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Human Checkpoint

Community
WILLOSCAR
human-checkpoint

Review and record one pending human checkpoint in a research Workspace; use when a HUMAN Unit is blocked on an `Approve C*` decision, and never treat silence or artifact existence as approval.

Overview

PublisherWILLOSCAR
Repositoryresearch-units-pipeline-skills
Skill namehuman-checkpoint
Stars
513
Forks
39
Bundled files
1
Links
Skill sourceRepository
  • 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 WILLOSCAR 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/WILLOSCAR/research-units-pipeline-skills/tree/main/.codex/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/WILLOSCAR/research-units-pipeline-skills.git /tmp/research-units-pipeline-skills
mkdir -p .claude/skills
cp -r /tmp/research-units-pipeline-skills/.codex/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

A checkpoint is consent, not a formatting step. It binds a named human Decision to the exact Artifacts and constraints reviewed before execution may continue.

Inputs

  • DECISIONS.md.
  • UNITS.csv and STATUS.md for the active checkpoint.
  • Artifacts declared by the locked Pipeline for that checkpoint.

Outputs

  • Updated DECISIONS.md.
  • A checkpoint Decision in the Run ledger.

Steps

1. Identify the pending checkpoint

Inspect STATUS.md, UNITS.csv, and the active runner message. Select the first blocked HUMAN Unit and its C* identifier. If multiple checkpoints appear active or the checklist is missing, stop and repair the projection before approving anything.

Completion criterion: exactly one pending checkpoint and its owning HUMAN Unit are identified.

2. Review the declared Artifacts

Read the locked Pipeline's checkpoint contract and inspect every named Artifact. Record requested constraints or scope changes in the checkpoint block before approval; do not silently modify reader-facing content as part of sign-off.

Completion criterion: the reviewer can name the Artifacts inspected and any constraints attached to the Decision.

3. Record approval through the adapter

Use the Pipeline adapter so the Markdown checkbox and machine Decision ledger remain synchronized:

bash
uv run python scripts/pipeline.py approve \
  --workspace workspaces/<name> \
  --checkpoint <C*>

Do not infer approval from chat silence, a completed Artifact, or an existing but unchecked checklist item.

Completion criterion: DECISIONS.md contains [x] Approve C* and the Run ledger records checkpoint.approved for the same checkpoint.

4. Hand execution back to the Runner

Resume through the Pipeline adapter. The Harness may complete the HUMAN Unit and expose the next eligible Unit; this Skill does not execute downstream semantic work itself.

Completion criterion: the checkpoint is no longer the active blocker, or one new specific blocker is visible in durable Workspace state.

Context Pointers

  • The locked pipelines/*.pipeline.md owns checkpoint purpose and required review Artifacts.
  • DECISIONS.md is the human-readable Decision surface.
  • .harness/decisions.jsonl is the machine-readable history; update it through the adapter rather than by hand.
  • Use checkpoint-brief to recreate a post-route checkpoint review block.
  • scripts/run.py is a runner-compatibility helper that only toggles the Markdown checkbox. Prefer scripts/pipeline.py approve, which also records the machine Decision.

Troubleshooting

  • If the approvals checklist is missing after C0, materialize the review block with checkpoint-brief before approval. Use pipeline-router only for the initial C0 route.
  • If reviewed upstream Artifacts later change, expect the Harness to revoke the stale approval and request a new Decision.

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?

Review and record one pending human checkpoint in a research Workspace; use when a HUMAN Unit is blocked on an `Approve C*` decision, and never treat silence or artifact existence as approval.

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/WILLOSCAR/research-units-pipeline-skills/tree/main/.codex/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?

It is published on GitHub by WILLOSCAR. 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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