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Handoff

CommunityPopular
rohitg00
handoff

Resume the most recent agent session for the current working directory, leading with any unanswered question. Use when the user says "where were we", "resume", "handoff", "pick up where I left off", or starts a session with no fresh context.

Overview

Publisherrohitg00
Repositoryagentmemory
Skill namehandoff
Stars
28.6K
Forks
2.5K
Bundled files
1
LicenseApache-2.0
Links
Skill sourceRepositoryHomepage
  • 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.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by rohitg00 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.
  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/rohitg00/agentmemory.git /tmp/agentmemory
mkdir -p .claude/skills
cp -r /tmp/agentmemory/plugin/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.

The user wants to resume work. Optional cwd override: $ARGUMENTS

Quick start

json
memory_sessions { "limit": 20 }

Pick the most recent session whose cwd matches this project, then: memory_recall { "query": "<session top concepts>", "limit": 10 }.

Expected output:

text
Resuming 7f3a9c2 "Auth refresh rework".
Open question: should logout revoke all device tokens or just the current one?
Next step: decide revoke scope, then update auth/logout.ts.

Why

Match the session by directory boundary, not raw prefix, so a sibling repo never gets mistaken for this one. Never invent observations for an empty session.

Workflow

  1. Resolve the project path: if $ARGUMENTS is given, normalize it to absolute (path.resolve(process.cwd(), $ARGUMENTS)); else use the cwd.
  2. Call memory_sessions. Pick the most recent session whose normalized cwd matches by directory boundary: equality, OR cwd.startsWith(projectPath + sep), OR projectPath.startsWith(cwd + sep). Prefer completed over abandoned. No match: fall back to the single most recent session overall.
  3. If the session ended on an unanswered user-facing question, surface it FIRST. Look in summary or recent conversation observations whose narrative ends in ?.
  4. Summarize: title/summary, key files, key decisions or errors, using memory_recall on the top concepts, limit 10.
  5. End with one concrete "next step?" pointer.

Anti-patterns

WRONG: session.cwd.startsWith(projectPath) matches /repo-a-staging when the project is /repo-a, resuming the wrong repo's session.

RIGHT: session.cwd === projectPath || session.cwd.startsWith(projectPath + sep), a directory-boundary check that cannot cross sibling repos.

Checklist

  • cwd override resolved to an absolute, normalized path.
  • Match used a directory-boundary check, not a raw prefix.
  • Unanswered question (if any) leads the response.
  • Empty session is reported plainly, with an offer to start fresh.

See also

  • recap, session-history, recall: same session data, broader views.

Troubleshooting

See ../_shared/TROUBLESHOOTING.md if memory_sessions or memory_recall is not available.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Handoff AI skill do?

Resume the most recent agent session for the current working directory, leading with any unanswered question. Use when the user says "where were we", "resume", "handoff", "pick up where I left off", or starts a session with no fresh context.

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/rohitg00/agentmemory/tree/main/plugin/skills/handoff. TypingMind reads its SKILL.md and bundles its files 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?

Yes. It is published on GitHub by rohitg00 under the Apache-2.0 license. 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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