Session Handoff logo

Session Handoff

OrganizationPopular
softaworks
session-handoff

Creates comprehensive handoff documents for seamless AI agent session transfers. Triggered when: (1) user requests handoff/memory/context save, (2) context window approaches capacity, (3) major task milestone completed, (4) work session ending, (5) user says 'save state', 'create handoff', 'I need to pause', 'context is getting full', (6) resuming work with 'load handoff', 'resume from', 'continue where we left off'. Proactively suggests handoffs after substantial work (multiple file edits, complex debugging, architecture decisions). Solves long-running agent context exhaustion by enabling fresh agents to continue with zero ambiguity.

Overview

Publishersoftaworks
Repositoryagent-toolkit
Skill namesession-handoff
Stars
2.5K
Forks
226
Bundled files
10
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.

  • 10 bundled files

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

  • Open source

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

Installation

Install the Session 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/softaworks/agent-toolkit.git /tmp/agent-toolkit
mkdir -p .claude/skills
cp -r /tmp/agent-toolkit/skills/session-handoff .claude/skills/session-handoff
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Session 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 Session 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 Session 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

Creates comprehensive handoff documents that enable fresh AI agents to seamlessly continue work with zero ambiguity. Solves the long-running agent context exhaustion problem.

Mode Selection

Determine which mode applies:

Creating a handoff? User wants to save current state, pause work, or context is getting full.

  • Follow: CREATE Workflow below

Resuming from a handoff? User wants to continue previous work, load context, or mentions an existing handoff.

  • Follow: RESUME Workflow below

Proactive suggestion? After substantial work (5+ file edits, complex debugging, major decisions), suggest:

"We've made significant progress. Consider creating a handoff document to preserve this context for future sessions. Say 'create handoff' when ready."

CREATE Workflow

Step 1: Generate Scaffold

Run the smart scaffold script to create a pre-filled handoff document:

bash
python scripts/create_handoff.py [task-slug]

Example: python scripts/create_handoff.py implementing-user-auth

For continuation handoffs (linking to previous work):

bash
python scripts/create_handoff.py "auth-part-2" --continues-from 2024-01-15-auth.md

The script will:

  • Create .claude/handoffs/ directory if needed
  • Generate timestamped filename
  • Pre-fill: timestamp, project path, git branch, recent commits, modified files
  • Add handoff chain links if continuing from previous
  • Output file path for editing

Step 2: Complete the Handoff Document

Open the generated file and fill in all [TODO: ...] sections. Prioritize these sections:

  1. Current State Summary - What's happening right now
  2. Important Context - Critical info the next agent MUST know
  3. Immediate Next Steps - Clear, actionable first steps
  4. Decisions Made - Choices with rationale (not just outcomes)

Use the template structure in references/handoff-template.md for guidance.

Step 3: Validate the Handoff

Run the validation script to check completeness and security:

bash
python scripts/validate_handoff.py <handoff-file>

The validator checks:

  • No [TODO: ...] placeholders remaining
  • Required sections present and populated
  • No potential secrets detected (API keys, passwords, tokens)
  • Referenced files exist
  • Quality score (0-100)

Do not finalize a handoff with secrets detected or score below 70.

Step 4: Confirm Handoff

Report to user:

  • Handoff file location
  • Validation score and any warnings
  • Summary of captured context
  • First action item for next session

RESUME Workflow

Step 1: Find Available Handoffs

List handoffs in the current project:

bash
python scripts/list_handoffs.py

This shows all handoffs with dates, titles, and completion status.

Step 2: Check Staleness

Before loading, check how current the handoff is:

bash
python scripts/check_staleness.py <handoff-file>

Staleness levels:

  • FRESH: Safe to resume - minimal changes since handoff
  • SLIGHTLY_STALE: Review changes, then resume
  • STALE: Verify context carefully before resuming
  • VERY_STALE: Consider creating a fresh handoff

The script checks:

  • Time since handoff was created
  • Git commits since handoff
  • Files changed since handoff
  • Branch divergence
  • Missing referenced files

Step 3: Load the Handoff

Read the relevant handoff document completely before taking any action.

If handoff is part of a chain (has "Continues from" link), also read the linked previous handoff for full context.

Step 4: Verify Context

Follow the checklist in references/resume-checklist.md:

  1. Verify project directory and git branch match
  2. Check if blockers have been resolved
  3. Validate assumptions still hold
  4. Review modified files for conflicts
  5. Check environment state

Step 5: Begin Work

Start with "Immediate Next Steps" item #1 from the handoff document.

Reference these sections as you work:

  • "Critical Files" for important locations
  • "Key Patterns Discovered" for conventions to follow
  • "Potential Gotchas" to avoid known issues

Step 6: Update or Chain Handoffs

As you work:

  • Mark completed items in "Pending Work"
  • Add new discoveries to relevant sections
  • For long sessions: create a new handoff with --continues-from to chain them

Handoff Chaining

For long-running projects, chain handoffs together to maintain context lineage:

handoff-1.md (initial work)
handoff-2.md --continues-from handoff-1.md
handoff-3.md --continues-from handoff-2.md

Each handoff in the chain:

  • Links to its predecessor
  • Can mark older handoffs as superseded
  • Provides context breadcrumbs for new agents

When resuming from a chain, read the most recent handoff first, then reference predecessors as needed.

Storage Location

Handoffs are stored in: .claude/handoffs/

Naming convention: YYYY-MM-DD-HHMMSS-[slug].md

Example: 2024-01-15-143022-implementing-auth.md

Resources

scripts/

ScriptPurpose
create_handoff.py [slug] [--continues-from <file>]Generate new handoff with smart scaffolding
list_handoffs.py [path]List available handoffs in a project
validate_handoff.py <file>Check completeness, quality, and security
check_staleness.py <file>Assess if handoff context is still current

references/

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 Session Handoff AI skill do?

Creates comprehensive handoff documents for seamless AI agent session transfers. Triggered when: (1) user requests handoff/memory/context save, (2) context window approaches capacity, (3) major task milestone completed, (4) work session ending, (5) user says 'save state', 'create handoff', 'I need to pause', 'context is getting full', (6) resuming work with 'load handoff', 'resume from', 'continue where we left off'. Proactively suggests handoffs after substantial work (multiple file edits, complex debugging, architecture decisions). Solves long-running agent context exhaustion by enabling...

Why use Session Handoff on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/softaworks/agent-toolkit/tree/main/skills/session-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 Session 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 Session Handoff?

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

Is the Session Handoff AI skill free?

Yes. It is published on GitHub by softaworks 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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