-2.0

At a glance

A skill your agent uses when running, configuring, debugging, or embedding skippy-serving, binary stage transport, OpenAI frontend integration, activation wire dtype settings, stage configs…

  • Embedding skippy-serving
  • SKILL.md covers Current Repo Shape, Validation and Rules
  • Calls cargo
  • Binary stage transport

What it does

Skippy Serving is an agent skill from Mesh-LLM/mesh-llm. Use this skill when running, configuring, debugging, or embedding skippy-serving, binary stage transport, OpenAI frontend integration, activation wire dtype settings, stage configs, lifecycle status, or nonblocking telemetry.

Its SKILL.md is about 380 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 Embeddings. It works with OpenAI. 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

  • Embedding skippy-serving
  • Binary stage transport
  • OpenAI frontend integration
  • Activation wire dtype settings

Example prompts

  • “/skippy-serving”

What it can do on your machine

Read from SKILL.md and the folder at commit 48bf685. 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

    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 Serving loads about 380 tokens when it runs. Until then it costs about 60 tokens; SKILL.md has 100 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~60
When it runs · the whole SKILL.md, loaded when a task matches
~380

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 48bf685, republished under its Apache-2.0 licence (© Mesh-LLM). 100 words, ~380 tokens.

Download SKILL.mdSave it as .claude/skills/skippy-serving/SKILL.md (or your agent's skills folder).
name
skippy-serving
description
Use this skill when running, configuring, debugging, or embedding skippy-serving, binary stage transport, OpenAI frontend integration, activation wire dtype settings, stage configs, lifecycle status, or nonblocking telemetry.
metadata.short-description
Run and debug skippy serving

skippy-serving

Use this skill for skippy serving, embedded runtime lifecycle, and binary stage-to-stage transport.

Current Repo Shape

The mesh integration embeds skippy-serving through Rust APIs instead of launching it as mesh's public OpenAI surface. Public OpenAI compatibility belongs in skippy-inference-api; skippy-serving should remain the backend stage runtime.

Important crates:

text
skippy/crates/skippy-serving
skippy/crates/skippy-protocol
skippy/crates/skippy-runtime
mesh/crates/mesh-llm/src/inference/skippy

Validation

Run cargo commands serially:

bash
cargo check -p mesh-llm
cargo test -p skippy-serving --lib
cargo test -p skippy-protocol --lib
cargo test -p mesh-llm-host-runtime --lib inference::skippy

For lifecycle/status changes, also run:

bash
cargo test -p mesh-llm-host-runtime --lib

Rules

Do not reintroduce standalone kv-server or ngram-pool dependencies into mesh. Keep structured outputs, tools, logprobs, and /v1/responses compatibility in skippy-inference-api.

Stage status exposed by mesh should be backend-neutral at the API boundary. Backend-specific details can remain in internal skippy structs.

© 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-server of Mesh-LLM/mesh-llm.

Open the folder on GitHubat commit 48bf685

Compare with similar skills

Skippy Serving 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 Serving compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Skippy Serving this skillMesh-LLM/mesh-llm3.5k—~380Automated safety check: PassApache-2.0
Chroma Vector DatabaseOrchestra-Research/AI-Research-SKILLs13k8 repos~2.3kAutomated safety check: PassMIT
CLIP Image-Text MatchingOrchestra-Research/AI-Research-SKILLs13k8 repos~1.7kAutomated safety check: PassMIT
Codebase Managementgiancarloerra/SocratiCode3.3k1 repos~1.8kAutomated safety check: PassAGPL-3.0
RAG ArchitectJeffallan/claude-skills12k1 repos~2kAutomated safety check: PassMIT
Xsaimoeru-ai/airi50k1 repos~1.3kAutomated safety check: PassMIT

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Works with

Categories

Questions about Skippy Serving

What does Skippy Serving do?

A skill your agent uses when running, configuring, debugging, or embedding skippy-serving, binary stage transport, OpenAI frontend integration, activation wire dtype settings, stage configs…. Skippy Serving is an agent skill from Mesh-LLM/mesh-llm. Use this skill when running, configuring, debugging, or embedding skippy-serving, binary stage transport, OpenAI frontend integration, activation wire dtype settings, stage configs, lifecycle status, or nonblocking telemetry.

When should I use Skippy Serving?

Skippy Serving fits situations like: embedding skippy-serving; binary stage transport; openAI frontend integration; activation wire dtype settings.

How do I install Skippy Serving in Claude Code?

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

How do I install Skippy Serving in Codex?

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

Can I use Skippy Serving 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-serving -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-serving, .gemini/skills/skippy-serving, .github/skills/skippy-serving and .opencode/skills/skippy-serving in your project.

What does Skippy Serving need to run?

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

Does Skippy Serving 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 Serving 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 Serving use?

Skippy Serving 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 Serving use?

About 380 tokens (SKILL.md is roughly 1.5k 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 Serving?

Skills that share tags, products or a category with Skippy Serving: Chroma Vector Database (Orchestra-Research/AI-Research-SKILLs, 13k stars), CLIP Image-Text Matching (Orchestra-Research/AI-Research-SKILLs, 13k stars), Codebase Management (giancarloerra/SocratiCode, 3.3k stars) and RAG Architect (Jeffallan/claude-skills, 12k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Skippy Serving?

Mesh-LLM (a GitHub organization) maintains it in Mesh-LLM/mesh-llm, which has 3,485 GitHub stars. The repository holds 25 skills in this directory. The repository was last updated on October 8, 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.