Agent Handoff logo

Agent Handoff

Organization
UitbreidenOS
agent-handoff

Implement structured handoffs between agents with JSON-based state transfer, conflict detection, and acknowledgment protocols

Overview

PublisherUitbreidenOS
RepositoryUitKit
Skill nameagent-handoff
Stars
14
Forks
5
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 UitbreidenOS on GitHub. Read the source before you install it.

Installation

Install the Agent 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/UitbreidenOS/UitKit/tree/main/plugins/uitkit-ai-engineering/skills/agent-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/UitbreidenOS/UitKit.git /tmp/UitKit
mkdir -p .claude/skills
cp -r /tmp/UitKit/plugins/uitkit-ai-engineering/skills/agent-handoff .claude/skills/agent-handoff
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Agent 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 Agent 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 Agent 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.

Agent Handoff Skill

When to activate

  • Passing work from one agent to another in a multi-agent workflow
  • Implementing request/response cycles between agents in a pipeline
  • Building agent teams where one agent depends on another's output
  • Debugging why an agent receives incorrect or incomplete input from a prior agent
  • Ensuring state mutations are correctly propagated between agents

When NOT to use

  • Single-agent loops (no handoff needed)
  • Asynchronous event-driven workflows without explicit request/response
  • Workflows where agents do not directly depend on each other's outputs

Instructions

Handoff Packet Schema

A handoff is a JSON packet passed from one agent to the next:

json
{
  "handoff_id": "hof_xyz123",
  "from_agent": "researcher",
  "to_agent": "analyst",
  "timestamp": "2026-06-15T14:15:30Z",
  "session_id": "sess_abc789",
  "trace_id": "tr_xyz456",
  
  "phase_name": "research",
  "phase_status": "completed",
  
  "payload": {
    "sources": [
      {"title": "...", "url": "...", "credibility": "high", "summary": "..."},
      {"title": "...", "url": "...", "credibility": "medium", "summary": "..."}
    ],
    "research_summary": "Found 15 credible sources on quantum computing...",
    "research_duration_seconds": 900
  },
  
  "metadata": {
    "tokens_used": {"input": 2400, "output": 1850},
    "model": "claude-opus-4-20250514",
    "output_schema": {
      "type": "object",
      "properties": {
        "sources": {"type": "array"},
        "research_summary": {"type": "string"}
      }
    }
  },
  
  "constraints_for_next_agent": [
    "Do not contradict findings from sources with credibility = 'high'",
    "Budget max 30 minutes for analysis phase"
  ],
  
  "required_tools_for_next_agent": ["web_search", "fetch_url"],
  
  "next_steps": {
    "agent": "analyst",
    "action": "analyze_findings",
    "estimated_latency_seconds": 1800
  }
}

Validation Before Handoff

The sending agent must validate the payload before handoff:

python
def validate_handoff(handoff_packet, expected_schema):
    """
    Validate that the handoff packet matches the expected schema.
    Raises ValueError if validation fails.
    """
    # Check required fields
    required = ['handoff_id', 'from_agent', 'to_agent', 'payload', 'timestamp']
    for field in required:
        if field not in handoff_packet:
            raise ValueError(f"Missing required field: {field}")
    
    # Validate payload against schema
    import jsonschema
    jsonschema.validate(
        instance=handoff_packet['payload'],
        schema=expected_schema
    )
    
    # Validate timestamp is recent
    from datetime import datetime, timedelta
    ts = datetime.fromisoformat(handoff_packet['timestamp'].replace('Z', '+00:00'))
    if datetime.now(ts.tzinfo) - ts > timedelta(seconds=300):
        raise ValueError("Handoff timestamp is stale (> 5 minutes old)")
    
    return True

Do not proceed with handoff if validation fails. Log the error and escalate.

Sending a Handoff

python
def send_handoff(handoff_packet, to_agent_inbox='.claude/agent-inboxes/'):
    """
    Write handoff packet to the receiving agent's inbox.
    Return acknowledgment wait handle.
    """
    import json
    from pathlib import Path
    
    # Write to agent inbox
    inbox_dir = Path(to_agent_inbox) / handoff_packet['to_agent']
    inbox_dir.mkdir(parents=True, exist_ok=True)
    
    handoff_file = inbox_dir / f"{handoff_packet['handoff_id']}.json"
    with open(handoff_file, 'w') as f:
        json.dump(handoff_packet, f)
    
