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Handoff

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TyrealQ
handoff

Capture session decisions, conventions, and lessons into plan files, auto-memory, and CLAUDE.md so a fresh session resumes cleanly. Use for "hand off," "wrap up," "update docs for next session," or before /compact.

Overview

PublisherTyrealQ
Repositoryq-skills
Skill namehandoff
Stars
109
Forks
1
Bundled files
Instructions only
LicenseMIT
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 TyrealQ 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/TyrealQ/q-skills/tree/main/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/TyrealQ/q-skills.git /tmp/q-skills
mkdir -p .claude/skills
cp -r /tmp/q-skills/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

Captures the load-bearing outcomes of a session and writes them to the right durable location. Goal: a future session can pick up without re-deriving context from the transcript.

Workflow

Step 1 — Survey the session

Identify what changed beyond the literal edits.

bash
git log --oneline <session-start>..HEAD
git diff <session-start>..HEAD --stat

If session-start is unclear, infer from the first commit timestamp after a context reset, or ask the user.

For each commit, note:

  • Decisions: terminology choices, structural conventions, methodological positions taken
  • Conventions reinforced or established: subtitle patterns, voice rules, argument structures, naming schemes
  • Controversies resolved: where the user pushed back and a final position landed
  • Lessons that emerged: when an early draft failed, what symptom triggered the fix, what rule the fix expressed (latent rules > literal pushbacks)

Skip routine work: typo fixes, formatting-only changes, mechanical refactors. Only capture outcomes a future session would otherwise have to re-derive.

Step 2 — Locate destinations

Discover what already exists; do not create new doc locations without asking.

bash
# Plan files (task tracking, current state, open items)
ls ~/.claude/plans/ 2>/dev/null | grep -i <project-keyword>

# Auto-memory (cross-session learnings keyed to project)
ls ~/.claude/projects/<project-id>/memory/ 2>/dev/null

# Project CLAUDE.md (project conventions, structure, banned terms)
ls <project-root>/CLAUDE.md 2>/dev/null

# User CLAUDE.md (cross-project preferences)
ls ~/CLAUDE.md 2>/dev/null

Match each session outcome to the right destination:

Outcome typeDestination
Task progress, completed items, residual open itemsPlan file
Verified-state anchors (specific phrasings that mark current correct state)Plan file
Writing/coding lessons, symptom → rule patternsAuto-memory *_lessons.md
Banned terms, terminology decisionsAuto-memory *_terms.md (or project CLAUDE.md if it already lists them)
Project-level conventions (file layout, naming, structure)Project CLAUDE.md
Cross-project user preferencesUser CLAUDE.md

Do not duplicate. If a rule lives in CLAUDE.md, do not also add it to memory; if a lesson lives in memory, do not restate it in the plan.

Step 3 — Apply updates

Read every target file before editing. Match the existing style, depth, and section conventions.

  • Plan files: mark resolved items with strikethrough or RESOLVED YYYY-MM-DD tag; append new open items; refresh "verified state" anchors with the line content as it stands now (not as drafted earlier in the session).
  • Auto-memory *_lessons.md: append numbered lessons (L7, L8, ...) with trigger + corrective move + brief rationale. Update the file's frontmatter description if the scope expanded.
  • MEMORY.md index: add a pointer line only if a new memory file was created. Update the existing entry's hook only if its underlying content shifted materially. Keep each line under ~150 chars.
  • Project CLAUDE.md: insert into the appropriate existing section (style rules, structure, etc.). Do not create a session log section. If a new convention does not fit any section, ask before adding.
  • User CLAUDE.md: only for preferences that generalize beyond the current project. Be conservative.

Step 4 — Produce a resume prompt

Output a single message the user can copy-paste verbatim into a fresh session as the opening prompt. The next Claude reads this message and a fresh, cold-started auto-memory load — nothing else from the prior transcript carries over. The resume prompt must be self-contained.

Wrap the resume prompt in a clearly delimited block (e.g., ```text ... ```) so the user can copy it cleanly.

Required sections, in order:

  1. Context — one sentence on what project this is and what stage the work is at.
  2. Read first — explicit absolute paths to the files the next Claude should load before acting (plan file, key memory files if not auto-loaded, the canonical source document, the active downstream document). Annotate each with one phrase on why.
  3. Where we left off — last 2–4 commits relevant to the resumed thread (hash + one-line summary). Do not list every commit; only the load-bearing ones.
  4. Conventions to honor — only conventions established or reinforced THIS session that have not yet propagated into memory or CLAUDE.md. If everything is durable, write "All conventions are captured in memory and the plan file."
  5. Next task — the single most actionable next item with a file:line pointer and a one-sentence statement of the change to make. If multiple, list up to 3 in priority order.
  6. Known gotchas — only if relevant: discrepancies, blockers, or non-obvious traps the next session should be aware of. Omit the section if none.

After the wrapped resume prompt, add a 2-line operator note for the user (outside the block):

  • where the new commits and doc updates landed (commit-hash range, files touched)
  • whether anything is uncommitted

Keep the resume prompt itself under ~250 words. The user pastes it; the next Claude executes from there.

Anti-patterns

  • Don't write session logs into project files. Permanent docs capture conventions, not session narratives.
  • Don't update CLAUDE.md with rules already in memory (and vice versa). Each rule has one durable home.
  • Don't create new doc locations without asking. Use what exists.
  • Don't restate edits as "decisions." A typo fix is not a decision. A terminology choice the user pushed back on twice is.
  • Don't bloat memory. Each memory file should have a clear scope. If a new lesson does not fit any existing file, ask before creating a new one.
  • Don't extract lessons the user has not validated. A first-draft pattern from one session is a hypothesis, not a rule. Wait for repeated correction or explicit confirmation before persisting.

Frequently asked questions

What does the Handoff AI skill do?

Capture session decisions, conventions, and lessons into plan files, auto-memory, and CLAUDE.md so a fresh session resumes cleanly. Use for "hand off," "wrap up," "update docs for next session," or before /compact.

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/TyrealQ/q-skills/tree/main/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?

Yes. It is published on GitHub by TyrealQ under the MIT 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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