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Epistemic Challenge

Community
AnastasiyaW
epistemic-challenge

Use this skill when the user asks the agent not to agree automatically, to challenge an assumption, evaluate a proposal critically, identify counterevidence, or make a high-consequence decision under uncertainty. Separates facts, inference, counterevidence, uncertainty, and a falsifier; do not use for simple instructions, direct observations, or user-owned preferences.

Overview

PublisherAnastasiyaW
Repositorycodex-claude-code-config
Skill nameepistemic-challenge
Stars
212
Forks
32
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Epistemic Challenge 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/AnastasiyaW/codex-claude-code-config/tree/main/skills/operational/epistemic-challenge
  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/AnastasiyaW/codex-claude-code-config.git /tmp/codex-claude-code-config
mkdir -p .claude/skills
cp -r /tmp/codex-claude-code-config/skills/operational/epistemic-challenge .claude/skills/epistemic-challenge
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Epistemic Challenge 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 Challenge 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 Challenge 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 Challenge

Purpose

Give evidence-bound disagreement when it is warranted, and evidence-bound agreement when it is warranted. The goal is not a "devil's advocate" persona: invented opposition is as misleading as automatic agreement.

When to use

Use for an explicit request for critical independence, a factual premise that would change an implementation or decision, a disputed conclusion, research, or an important recommendation. Do not apply the full protocol to a simple instruction, a preference the user owns, or a fact directly measured in the current tool result.

Procedure

  1. State the operative claim or decision in a falsifiable form. Separate a user preference (which needs no fact-check) from an empirical claim (which does).
  2. Collect source-backed evidence before giving a verdict. First prefer a current local observation (code, log, probe) or primary documentation; an explicit user constraint is evidence for a value choice. Memory and prior assistant text are only search leads and must be re-checked before they support a current factual claim. User confidence and a pleasing narrative are not evidence.
  3. Name the strongest realistic counter-hypothesis and the observation that distinguishes it from the proposed explanation. Do not create a weak counterargument merely to sound critical.
  4. For research or a consequential decision, verify the discriminator without showing the checker the proposed conclusion when practical. Prefer a fresh reviewer for destructive, production, financial, security, or architectural actions.
  5. Return one of: SUPPORTED, REFUTED, INCONCLUSIVE, or VALUE_CHOICE. Say what would change the verdict. Change a conclusion only for new evidence or a corrected inference, not because the user repeats a preference or asks "are you sure?".

Output contract

For a substantive claim, use this compact shape:

markdown
Verdict: SUPPORTED | REFUTED | INCONCLUSIVE | VALUE_CHOICE
Evidence: [observed source or command result]
Counterevidence / alternative: [strongest live alternative, or none found]
Boundary: [what was not established]
Next falsifier: [one observation that would change the verdict]

When agreement is supported, say so plainly. Do not start with praise, validation, or agreement before the evidence.

Delegated review

Give the reviewer artifacts and verification commands, not the generator's conclusion. The reviewer must attempt to refute the claim first and return a durable PROCEED, HOLD, or REJECT verdict with the decisive evidence.

Gotchas

  • "Be more critical" alone does not make an answer true. The necessary unit is a discriminating observation, not a more forceful tone.
  • A response may legitimately agree. Penalize unsupported agreement, not agreement itself.
  • A model's confidence and a user challenge are not a substitute for source evidence. If no discriminator is accessible, keep the result INCONCLUSIVE.
  • Do not expose a long private reasoning trace. Report evidence, assumptions, uncertainty, and the actionable check.

Troubleshooting

SymptomCauseFix
Every answer becomes oppositionalThe protocol was treated as a personaRequire a real alternative and a discriminating observation; otherwise state agreement or uncertainty.
Agent changes a correct conclusion after "are you sure?"Social pressure was accepted as evidenceRe-run the named verification or retain the verdict and name the missing evidence.
Reviewer agrees with the author without testingReviewer saw a persuasive conclusion instead of a testable artifactSend only the artifact, current-state anchors, and the question to disprove.

Evidence basis

  • Chain-of-Verification uses independent verification questions to reduce hallucinations.
  • OpenAI's sycophancy postmortem shows that positive user feedback and narrow offline tests did not catch over-agreeable behaviour; behavior-specific evaluations are required.
  • Sharma et al. find that preference signals can favour convincing agreement over truth.

Frequently asked questions

What does the Epistemic Challenge AI skill do?

Use this skill when the user asks the agent not to agree automatically, to challenge an assumption, evaluate a proposal critically, identify counterevidence, or make a high-consequence decision under uncertainty. Separates facts, inference, counterevidence, uncertainty, and a falsifier; do not use for simple instructions, direct observations, or user-owned preferences.

Why use Epistemic Challenge on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/AnastasiyaW/codex-claude-code-config/tree/main/skills/operational/epistemic-challenge. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Epistemic Challenge?

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 Challenge?

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

Is the Epistemic Challenge AI skill free?

Yes. It is published on GitHub by AnastasiyaW 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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