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Epistemic Checkpoint

Community
jiaxiaojunQAQ
epistemic-checkpoint

Force verification before answering questions involving versions, dates, status, or "current" state. Prevents hallucinations at the REASONING level by checking assertions.yaml and WebSearch before forming beliefs. Triggers on software versions, release status, dates, and package versions.

Overview

PublisherjiaxiaojunQAQ
RepositorySkillJect
Skill nameepistemic-checkpoint
Stars
79
Forks
8
Bundled files
1
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 jiaxiaojunQAQ on GitHub. Read the source before you install it.

Installation

Install the Epistemic 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.
  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/jiaxiaojunQAQ/SkillJect.git /tmp/SkillJect
mkdir -p .claude/skills
cp -r /tmp/SkillJect/data/skills_sample/epistemic-checkpoint .claude/skills/epistemic-checkpoint
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Epistemic 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 Epistemic 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 Epistemic 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.

Epistemic Checkpoint

Force verification before answering questions involving versions, dates, status, or "current" state.

Purpose

Prevents the ROOT CAUSE of hallucinations - not just blocking wrong output, but preventing wrong REASONING. Claude's training data is stale; this skill forces verification before forming beliefs.

Triggers

Activate this skill when the question involves ANY of:

  • Software versions (.NET, Node, React, Python, etc.)
  • Release status (preview, LTS, GA, RC, deprecated)
  • "Current" or "latest" anything
  • Dates that might be after training cutoff
  • Package versions
  • API deprecations

MANDATORY Protocol

Step 1: Recognize Uncertainty

Say to yourself: "My training data may be stale for: [topic]"

Step 2: Check Local Ground Truth

text
Read ${CLAUDE_PLUGIN_ROOT}/blackboard/assertions.yaml
Search for relevant entries

If found - use that value with high confidence.

Step 3: If Not in Assertions - WebSearch

text
WebSearch("[software] [version] release date site:official-docs")
WebSearch("[software] LTS release 2025")

Prefer official sources:

  • microsoft.com/dotnet for .NET
  • nodejs.org for Node
  • python.org for Python

Step 4: State Verified Facts

Say: "Based on [source], [software] [version] is [status] as of [date]."

Step 5: THEN Proceed

Only now answer the actual question with the verified baseline.

Red Flag Thoughts (REJECT THESE)

If you think...Actually do...
"I'm pretty sure .NET 10 is..."WebSearch to verify
"This is probably still preview"Check assertions.yaml
"I remember this from training"Training is stale, verify
"This is a simple factual question"Simple facts are often WRONG
"The user said it's preview"User might be wrong too, verify

Anti-Pattern Examples

WRONG:

text
"If targeting .NET 10 preview, use C# 14 extension types..."

CORRECT:

text
"Let me verify .NET 10 status... [WebSearch] ...
.NET 10 is LTS (released November 2025), not preview.
Standard extension methods work fine."

WRONG:

text
"React 19 is still in beta, so..."

CORRECT:

text
"Checking React 19 status... [WebSearch] ...
React 19 was released December 2024 as stable.
Proceeding with React 19 stable patterns."

Common Danger Patterns

TopicDanger PatternLikely Truth
.NET"preview", "not released".NET 10 LTS since Nov 2025
React"beta", "experimental"React 19 stable since Dec 2024
Node"current" without versionAlways specify exact version
Python"3.x is latest"Verify - 3.13+ exists

Output Format

When this skill activates, structure your verification as:

text
Epistemic Checkpoint

Claim to verify: [what you were about to assume]
Source checked: [assertions.yaml / WebSearch / official docs]
Verified fact: [the actual truth]
Confidence: [high/medium/low]

Proceeding with verified baseline...

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 Epistemic Checkpoint AI skill do?

Force verification before answering questions involving versions, dates, status, or "current" state. Prevents hallucinations at the REASONING level by checking assertions.yaml and WebSearch before forming beliefs. Triggers on software versions, release status, dates, and package versions.

Why use Epistemic Checkpoint on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jiaxiaojunQAQ/SkillJect/tree/main/data/skills_sample/epistemic-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 Epistemic 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 Epistemic Checkpoint?

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

Is the Epistemic Checkpoint AI skill free?

It is published on GitHub by jiaxiaojunQAQ. 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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