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.
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.
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Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.
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Repository health
Stars13
LicenseLICENSE
Default branchmaster
Open issues2
Status
Archived
Package contents
Files included with this skill beyond the listing page.
skill mdSKILL.md2,519 B
docsSUMMARY.md331 B
History
First seen on skills.sh
First recorded snapshot · 1 installs
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.
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