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openhuman-bridge

Map OpenHuman Jarvis patterns into TEL scheduling and tool-output compression.

Updated Aug 27, 2026
Search Tags:#context refresh#integration guidance#desktop assistant#tel#openhuman#tool compression#ux presence
One-click install
npx skills add https://github.com/Z5Jonathan-maker/ai-coding-os --skill openhuman-bridge
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: openhuman-bridge
Source: https://github.com/Z5Jonathan-maker/ai-coding-os/tree/main/claude/skills/openhuman-bridge
Command: npx skills add https://github.com/Z5Jonathan-maker/ai-coding-os --skill openhuman-bridge

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill translates OpenHuman’s “Jarvis desktop” value into concrete, composable patterns you can apply to your existing TEL + Claude Code + memory pipeline without trying to rebuild the whole desktop product.

Core Features & Use Cases

  • Use-case routing guide: Maps when to choose “openhuman”-style experience and what parts should be installed vs implemented in your environment.
  • Pattern lift list (with boundaries): Extracts three genuinely portable patterns (auto-fetch cadence, tool-output boundary compression, presence-as-UX) and clearly flags six areas that should not be lifted cleanly (voice loop, Google Meet agent, massive auth/UI flow).
  • Implementation direction: Recommends how to extend your TEL with periodic refresh, add a tool-compression helper for augmenter outputs, and optionally model a “presence layer” conceptually.

Quick Start

Ask: Compose a TEL-driven periodic-context refresh plan that matches the OpenHuman 20-minute auto-fetch cadence, and outline a tool-compress module that reduces tool-call output before it re-enters my prompts.

Frequently Asked Questions about openhuman-bridge

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I integrate periodic context refresh into a TEL scheduling workflow?▼

Periodic context refresh integrates into TEL scheduling by mapping OpenHuman's 20-minute auto-fetch cadence to your pipeline, ensuring your AI assistant retrieves updated context at regular intervals without manual prompting.

What is tool-output compression and how does it reduce prompt costs?▼

Tool-output compression reduces prompt costs by compressing tool-call outputs before they re-enter your prompts, preventing large augmenter payloads from inflating token usage and degrading context window efficiency.

How do I add a desktop mascot presence layer to a coding-first AI assistant?▼

Adding a presence-as-UX layer involves conceptually modeling a desktop mascot presence without rebuilding the full UI, directing how your AI assistant manifests visually during voice-first feature requests.

Can I lift OpenHuman's voice loop and Google Meet agent into my own stack?▼

OpenHuman's voice loop and Google Meet agent should not be lifted cleanly; this integration focuses on three portable patterns—auto-fetch cadence, tool-output compression, and presence-as-UX—while flagging complex UI flows as boundaries.

When should I not try to rebuild a full Jarvis desktop ecosystem?▼

You should avoid rebuilding the full Jarvis desktop ecosystem when you only need specific composable patterns like periodic refresh or tool compression, rather than replicating massive auth flows and voice loop infrastructure.

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