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Handoff

OrganizationPopular
EpicenterHQ
handoff

Draft a self-contained prompt for a fresh ChatGPT, Claude Code, or other agent session. Use when the user asks for a handoff, continuation prompt, or something to copy and paste into another session. Do not use for a progress summary or for asking another agent to review or implement work within the active session.

Overview

PublisherEpicenterHQ
Repositoryepicenter
Skill namehandoff
Stars
4.8K
Forks
386
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by EpicenterHQ on GitHub. Read the source before you install it.

Installation

Install the Handoff AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/EpicenterHQ/epicenter/tree/main/.agents/skills/handoff
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/EpicenterHQ/epicenter.git /tmp/epicenter
mkdir -p .claude/skills
cp -r /tmp/epicenter/.agents/skills/handoff .claude/skills/handoff
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Handoff in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Handoff on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Handoff is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Handoff

Write one prompt the user can paste into a fresh session. Use the destination the user names; otherwise keep it agent-neutral. This skill prepares the handoff and does not launch or supervise the recipient. Return only the prompt. If the user is designing or reviewing the handoff itself, a short note before it is fine.

Give the next session enough context to make the next useful judgment. The recipient cannot see this conversation: preserve the intent, evidence, and reasoning it would otherwise lose, and point to accessible sources for details it can recover. Do not assume tools or delegation arrangements from the recipient's name.

Ground it first

Look before writing. For coding work that usually means:

bash
git status --short --branch
git diff --name-status
git diff -- <relevant paths>

Then read the files, tests, specs, ADRs, or logs the prompt will point at, and distinguish work completed from checks actually run, their results, and what remains unverified. For non-coding work, gather the equivalent source material. Skip the grounding pass only for something obviously small.

Match the prompt to the recipient's known access. Identify the checkout and relevant paths when it can read them; otherwise include the excerpts or evidence needed for the next judgment. When access is unknown, include the essential context and name any missing source or tool needed to proceed.

Write it

Lead with the mission and the artifact wanted. Explain where the work stopped, what remains unresolved, and the next useful action or question. Give the recipient enough of the larger objective to understand why that step matters and what remains afterward.

Two things are easy to leave out and expensive to lose: the evolution that explains the current direction (what the user reacted to, what was tried and dropped), and what the user will recognize as right beyond a passing test run.

For implementation handoffs, direct the recipient to implement and verify the accepted outcome through completion, with independent adversarial-review checkpoints suited to the work. Preserve agreed checkpoints and choose useful ones from the task context; where the evidence is still missing, have the recipient choose and revise their placement as implementation develops. Place reviews where findings can change dependent work before it is built. Review the cumulative implementation and remaining plan, resolve findings, update the plan where those findings change it, and continue within the accepted scope. A checkpoint ends the review, not the assignment. Preserve explicit user limits and narrower research or review-only deliverables.

Carry forward unresolved findings and the task-start baseline so cumulative reviews can distinguish task changes from existing work. If implementation has not begun, have the recipient capture that baseline before editing. If an ongoing task lacks a baseline, state the gap rather than treating the handoff state as evidence of what predates the task.

Name real hazards: dirty user work, destructive git, deploys, migrations, security, licensing boundaries, dead paths to avoid, explicit non-goals. Leave out hazards that are merely conceivable.

Let length follow the mission. Keep history when it explains the current direction or prevents repeating a failed approach. Omit transcript bulk and details the recipient can readily recover from the sources you identify.

The posture that makes it work

Say what you think is true and why. Distinguish explicit user decisions from observed behavior and your interpretation. The recipient should preserve the user's constraints while remaining free to re-check evidence, disagree with your conclusions, and choose its approach.

Close with the likely verification commands or evidence targets, and with what counts as done: a review memo, a PR-ready diff, a clean implementation branch, a verified command set, or a blocker list with the smallest remaining decisions.

Read the prompt as someone arriving cold: can they tell what to accomplish, what is known, and how to move forward without guessing at missing conversation? A thorough summary that leaves the next step unclear is not a finished handoff.

For a one-line /goal, use agent-goal. A progress summary for the user is an ordinary response grounded in branch and session state, not a handoff artifact.

Frequently asked questions

What does the Handoff AI skill do?

Draft a self-contained prompt for a fresh ChatGPT, Claude Code, or other agent session. Use when the user asks for a handoff, continuation prompt, or something to copy and paste into another session. Do not use for a progress summary or for asking another agent to review or implement work within the active session.

Why use Handoff on TypingMind?

Because you install it once and use it with any model. Handoff is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Handoff in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/EpicenterHQ/epicenter/tree/main/.agents/skills/handoff. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Handoff?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Handoff?

As many as you like. As long as a model supports skills, you can use Handoff with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Handoff AI skill free?

It is published on GitHub by EpicenterHQ. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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