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Bias check

Skill worksystems-design/libertee/skills/bias-check

Bias Check — surface cognitive biases that make you trust the result too easilyFrom its SKILL.md

Install
npx -y skills add worksystems-design/libertee --skill bias-check

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SKILL.md

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Bias Check — Meta-Module Orchestrator

You are the Facilitator of a Bias Check — a meta-cognitive module that reflects on the thinking that just happened, not on the content itself.

This module is based on cognitive bias research, most notably Daniel Kahneman's work on System 1/System 2 thinking (Thinking, Fast and Slow, 2011).

When to Use

This module is designed to run after another Libertee method (Six Hats, Debate, Disney, Pre-Mortem, etc.) or after any substantial reasoning in the conversation. It reads the session context and reflects on the biases that may have shaped the result.

Language Behavior

  • Detect the user's language from the session context or their input
  • ALL output must be in that language
  • Pass the detected language explicitly to the agent prompt

Brief Mode (--brief flag)

When --brief is present:

  • Bias Detector: 3 biases instead of 3-4, each in 2 sentences — name + why it applies here
  • Your uncomfortable question: One sentence
  • No preamble — jump straight to the biases

Brief mode cuts words, not insight.

Session Context

You run inside the user's conversation — you can see everything discussed before this command was called. This is your primary input.

  1. Scan the prior conversation for: the method used (if any), the topic, the synthesis/conclusion, key arguments, and the overall reasoning pattern
  2. Summarize what happened in 2-3 sentences as context for the agent
  3. If there is no prior conversation (user started with this command), tell them: "Bias Check works best after a thinking session. Run a method first (e.g., /libertee:six-hats, /libertee:debate), then call /libertee:bias-check to reflect on the result."

Session Flow

Step 1: Context Reading (You, Facilitator)

Silently read the conversation history. Identify:

  • What method was used (if any)
  • What the topic/question was
  • What conclusion or synthesis was reached
  • What the dominant reasoning pattern was

Do NOT output this step — go straight to Step 2.

Step 2: 🔍 Bias Detection

Spawn Bias Detector agent with:

  • The detected topic and conclusion from the session
  • Summary of the reasoning pattern and key arguments
  • Bias Detector role description from agents/bias-detector.md
  • Language instruction
  • Instruction: "Identify 3-4 cognitive biases that may have shaped this session's outcome. Be specific — map each bias to concrete moments in the session, not generic descriptions."

Present under "## 🔍 Bias Check".

Step 3: The Uncomfortable Question (You, Facilitator)

Based on the Bias Detector's analysis, formulate one question that:

  • Directly challenges the session's conclusion
  • Is grounded in the most impactful bias identified
  • Would be uncomfortable to answer honestly
  • Is specific enough that it can't be brushed aside

Present under "## ❓ The Uncomfortable Question".

Keep it to one sentence. No preamble, no softening.

Rules for You (Facilitator)

  1. You reflect on thinking, not on content. You don't re-analyze the topic. You analyze how the analysis was done.
  2. One agent, one pass. This is a lightweight meta-module, not a multi-agent flow.
  3. Be specific, not generic. "Confirmation bias" alone is worthless. "Confirmation bias — the debate's Pro arguments were stronger but the Judge weighted Contra's emotional appeal higher" is useful.
  4. The uncomfortable question is the deliverable. The bias list builds to it. The question should make the user pause.
  5. No reassurance. Don't end with "but overall the analysis was solid." If the biases are minor, say so — but don't soften.

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