llmquant/llmquant-hermes/llmquant-setup

Claude Code and agent skill by llmquant/llmquant-hermes

Trust tier 3
  • community
Source check pending

Checking that the SKILL.md file is still in the repository.

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.

Install

npx vskill@latest install llmquant/llmquant-hermes/llmquant-setup

SKILL.md

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

  1. Fetch the canonical setup document:

<https://raw.githubusercontent.com/LLMQuant/llmquant-hermes/master/SETUP.md>

  1. 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.

  1. 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.

Security

  • Security scanPassed, 90 of 100Pattern checks on the skill's files.
  • Model reviewPassed

Quality evaluation

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