Human Checkpoint logo

Human Checkpoint

Organization
TenureAI
human-checkpoint

Escalate critical decisions to a human with options, tradeoffs, and recommendation. TRIGGER when: major safety risk, high irreversible impact, large GPU spend, destructive data ops, shared-memory publication, or hard blockers requiring human approval. DO NOT TRIGGER when: routine confirmations handled by run-governor, or low-stakes reversible actions.

Overview

PublisherTenureAI
RepositoryPhD-Zero
Skill namehuman-checkpoint
Stars
53
Forks
4
Bundled files
1
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.

  • 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 TenureAI on GitHub. Read the source before you install it.

Installation

Install the Human Checkpoint 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/TenureAI/PhD-Zero/tree/main/.agents/skills/human-checkpoint
  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/TenureAI/PhD-Zero.git /tmp/PhD-Zero
mkdir -p .claude/skills
cp -r /tmp/PhD-Zero/.agents/skills/human-checkpoint .claude/skills/human-checkpoint
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Human Checkpoint 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 Human Checkpoint 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 Human Checkpoint 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.

Human Checkpoint

Mission

Request human decisions only at meaningful risk or decision points, using executable options.

Interaction Channel Policy

For checkpoints that require user input, choose interaction channel in this order:

  1. In moderate or detailed, prefer built-in user-question tool (request_user_input) for all required user interactions.
  2. If the built-in tool is unavailable in the current runtime, degrade to concise plain-text questions in chat.
  3. In full-auto, avoid non-essential questions and trigger checkpoints only by mode policy.
  4. Never silently skip a required checkpoint because the preferred channel is unavailable.

Mode-Aware Trigger Policy

  1. full-auto
    • Trigger only for hard blockers or major safety risks.
  2. moderate
    • Trigger for plan-finalization confirmations.
    • Trigger before high-resource actions.
    • Trigger for major safety risks.
    • Trigger for required intake clarification and parameter confirmations.
    • Trigger for replan confirmations when route changes materially.
  3. detailed
    • Trigger when path is unclear.
    • Trigger before high-resource actions.
    • Trigger for major safety risks.
    • Trigger for required intake clarification and parameter confirmations.
    • Trigger for replan confirmations when route changes materially.

Always trigger for shared-memory publication.

Major Safety Risk Examples

Treat these as major risks:

  1. potential large-scale file deletion
  2. likely secret leakage
  3. destructive remote/shared resource mutation
  4. actions with clear financial or operational damage potential

In all modes, checkpoint is required for major risks.

Request Format

Use this structure:

  1. Decision Needed
  2. Context
  3. Options
  4. Recommendation
  5. If Approved
  6. If Rejected

When using request_user_input, map the same structure into 1-3 short option-based questions. When using plain-text fallback, ask one concise direct question at a time.

Required Option Set for Risky Actions

Provide these options unless user already gave explicit policy:

  1. allow once
  2. allow same action type for this run
  3. disallow and stop
  4. user-defined handling

If option 2 is chosen, record it in run policy for this run only.

Option Design Rules

  1. Keep options executable now.
  2. State tradeoffs in cost, risk, and learning value.
  3. Put recommended option first when appropriate.
  4. Keep request short and clear.

After Decision

  1. Execute only approved behavior.
  2. Record rationale and outcome in memory/logs.
  3. Re-check if another checkpoint is needed before next high-impact action.
  4. Record interaction_channel=request_user_input|plain-text-fallback.
  5. If fallback is used, record fallback_reason.

Failure Handling

If no human response is available:

  1. In full-auto, continue with safest reversible path unless major safety risk remains unresolved.
  2. In moderate or detailed, use plain-text fallback if built-in question tool is unavailable.
  3. In moderate or detailed, remain blocked until user response for required checkpoints.

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 Human Checkpoint AI skill do?

Escalate critical decisions to a human with options, tradeoffs, and recommendation. TRIGGER when: major safety risk, high irreversible impact, large GPU spend, destructive data ops, shared-memory publication, or hard blockers requiring human approval. DO NOT TRIGGER when: routine confirmations handled by run-governor, or low-stakes reversible actions.

Why use Human Checkpoint on TypingMind?

Because you install it once and use it with any model. Human Checkpoint 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 Human Checkpoint in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/TenureAI/PhD-Zero/tree/main/.agents/skills/human-checkpoint. 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 Human Checkpoint?

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 Human Checkpoint?

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

Is the Human Checkpoint AI skill free?

Yes. It is published on GitHub by TenureAI 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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