Model Handoffv1.0.0

A research team rotates between Claude for deep analysis, GPT for code review, and Gemini for long-document summarization to cut token costs during multi-day research sprints. Each switch triggers a HANDOFF.md update so the next model picks up active hypotheses, file paths, and key findings without re-reading the whole session. This keeps the project coherent while saving 30-50% on premium model usage.

A software team uses different LLM copilots for different tasks, such as Cursor with Claude for refactoring and Codex for test generation. When a developer hands off a work-in-progress branch to another team member using a different model, HANDOFF.md captures the current branch, failing tests, architectural decisions, and next steps. This prevents context loss and reduces duplicated debugging effort.

A digital agency runs multiple client projects simultaneously, each using different models for content, code, and data analysis. Agents update HANDOFF.md after each milestone, enabling seamless model switching between clients without mixing contexts. This ensures consistent branding and project state tracking across a distributed team.

A power user runs local models on a laptop and cloud models on a phone, switching based on offline availability. HANDOFF.md, synced via a shared workspace, lets the phone model instantly resume a coding session started on the laptop. This delivers a persistent AI assistant experience across heterogeneous environments.

A university lab deploys multiple specialized agents (literature review, data analysis, paper drafting) backed by different LLMs. When the data analysis agent finishes, it updates HANDOFF.md with findings, file paths, and open questions, allowing the drafting agent on a different model to begin immediately. This reduces ramp-up time and maintains reproducibility in research workflows.

The core skill is open-source and free for individual developers, with a paid tier that adds cloud sync for HANDOFF.md across team members and models. Teams pay per seat to ensure consistent handoff context and audit logs across their AI stack.

Sold as a platform that automatically maintains HANDOFF.md and similar context files across an organization's AI agents, integrating with Slack, GitHub, and internal wikis. It offers governance, versioning, and secret-scanning to prevent leaking credentials in handoff files.

The skill is bundled as a paid add-on within AI model marketplaces or orchestrators (e.g., Poe, Vellum), where users pay for seamless handoff between hosted models. Revenue is shared with the platform per handoff transaction or as a flat monthly fee.