    # Return wait handle (wait for acknowledgment)
    return {
        'handoff_id': handoff_packet['handoff_id'],
        'acknowledgment_file': inbox_dir / f"{handoff_packet['handoff_id']}.ack"
    }

def wait_for_acknowledgment(wait_handle, timeout_seconds=30):
    """
    Poll for acknowledgment from the receiving agent.
    Timeout if no acknowledgment received.
    """
    from pathlib import Path
    from time import sleep
    from datetime import datetime, timedelta
    
    ack_file = Path(wait_handle['acknowledgment_file'])
    start = datetime.now()
    
    while datetime.now() - start < timedelta(seconds=timeout_seconds):
        if ack_file.exists():
            with open(ack_file, 'r') as f:
                import json
                ack = json.load(f)
            return ack
        sleep(0.5)
    
    raise TimeoutError(f"No acknowledgment after {timeout_seconds}s for {wait_handle['handoff_id']}")

Receiving a Handoff

python
def receive_handoff(from_agent, inbox_dir='.claude/agent-inboxes/'):
    """
    Poll for incoming handoffs from a specific agent.
    Return the first available handoff.
    """
    from pathlib import Path
    import json
    
    inbox = Path(inbox_dir) / from_agent
    
    # Find first unacknowledged handoff
    for handoff_file in sorted(inbox.glob('*.json')):
        ack_file = handoff_file.with_suffix('.ack')
        if not ack_file.exists():
            with open(handoff_file, 'r') as f:
                handoff = json.load(f)
            return handoff
    
    return None

def acknowledge_handoff(handoff_packet):
    """
    Send acknowledgment back to the sending agent.
    """
    import json
    from pathlib import Path
    
    ack = {
        'handoff_id': handoff_packet['handoff_id'],
        'acknowledged_by': 'analyst',
        'timestamp': datetime.utcnow().isoformat() + 'Z',
        'ready_to_proceed': True
    }
    
    ack_file = Path('.claude/agent-inboxes') / handoff_packet['from_agent'] / f"{handoff_packet['handoff_id']}.ack"
    with open(ack_file, 'w') as f:
        json.dump(ack, f)

Conflict Detection

When a handoff arrives, check for conflicts with previously received state:

python
def detect_handoff_conflict(new_handoff, previous_state):
    """
    Detect if the new handoff conflicts with previous state.
    Returns conflict details or None if no conflict.
    """
    if not previous_state:
        return None
    
    # Check if from_agent is the same (linear handoff)
    if new_handoff['from_agent'] != previous_state.get('last_handoff_from'):
        return {
            'type': 'source_mismatch',
            'last_agent': previous_state.get('last_handoff_from'),
            'new_agent': new_handoff['from_agent']
        }
    
    # Check for version mismatch (has data been modified since I received it?)
    if new_handoff.get('version', 1) < previous_state.get('last_handoff_version', 1):
        return {
            'type': 'version_downgrade',
            'previous_version': previous_state.get('last_handoff_version'),
            'new_version': new_handoff.get('version', 1)
        }
    
    return None

If a conflict is detected, escalate to supervisor before proceeding.

Example

python
# Researcher agent (sender)
def researcher_finalize():
    handoff = {
        'handoff_id': 'hof_r2a_001',
        'from_agent': 'researcher',
        'to_agent': 'analyst',
        'timestamp': '2026-06-15T14:15:30Z',
        'phase_name': 'research',
        'phase_status': 'completed',
        'payload': {
            'sources': [...],  # 15 sources
            'research_summary': '...'
        },
        'output_schema': {
            'type': 'object',
            'properties': {'sources': {'type': 'array'}, 'research_summary': {'type': 'string'}}
        },
        'constraints_for_next_agent': [
            'Do not ignore high-credibility sources',
            'Budget 30 min for analysis'
        ]
    }
    
    validate_handoff(handoff, handoff['output_schema'])
    wait_handle = send_handoff(handoff)
    ack = wait_for_acknowledgment(wait_handle)
    print(f"Handoff {handoff['handoff_id']} acknowledged at {ack['timestamp']}")

# Analyst agent (receiver)
def analyst_receive():
    handoff = receive_handoff('researcher')
    if handoff:
        print(f"Received handoff {handoff['handoff_id']} from {handoff['from_agent']}")
        print(f"Sources: {len(handoff['payload']['sources'])}")
        acknowledge_handoff(handoff)
        
        # Now proceed with analysis
        analyze(handoff['payload'])

Frequently asked questions

What does the Agent Handoff AI skill do?

Implement structured handoffs between agents with JSON-based state transfer, conflict detection, and acknowledgment protocols

Why use Agent Handoff on TypingMind?

Because you install it once and use it with any model. Agent 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 Agent Handoff in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/UitbreidenOS/UitKit/tree/main/plugins/uitkit-ai-engineering/skills/agent-handoff. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Agent 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 Agent Handoff?

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

Is the Agent Handoff AI skill free?

It is published on GitHub by UitbreidenOS. 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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