openclaw-hermes-upgrade-kit

name	openclaw-hermes-upgrade-kit
description	Install two Hermes-like capabilities into OpenClaw: a learning loop for self-improvement and Honcho-based user modeling for richer cross-session memory. Use when the user wants a packaged OpenClaw upgrade kit, a public tutorial bundle, or a one-click distribution package that combines OpenClaw Learning Loop and Honcho onboarding.

OpenClaw Hermes Upgrade Kit

Use this skill when the user wants to package or install a combined upgrade bundle for OpenClaw that includes:

• a learning loop / self-improvement workflow
• Honcho-based user modeling and shared memory

This skill is for:

• public-facing delivery bundles
• installable OpenClaw add-on kits
• “Hermes-like experience” packaging for tutorials
• combining multiple reusable sub-skills into one distribution

This skill is not for:

• claiming feature parity with Hermes
• publishing private paths, keys, or identities
• promising fully automatic orchestration

Package Goal

The package should help a user add two missing layers to OpenClaw:

1. Learning Loop Turn solved problems into reusable learning artifacts and future skills.

2. Honcho User Modeling Add user profile inference, cross-session recall, and better multi-agent context sharing.

Delivery Shape

Build the package as:

• one top-level installable kit
• two bundled sub-skills
• one-click install script
• local Honcho deployment scaffold
• verification instructions

Safe Publishing Rules

• remove all real API keys
• remove personal workspace names
• remove personal filesystem paths
• use neutral peer names and workspace labels
• do not embed private logs or personal memory content

Recommended Bundle Layout

openclaw-hermes-upgrade-kit/
├── SKILL.md
├── README.md
├── INSTALL.md
├── run.command
├── install-openclaw-hermes-upgrade.sh
├── scripts/
│   └── verify_install.sh
├── templates/
│   ├── honcho.env.example
│   └── HONCHO_NEXT_STEPS.md
└── bundled-skills/
    ├── openclaw-learning-loop/
    └── honcho-memory-starter/

Operating Rules

1. Keep the learning-loop skill self-contained and path-agnostic.
2. Keep the Honcho skill focused on memory modeling, not orchestration.
3. Make the installer copy bundled skills into the user's OpenClaw workspace.
4. If cloning Honcho, clone the public upstream repository rather than shipping a private fork.
5. Scaffold configuration safely with .env.example, never real secrets.

Output Contract

When building this package, provide:

• a reusable top-level skill
• a public README
• a clear installation guide
• a one-click installer
• bundled sub-skills
• verification guidance