Mesh LLM is a distributed AI system that combines GPUs and memory from multiple machines and exposes them through one OpenAI-compatible inference server, which is a service that runs models and answers API requests. It is for people who want to share compute privately or publicly to run models for agents and chat, including models too large for one machine. The catalogue add-ons support workflows for operating and using the mesh.
Borrowing it
Nothing to install: this file belongs to Mesh-LLM/mesh-llm. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Mesh-LLM/mesh-llm/main/.agents/skills/skippy-prompt/SKILL.mdgit clone --depth 1 https://github.com/Mesh-LLM/mesh-llmWrote 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/mesh-llm/mesh-llm/skippy-prompt)<a href="https://agentmods.dev/skills/mesh-llm/mesh-llm/skippy-prompt"><img src="https://agentmods.dev/badge/skills/mesh-llm/mesh-llm/skippy-prompt/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/mesh-llm/mesh-llm/skippy-prompt"><img src="https://agentmods.dev/badge/skills/mesh-llm/mesh-llm/skippy-prompt.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 | $0.00082 | $0.01044 |
| Opus 5 | $0.00041 | $0.00522 |
| Sonnet 5 | $0.00016 | $0.00209 |
| Haiku 4.5 | $0.00008 | $0.00104 |
Grade A, and why
skippy-prompt 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 12d 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 — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
skippy-prompt
Use this skill for prompt-owned staged workflows. The skill is the launcher: Codex orchestrates sync, host-native builds, stage config generation, process startup, observation, prompt driving, and teardown.
Ownership Rules
- The machine where the user asks to launch prompt is always
stage-0. - Remote hosts are
stage-1..Nin the order provided by the user. - Bring down any running
mesh-llmserving on the chosen nodes before starting prompt-owned stage servers. - Do not bring back standalone
kv-serverorngram-pool. - Use
$HOME/tmpfor run roots, source syncs, logs, and bundles. Avoid/tmpunless the user explicitly asks for it. - Public OpenAI compatibility belongs in
openai-frontend, not prompt tooling. Prompt workflows are for development, diagnostics, and reproducible model checks. - Do not use
skippy-prompt promptas the launcher on this branch. The skill startsskippy-server serve-binarystages directly and usesskippy-prompt binaryas the interactive client.
Launch Workflow
- Confirm repo state, branch, commit, model ref/path, hosts, desired layer ranges, context size, and prompt mode.
- Stop existing mesh/runtime processes on every selected host:
mesh-llm stopfirst, then verify withps; usepkill -fonly if the scoped stop path fails. - Rsync the current source tree to each remote host under
$HOME/tmp/mesh-llm-prompt-src/<branch-or-sha>/, excluding build outputs and caches (target/,.git/,.deps/llama-build/, UInode_modules/). - Detect each host:
uname -s,uname -m, GPU inventory, compiler/runtime availability, and existing llama build cache. - Choose the best backend per host:
- macOS: Metal.
- Linux NVIDIA with CUDA toolchain: CUDA. Use this for
white.localunless CUDA is genuinely unavailable. - Linux AMD with ROCm toolchain: ROCm.
- Vulkan-capable Linux without CUDA/ROCm: Vulkan.
- CPU only as a last resort or explicit user request.
- Build on each host with repo-native
justtargets. Usejust buildon macOS andjust build-runtime backend=<backend> ...on Linux when UI rebuild is unnecessary. Do not hand-roll cargo/cmake build sequences. - Materialize or locate model/package inputs on the launcher. If the source model only exists locally, rsync package/materialized stage inputs to remote hosts.
- Start final stage first, then upstream stages, ending with local
stage-0. Use foreground TTY SSH for first repro/debug runs and tee logs under$HOME/tmp/skippy-prompt-runs/<run-id>/. - Wait for readiness of every stage, then attach
skippy-prompt binaryfrom the launcher to the local stage-0 endpoint. - Keep process handles or SSH sessions observable. Do not report success until stage servers are running and a prompt request has been attempted or the user explicitly only asked for startup.
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.
- 12d ago First seen · 103 lines · 82 tokens per session scan A 7e5dcb294704
skippy-prompt is a skill published in the GitHub repository Mesh-LLM/mesh-llm (3,384 stars, last pushed today), licensed Apache-2.0. It adds 82 tokens to every session and 1,044 once invoked, about $0.0004 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 2026-08-30.
Other skills, from other repositories
map
Build and commit a Cortex function knowledge graph — maps structural dependencies and domain intent relationships across all AI functions in the project. Supports --reduce (default on) for transitive reduction of the dependency graph.
context-add
Guided wizard to capture or update organizational Snowflake/Cortex standards into .cocoplus/context/ .md. Presents a menu of 6 standard types, collects answers via multi-turn dialogue, and commits the file.
cocolean
CocoLean — minimum viable Cortex surface discipline. Pre-build decision ladder and intensity mode management. Invoke with $lean, $lean lite, $lean full, or $lean ultra.
test
Enter the Test phase of CocoBrew. Reads spec.md test requirements, generates test cases, executes SQL validation and quality checks, records results in test.md. Can be re-run without full rebuild. Requires Build phase completion.
map-diff
Analyze the impact of staged git changes against the committed Cortex function knowledge graph — shows which downstream functions are affected before you commit.
map-explain
Produce a natural-language explanation of a specific Cortex function, business capability, or schema element from the committed knowledge graph.