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Handoff

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dsifry
handoff

Analyze the current session and write a self-contained handoff document so a fresh agent can resume the work with full context — outputs a single "Read XXX.md and do YYY." sentence

Overview

Publisherdsifry
Repositorymetaswarm
Skill namehandoff
Stars
421
Forks
54
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 dsifry 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/dsifry/metaswarm/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/dsifry/metaswarm.git /tmp/metaswarm
mkdir -p .claude/skills
cp -r /tmp/metaswarm/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 Skill

Capture everything a fresh agent with zero prior context needs to resume the current work, write it to a single self-contained markdown document, and hand it off with one unambiguous sentence:

Read docs/handoffs/<file>.md and do <the next concrete action>.

Use this when a session is ending, when context is about to be compacted, when you are switching machines or sessions, or when the user explicitly asks for a handoff. The goal is zero context loss: the receiving agent should be able to act correctly after reading exactly one file.


Output Contract

This skill produces exactly two things:

  1. A handoff document at docs/handoffs/handoff-<YYYY-MM-DD-HHmm>.md (created if the directory does not exist). It is comprehensive, self-contained, and links to — or quotes — every artifact the next agent must read.

  2. A single closing sentence, and nothing else after the document is written, of the exact form:

    text
    Read docs/handoffs/handoff-2026-06-17-1432.md and do <YYY>.

    Where <YYY> is one concrete, actionable next step (an imperative, not a vague theme). Good: "implement the parseConfig validation in src/config.ts:84 and make tests/config.test.ts pass". Bad: "continue the work" / "look into the config stuff".

The sentence is the deliverable the user hands to the next agent. The document is what makes that sentence safe to follow.


Method

Step 1 — Reconstruct what we are working on

Review the session to date and answer, concretely:

  • Objective: What is the user actually trying to accomplish? State it in one or two sentences.
  • Definition of Done: What does "finished" look like? Enumerate verifiable acceptance criteria if any exist.
  • Why: The motivation/problem behind the task, so the next agent does not re-litigate settled decisions.

Pull from: the user's original request, any GitHub Issue (gh issue view <n>), the design/plan docs, and the arc of the conversation.

Step 2 — Load any persisted metaswarm state

If this project uses metaswarm's context persistence, read and fold in whatever exists — do not duplicate it blindly, summarize and link:

bash
ls .beads/plans/active-plan.md            # the approved plan (if mid-execution)
ls .beads/context/project-context.md      # completed work units, patterns, tooling
ls .beads/context/execution-state.md      # current work unit, phase, retry count
bd prime --work-type recovery 2>/dev/null # reload plan + state + knowledge, if beads present

If an active plan exists, the handoff must point to it explicitly and state which work unit/phase is in progress.

Step 3 — Establish current status

Be honest and specific about state. Distinguish clearly between:

  • Done & verified — with evidence (tests passing, build green, commit SHAs).
  • Done but unverified — written but not yet tested/run.
  • In progress — the exact thing being worked on right now, and where it stopped.
  • Not started — remaining work.

Capture the working tree reality so the next agent is not surprised:

bash
git branch --show-current
git status --short
git log --oneline -10
git diff --stat

Note uncommitted changes, stashes, and whether the branch is pushed.

Step 4 — Identify required reading

List every artifact the next agent must read before acting, and for each one say why and what to look for. Categories to sweep:

  • Specs / Issues — the requirements source of truth (GitHub Issue, spec section, DoD).
  • Design docs — docs/plans/*-design.md and any approved design.
  • Plans — .beads/plans/active-plan.md, docs/plans/*-plan.md.
  • Code — the specific files and file:line anchors that are the focus of the work, plus any pattern files to imitate.
  • Tests — the tests that define correctness (failing tests are the spec under TDD).
  • Config / gates — .coverage-thresholds.json, CLAUDE.md rules, CI config that the change must satisfy.

Prefer precise pointers (src/foo.ts:120-145) over whole-file references. If something is short and load-bearing, quote it directly into the handoff so the next agent does not have to hunt.

