How do I install the Llmquant Setup skill?

Run npx skillmds@latest add llmquant/llmquant-setup 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 Llmquant Setup safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Llmquant Setup?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Llmquant Setup free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Llmquant Setup?

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

Llmquant Setup by llmquant — Agent Skill

Name: Llmquant Setup
Author: llmquant

Llmquant Setup

Connect LLMQuant Data to this Hermes instance, confirm the DeepSeek baseline, smoke-test auth for zero credits, install the playbooks, and schedule the first standing job. Use when the user says "set up llmquant", "connect llmquant data", "llmquant setup", or otherwise asks to wire LLMQuant Data into Hermes.

llmquant 675ee3b 2.4 KB Updated Sep 17, 2026

File contents

LLMQuant Setup

Thin entry point for connecting LLMQuant Data to Hermes. This skill does not contain the setup steps — the single canonical source is SETUP.md at the root of this repo. Your job is to fetch it and follow it exactly, so this skill never drifts out of sync with the real procedure.

Do this

Fetch the canonical setup document: https://raw.githubusercontent.com/LLMQuant/llmquant-hermes/master/SETUP.md
Follow it top to bottom. It is agent-facing and self-contained: a trust contract, an instance self-check, DeepSeek baseline guidance, and setup steps (get key or Remote MCP URL -> write config -> smoke-test -> install playbooks -> schedule first cron), each ending in a check.
If this repo is already tapped locally, read the local SETUP.md instead of fetching — same document.

If you cannot reach the network (fallback only)

The fetched SETUP.md is authoritative and far more complete. Use this skeleton only if you truly cannot retrieve it, and tell the user you're running from memory:

Never echo secrets. Back up ~/.hermes/config.yaml before editing; show the user the diff before writing; prefer the hosted Remote MCP URL.
Step 0: self-check with hermes --version, hermes skills tap --help, hermes cron --help, hermes model --help — verify CLI syntax on this instance before using it.
Model: DeepSeek is the recommended baseline; do not change model config without user consent.
Get credentials: ask the user to sign up at https://llmquantdata.com and paste a Remote MCP URL (Dashboard → Connect) or API key (→ API Keys).
Write config: add/update a single llmquant-data entry under mcp_servers (url preferred; stdio command/args/env fallback), then reload MCP.
Smoke-test: call the 0-credit personal_profile; report meta.remainingCredits.
Install + schedule: hermes skills tap add LLMQuant/llmquant-hermes, verify the skills are actually listed, then create one cron for the playbook, channel, and time the user picks.

---name: llmquant-setupdescription: Connect LLMQuant Data to this Hermes instance, confirm the DeepSeek baseline, smoke-test auth for zero credits, install the playbooks, and schedule the first standing job. Use when the user says "set up llmquant", "connect llmquant data", "llmquant setup", or otherwise asks to wire LLMQuant Data into Hermes.---# LLMQuant SetupThin entry point for connecting LLMQuant Data to Hermes. **This skill does notcontain the setup steps** — the single canonical source is `SETUP.md` at theroot of this repo. Your job is to fetch it and follow it exactly, so thisskill never drifts out of sync with the real procedure.## Do this1. Fetch the canonical setup document: <https://raw.githubusercontent.com/LLMQuant/llmquant-hermes/master/SETUP.md>2. Follow it top to bottom. It is agent-facing and self-contained: a trust contract, an instance self-check, DeepSeek baseline guidance, and setup steps (get key or Remote MCP URL -> write config -> smoke-test -> install playbooks -> schedule first cron), each ending in a check.3. If this repo is already tapped locally, read the local `SETUP.md` instead of fetching — same document.## If you cannot reach the network (fallback only)The fetched `SETUP.md` is authoritative and far more complete. Use thisskeleton *only* if you truly cannot retrieve it, and tell the user you'rerunning from memory:- **Never echo secrets.** Back up `~/.hermes/config.yaml` before editing; show the user the diff before writing; prefer the hosted Remote MCP URL.- **Step 0:** self-check with `hermes --version`, `hermes skills tap --help`, `hermes cron --help`, `hermes model --help` — verify CLI syntax on this instance before using it.- **Model:** DeepSeek is the recommended baseline; do not change model config without user consent.- **Get credentials:** ask the user to sign up at <https://llmquantdata.com> and paste a Remote MCP URL (Dashboard → Connect) or API key (→ API Keys).- **Write config:** add/update a single `llmquant-data` entry under `mcp_servers` (`url` preferred; stdio `command`/`args`/`env` fallback), then reload MCP.- **Smoke-test:** call the 0-credit `personal_profile`; report `meta.remainingCredits`.- **Install + schedule:** `hermes skills tap add LLMQuant/llmquant-hermes`, verify the skills are actually listed, then create one cron for the playbook, channel, and time the user picks.

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