enumeration-protocol-execution
Enforce a Divergent-Convergent Scan loop to overcome 'Prevalent Noun Bias' and statistical probability reflexes (System 1).
When & Why to Use This Skill
The Enumeration Protocol Execution skill is a sophisticated cognitive framework designed to enhance AI reasoning by implementing a 'Divergent-Convergent Scan' loop. It acts as a 'Cognitive Brake,' forcing the model to bypass 'Prevalent Noun Bias' and standard statistical reflexes (System 1 thinking). By mandating a survey of low-probability candidates and an inversion test, it ensures high-precision outputs in complex problem-solving scenarios where the most obvious answer might be a decoy or a result of probabilistic bias.
Use Cases
- Complex Debugging: Identifying non-obvious root causes in code blocks by bypassing standard library assumptions and 'obvious' syntax errors.
- Visual and Logical Puzzles: Solving intricate puzzles where the most salient visual elements are designed to distract from the actual solution.
- Error Log Analysis: Systematically scanning system logs for subtle, low-frequency anomalies that might be overlooked by standard diagnostic patterns.
- High-Stakes Decision Support: Evaluating multiple strategic options by intentionally challenging the 'statistical default' to avoid model bias and ensure contextual fit.
| name | enumeration-protocol-execution |
|---|---|
| description | Enforce a Divergent-Convergent Scan loop to overcome 'Prevalent Noun Bias' and statistical probability reflexes (System 1). |
| version | 1.0.0 |
Description
This protocol serves as a "Cognitive Brake." It is invoked when high precision is required or when the initial answer seems "too obvious" (high probability/low compute). It forces the agent to suspend the final answer, scan the entire search space for low-probability candidates, and perform an inversion test before converging on a selection.
Instructions
Step 1: Divergent Scan (The Silent Survey)
Before formulating the final response, generate an internal list of 3-5 distinct candidates that fit the user's criteria.
- Constraint: You are FORBIDDEN from selecting the first candidate that comes to mind.
- Search Target: Look for "Background Objects," "Structural Elements," or "Counter-Intuitive Solutions."
Step 2: Bias Identification
Review the generated list and identify the "Statistical Default."
- Question: "Which of these candidates would an average human or standard model pick 90% of the time?"
- Action: Flag this candidate as
[BIAS_DEFAULT].
Step 3: The Inversion Test
Challenge the [BIAS_DEFAULT].
- Question: "Why might this obvious answer be a decoy or incorrect?"
- Action: Check for exclusion criteria (e.g., user said 'Nope', context implies a trick, visual obstruction).
Step 4: Convergence & Selection
Select the final answer based on Contextual Fit rather than Saliency.
- If the
[BIAS_DEFAULT]survives the Inversion Test, output it. - If it fails, promote the highest-ranked alternative (e.g., the 'Dolly' instead of the 'Hat').
Examples
- "Engage enumeration protocol for this visual puzzle."
- "Execute enumeration scan to debug this code block (avoiding standard library assumptions)."
- "Run enumeration-protocol-execution on the error logs."