Step 5 — Decide the single next action (YYY)

Pick the one most important, concrete next step. It must be:

  • Actionable — an imperative the agent can start immediately.
  • Specific — names files, functions, or DoD items.
  • Bounded — the immediate next move, not the entire remaining roadmap (the rest goes in the document's "Remaining work" section).

Step 6 — Write the document

Create docs/handoffs/handoff-<YYYY-MM-DD-HHmm>.md using the template below. Fill every section; write "None" where a section genuinely does not apply rather than deleting it.

Step 7 — Emit the handoff sentence

After writing the file, verify it (Step 8), then output the single sentence — exactly one line, the literal file path, and the concrete action. Do not add commentary after it.

Step 8 — Self-check before handing off

Confirm, as if you were the receiving agent who knows nothing:

  • Could I start work from this document alone, without the prior conversation?
  • Are all referenced files/paths/issues real and correct (spot-check with ls/git)?
  • Is the next action unambiguous and immediately startable?
  • Are decisions and their rationale captured so I won't undo them?
  • Are the quality gates (tests, coverage, lint, build commands) stated?
  • Does the closing sentence name the exact file and a concrete action?

Handoff Document Template

markdown
# Handoff: <short title of the work>

**Date**: <YYYY-MM-DD HH:mm> · **Branch**: `<branch>` · **Author session**: <model/agent>

## 1. Objective
<1–2 sentences: what we are trying to accomplish and why.>

## 2. Definition of Done
- [ ] <verifiable acceptance criterion>
- [ ] <…>

## 3. Current Status
**Done & verified:**
- <item> (evidence: <tests/commit>)

**Done, not yet verified:**
- <item>

**In progress (stopped here):**
- <the exact thing being worked on, and where/why it paused>

**Not started:**
- <remaining item>

### Working tree
- Branch `<branch>`, <pushed/not pushed>
- Uncommitted changes: <git status --short summary, or "clean">
- Recent commits:
  - `<sha>` <subject>

## 4. Required Reading (read these before acting)
| # | Path / reference | Why it matters | What to look for |
|---|---|---|---|
| 1 | `<path-or-issue>` | <reason> | <specific thing> |
| 2 | `<path:line>` | <reason> | <specific thing> |

## 5. Key Decisions & Rationale
- **<decision>** — <why; what alternatives were rejected and why>. Do not undo without reason.

## 6. Code Map
- `<file:line>` — <what lives here / its role in this task>
- Pattern to imitate: `<file>` — <why>

## 7. How to Verify
```bash
<test command>           # e.g., npm test
<coverage command>       # reads .coverage-thresholds.json
<lint/build command>
```
Expected: <what green looks like>.

## 8. Open Questions / Blockers
- <question needing the user, or external dependency>  — or "None"

## 9. Next Action
<The single concrete next step — the YYY — expanded with any detail the one-liner can't hold.>

## 10. Remaining Work (after the next action)
1. <subsequent step>
2. <…>

Anti-Patterns

  1. Vague next action — "continue where we left off" is useless. Name the file and the change.
  2. Assuming shared memory — the next agent has none. If it matters and isn't in the doc, it's lost.
  3. Dumping the transcript — synthesize. A 40-line oriented summary beats a 4,000-line paste.
  4. Stale pointers — verify paths and line numbers exist before citing them; code may have moved.
  5. Hiding uncommitted state — always disclose dirty working tree, stashes, and unpushed commits.
  6. Multiple "final" sentences — emit exactly one Read <file> and do <action>. line.
  7. Silent decision loss — if a choice was made and settled, record it with rationale so it isn't reopened.

Relationship to Other Metaswarm Pieces

  • Complements /prime (which loads knowledge into a session) by serializing context out of a session for the next one.
  • For mid-execution work, this skill should reference .beads/plans/active-plan.md and .beads/context/execution-state.md rather than restating them, so the next agent can bd prime --work-type recovery and then read the handoff for the human-readable narrative.
  • Run /self-reflect separately to capture durable learnings into the knowledge base; /handoff captures this task's transient state to resume it.

Frequently asked questions

What does the Handoff AI skill do?

Analyze the current session and write a self-contained handoff document so a fresh agent can resume the work with full context — outputs a single "Read XXX.md and do YYY." sentence

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/dsifry/metaswarm/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 dsifry 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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