llm-transfer

Assembles self-contained master prompts for handing work to external LLMs.

Updated Aug 1, 2026
One-click install
npx skills add https://github.com/dxiiren/project-skeleton --skill llm-transfer-dxiiren
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llm-transfer
Source: https://github.com/dxiiren/project-skeleton/tree/main/.claude/skills/llm-transfer
Command: npx skills add https://github.com/dxiiren/project-skeleton --skill llm-transfer-dxiiren

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? When you need a second opinion or want to continue work in ChatGPT, Ollama, Gemini, or another LLM, that model has zero context about your repo, session, or task. This Skill packages the current work into one self-contained master prompt so a cold, tool-less model can act on it immediately. ## Core Features & Use Cases - Structured 9-section master prompt: Builds a handoff covering role, mission, context, current state, verbatim artifacts, constraints, task, output format, and open questions. - Dual target modes: Emits a full self-contained prompt for cold targets (browser ChatGPT, plain ollama run) or a lean orchestration brief for agentic targets (Codex, opencode, Aider) that can read the repo themselves. - Local, git-ignored archive: Saves each handoff to .claude/workspace/reports/transfers/{tool}/ as a dated Markdown file that is never committed. - Use Case: You hit a stubborn bug and want ChatGPT's take. Invoke the Skill, and it reads the real files, embeds the exact code and error output, and prints a copy-paste block plus a saved .md you can pipe into ollama run. ## Quick Start Ask the assistant to transfer the current task to ChatGPT or Ollama, and it will gather the relevant context and produce a ready-to-paste master prompt saved under the transfers folder.

Frequently Asked Questions about llm-transfer

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

FAQPage Schema
How do I hand off a coding task to ChatGPT or another LLM?▼

Invoke the Skill with /llm-transfer or ask to transfer the task to another LLM. It enters plan mode, gathers the relevant code, errors, and constraints verbatim, then assembles a self-contained master prompt you paste into the target model.

How do I give Ollama context about my local project?▼

A plain ollama run session has no repo or file access, so context must be embedded in the prompt. The Skill saves the handoff as a Markdown file you pipe in with cat file.md | ollama run model, and recommends raising the context window for large prompts.

What is the difference between agentic and cold LLM handoff targets?▼

Agentic targets like Codex or Aider can read the repo and run commands, so they receive a lean orchestration brief pointing to files and conventions. Cold targets like browser ChatGPT receive a fully self-contained prompt with all artifacts inlined.

Does the exported prompt include secrets or credentials?▼

Yes, artifacts are kept verbatim because the saved file is git-ignored and stays local. Content only leaves the machine when you paste it into a cloud service, so strip any live credential manually before sending to ChatGPT or Gemini.

Where are the generated handoff prompts saved?▼

Each handoff is saved as a dated Markdown file under .claude/workspace/reports/transfers/{tool}/, where {tool} is the target such as gpt, ollama, or gemini. The directory is git-ignored so files are never committed.