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From thinking-frameworks-skills
Detects and removes cognitive biases from reasoning using Julia Galef's Scout Mindset framework. Provides reversal tests, scope sensitivity checks, status quo bias tests, confidence interval audits, and full bias audits.
How this skill is triggered — by the user, by Claude, or both
Slash command
/thinking-frameworks-skills:scout-mindset-bias-checkThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
- [Interactive Menu](#interactive-menu)
Core Principle: Map the territory accurately rather than defending a position. Forecasting requires intellectual honesty -- biases systematically distort probabilities, emotional attachment clouds judgment, and motivated reasoning leads to overconfidence.
What would you like to do?
1. Run the Reversal Test - Check if you'd accept opposite evidence
2. Check Scope Sensitivity - Ensure probabilities scale with inputs
3. Test Status Quo Bias - Challenge "no change" assumptions
4. Audit Confidence Intervals - Validate CI width
5. Run Full Bias Audit - Comprehensive bias scan
6. Learn the Framework - Deep dive into methodology
7. Exit - Return to main forecasting workflow
Check if you'd accept evidence pointing the opposite direction.
Reversal Test Progress:
- [ ] Step 1: State your current conclusion
- [ ] Step 2: Identify supporting evidence
- [ ] Step 3: Reverse the evidence
- [ ] Step 4: Ask "Would I still accept it?"
- [ ] Step 5: Adjust for double standards
What are you predicting?
List the evidence that supports your conclusion.
Example: Candidate A will win (75%)
Imagine the same evidence pointed the OTHER way.
Reversed: What if polls showed B ahead, B had more funding, experts favored B, and B had better ratings?
The Critical Question:
If this reversed evidence existed, would I accept it as valid and change my prediction?
Three possible answers:
A) YES - I would accept reversed evidence ✓ No bias detected, continue with current reasoning
B) NO - I would dismiss reversed evidence ⚠ Warning: Motivated reasoning - you're accepting evidence when it supports you, dismissing equivalent evidence when it doesn't (special pleading)
C) UNSURE - I'd need to think about it ⚠ Warning: Asymmetric evidence standards suggest rationalizing, not reasoning
If you answered B or C:
Ask: Why do I dismiss this evidence in one direction but accept it in the other? Is there an objective reason, or am I motivated by preference?
Common rationalizations:
The Fix:
Probability adjustment: If you detected double standards, move probability 10-15% toward 50%
Next: Return to menu
Ensure your probabilities scale appropriately with magnitude.
Scope Sensitivity Progress:
- [ ] Step 1: Identify the variable scale
- [ ] Step 2: Test linear scaling
- [ ] Step 3: Check reference point calibration
- [ ] Step 4: Validate magnitude assessment
- [ ] Step 5: Adjust for scope insensitivity
What dimension has magnitude?
The Linearity Test: Double the input, check if impact doubles.
Example: Startup funding
Scope sensitivity check: Did probabilities scale reasonably? If they barely changed → Scope insensitive
The Anchoring Test: Did you start with a number (base rate, someone else's forecast, round number) and insufficiently adjust?
The fix:
The "1 vs 10 vs 100" Test: For your forecast, vary the scale by 10×.
Example: Project timeline
Expected: Probability should change significantly. If all three estimates are within 10 percentage points → Scope insensitivity
The problem: Your emotional system responds to the category, not the magnitude.
The fix:
Method 1: Logarithmic scaling - Use log scale for intuition
Method 2: Reference class by scale - Don't use "startups" as reference class. Use "Startups that raised $1M" (10% success) vs "Startups that raised $100M" (60% success)
Method 3: Explicit calibration - Use a formula: P(success) = base_rate + k × log(amount)
Next: Return to menu
Challenge the assumption that "no change" is the default.
Status Quo Bias Progress:
- [ ] Step 1: Identify status quo prediction
- [ ] Step 2: Calculate energy to maintain status quo
- [ ] Step 3: Invert the default
- [ ] Step 4: Apply entropy principle
- [ ] Step 5: Adjust probabilities
Are you predicting "no change"? Examples: "This trend will continue," "Market share will stay the same," "Policy won't change"
Status quo predictions often get inflated probabilities because change feels risky.
The Entropy Principle: In the absence of active energy input, systems decay toward disorder.
Question: "What effort is required to keep things the same?"
Examples:
Mental Exercise:
Bias check: If P(change) + P(same) ≠ 100%, you have status quo bias.
Second Law of Thermodynamics (applied to forecasting):
Ask:
If you detected status quo bias:
For "no change" predictions that require high energy:
For predictions where inertia truly helps: No adjustment needed
The heuristic: If maintaining status quo requires active effort, decay is more likely than you think.
Next: Return to menu
Validate that your CI width reflects true uncertainty.
Confidence Interval Audit Progress:
- [ ] Step 1: State current CI
- [ ] Step 2: Run surprise test
- [ ] Step 3: Check historical calibration
- [ ] Step 4: Compare to reference class variance
- [ ] Step 5: Adjust CI width
Current confidence interval:
The Surprise Test: "Would I be genuinely shocked if the true value fell outside my confidence interval?"
Calibration:
Test: Imagine the outcome lands just below your lower bound or just above your upper bound.
Three possible answers:
Look at your past forecasts:
| CI Level | Expected Outside | Your Actual |
|---|---|---|
| 80% | 20% | ___% |
| 90% | 10% | ___% |
Diagnosis: Actual > Expected → CIs too narrow (overconfident) - Most common
If you have reference class data:
Example: Reference class SD = 12%, your 80% CI ≈ Point estimate ± 15%
If your CI is narrower than reference class variance, you're claiming to know more than average. Justify why, or widen CI.
Adjustment rules:
Next: Return to menu
Comprehensive scan of major cognitive biases.
Full Bias Audit Progress:
- [ ] Step 1: Confirmation bias check
- [ ] Step 2: Availability bias check
- [ ] Step 3: Anchoring bias check
- [ ] Step 4: Affect heuristic check
- [ ] Step 5: Overconfidence check
- [ ] Step 6: Attribution error check
- [ ] Step 7: Prioritize and remediate
See Cognitive Bias Catalog for detailed descriptions.
Quick audit questions:
If NO to any → Confirmation bias detected
If NO to any → Availability bias detected
If NO to any → Anchoring bias detected
If NO to any → Affect heuristic detected
If NO to any → Overconfidence detected
If NO to any → Attribution error detected
For each detected bias:
Remediation example:
| Bias | Severity | Direction | Adjustment |
|---|---|---|---|
| Confirmation | High | Up | -15% |
| Availability | Medium | Up | -10% |
| Affect heuristic | High | Up | -20% |
Net adjustment: -45% → Move probability down by 45 points (e.g., 80% → 35%)
Next: Return to menu
Deep dive into the methodology.
Next: Return to menu
Scout mindset is the drive to see things as they are, not as you wish them to be.
📁 resources/
Ready to start? Choose a number from the menu above.
npx claudepluginhub lyndonkl/claude --plugin thinking-frameworks-skillsSurfaces 3-4 cognitive biases in prior conversation reasoning or Libertee methods (Six Hats, Debate). Maps biases to session moments and poses one uncomfortable question challenging conclusions. Use after synthesis.
Diagnoses which cognitive biases are actively distorting thinking in a specific situation. Activates on queries about bias, objectivity, and decision clarity.
Stress-tests predictions by assuming failure and working backward to identify blind spots, tail risks, and overconfidence. Use when confidence is high (>80% or <20%) or to widen overconfident intervals.