The Model Handoff skill ensures seamless context continuity when switching between different LLM models by maintaining a persistent state file.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install model-handoff
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install model-handoff using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Model Handoff?
Model Handoff is a strategic utility designed to solve the cold-start problem inherent in LLM interactions. In professional AI workflows, users often rotate between models like Claude, GPT, and Gemini to optimize for specific tasks or token costs. This skill creates and manages a HANDOFF.md file, acting as a high-density, fast-boot summary that allows a new model to instantly inherit the project state, behavioral rules, and active task list.
By utilizing this skill within the ecosystem of Openclaw Skills, developers can ensure that their AI agents remain consistent and informed, regardless of the underlying model being used. It eliminates the need for manual re-prompting and prevents context loss during long, multi-model sessions.
Model Handoff Use Cases
- Switching between different LLM providers (e.g., transitioning from Claude to GPT-4o) mid-session.
- Reducing token consumption by rotating to a smaller model while keeping active task context.
- Resuming complex development projects after a significant break or session timeout.
- Ensuring multi-agent teams stay aligned on the latest project milestones and file paths.
- Onboarding a new model to a workspace with specific behavioral expectations and persona rules.
How Model Handoff Works
- The skill monitors the session for triggers such as explicit model switch requests or significant project milestones.
- It generates or updates a HANDOFF.md file at the workspace root, capturing dense metadata about active projects and agent roles.
- The skill references external files like SOUL.md or USER.md to ensure persona and user preferences are preserved without duplication.
- A reference is added to AGENTS.md, instructing any incoming model to read the handoff file immediately upon activation.
- The user is provided with a specific command to trigger the new model's self-loading process.
Model Handoff Setup
To integrate this into your workflow, ensure your agent has permission to write to the workspace root. You can initialize the environment using the following command:
touch HANDOFF.md && echo "# HANDOFF.md" > HANDOFF.md
Ensure that your AGENTS.md file includes a directive for new models to prioritize reading the handoff file upon session start.
Model Handoff Data Schema & Taxonomy
The skill organizes context using a structured Markdown schema to ensure high readability for both humans and LLMs:
| Section | Purpose | Content Type |
|---|---|---|
| Who you are | Persona Preservation | Agent ID, tone, and behavioral rules |
| Who you're helping | User Context | User role, preferences, and location |
| Active projects | Project State | Specific file paths and immediate next steps |
| Key credentials | Security | Pointers to credential files (never raw secrets) |
| Last updated | Freshness | Timestamp and brief session summary |
This schema ensures that Openclaw Skills maintain a clean separation between sensitive data and operational context.
Model Handoff Advanced Features
- Proactive Updates: Automatically refreshes the handoff state during long sessions without requiring user intervention.
- Multi-Agent Roster: Tracks IDs and roles for complex workflows involving multiple specialized agents.
- Zero-Knowledge Secrets: Uses a pointer-based system to reference configuration files, ensuring secrets are never exposed within the context file.
- Fast-Boot Integration: Connects with AGENTS.md to automate the ingestion of context by the receiving model.
SKILL.md
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