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Documentation Index

Fetch the complete documentation index at: https://hubify.com/docs/llms.txt

Use this file to discover all available pages before exploring further.

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Skills Overview

A Skill is a single markdown file that teaches your local AI agent (Claude Code, Codex, Cursor, anything that reads system prompts or MCP servers) how to do one specific thing inside a Hubify lab. Drop it into your agent’s skills directory, type /skill-name, and the agent runs the recipe end-to-end against your live Convex data. Skills are the answer to the question Captain (the researcher) actually asks: “I already use Claude Code / Cursor / Codex every day. Why do I need a separate Hubify CLI?” Short answer: you don’t. Skills give your existing agent the same powers the dedicated hubify CLI has, just packaged as /slash-commands your agent already understands.

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Why skills exist

The dedicated hubify CLI is great when you want a deterministic, scriptable surface (hubify experiment run --pod h100, hubify paper publish, hubify status). But most research work is conversational, exploratory, and already happens inside an AI coding session. Skills meet you where you are:
  • No new tool to install. If you have Claude Code, Cursor, or Codex working today, you’re 60 seconds from /hubify-status returning real data.
  • Git-tracked, reviewable, forkable. Skills are markdown. Commit them to your lab repo. Share with collaborators. Fork to override behavior for your domain.
  • Composable with existing skills. Run /hubify-experiment-queue then /review (your normal code-review skill) on the experiment script before it dispatches to a GPU.
Skills wrap the MCP server (which already exposes raw tools like experiment_list, knowledge_search) with opinionated, Captain-friendly prompts. The MCP server is the “API layer”; skills are the “user interface.”
Hat-tip: the skills format and authoring workflow here is heavily inspired by gstack, Garry Tan’s open-source skills library for Claude Code. If you’ve used /qa, /review, or /ship from gstack, the Hubify skills will feel identical.

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What ships today

SkillWhat it doesAgents
/hubify-statusLists labs, shows active lab, last 5 agentEvents (read-only, safe everywhere)CC, Cursor, Codex
/hubify-experiment-queueWraps experiments.queue mutation with hypothesis prompt + priority pickerCC, Cursor, Codex
/hubify-runpodStarts/stops a GPU pod with cost confirmation gateCC, Cursor, Codex
/hubify-knowledge-addCaptures a finding into the lab knowledge base with auto-tag suggestionsCC, Cursor, Codex
/hubify-paper-readinessChecks paper draft against the 5-round publish-ready loopCC, Cursor, Codex
More are queued in the polish loop. If you want one we don’t ship, it’s a 30-line markdown file, see Authoring a skill.

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Install path

The fastest path is the MCP server (gives all skills access to your lab data) plus dropping the skill files into your agent’s skills directory.

Claude Code

claude mcp add hubify ... then drop skills in ~/.claude/skills/

Cursor

.cursor/mcp.json for tools, .cursor/rules/*.mdc for skills

Codex / others

Any agent that reads MCP servers + system prompts works

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Skill anatomy

Every Hubify skill is a single SKILL.md with frontmatter and a body. The body is a prompt the agent reads and executes step by step. 
---
name: hubify-status
description: List labs, show active lab, last 5 agentEvents
---

# /hubify-status

1. Call `lab_list` (MCP tool). Print the labs as a numbered list. Mark the active one (matches `--lab` flag).
2. Call `experiment_list --lab <active>` and `agent_event_list --lab <active> --limit 5`.
3. Render in a single table: lab name, # experiments running, last 5 events with relative timestamps.
4. If any lab returns 0 experiments AND 0 events in the last 24h, suggest `/hubify-experiment-queue`.
That’s the entire skill. The agent does the rest. No SDK, no Python, no JSON schemas. Just a recipe written for the agent the same way you’d write one for a junior researcher.

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See also