ai-skills/skills/systematic-enumeration/SKILL.md

name, description, version, user-invocable, disable-model-invocation

name	description	version	user-invocable	disable-model-invocation
Systematic Enumeration	Forces element-by-element verification for finite sets to prevent counting errors.	1.0	true	false

Systematic Enumeration & Verification Skill

Objective

To eliminate heuristic errors and "hallucinated patterns" when analyzing finite sets. This protocol overrides the model's tendency toward "holistic recognition" in favor of systematic, element-by-element verification.

Execution Protocol

When this skill is triggered, you MUST NOT provide a direct answer immediately. Follow these three phases to ensure accuracy:

Phase 1: Set Definition

Explicitly define the boundaries and members of the finite set being analyzed.

• Requirement: List the members before performing any tests.
• Example: "The set consists of the files in the /src directory: [main.rs, utils.rs, types.rs]."

Phase 2: Atomic Element Testing (O(n))

Iterate through every item in the set. For each item, perform a literal check against the target property.

• Format: Use a list or table to force token-level focus on each element.
• Structure: [Item] -> [Logic/Observation] -> [Boolean Result]
• Note: For character-based tests, split the string into individual characters to bypass tokenization bias.

Phase 3: Reduction & Summation

Aggregate the True results from Phase 2 to derive the final answer.

• Self-Correction: Verify that the count of items tested in Phase 2 exactly matches the count of the set defined in Phase 1. If there is a mismatch, restart Phase 2.

Constraints & Anti-Patterns

• STRICT BAN on Heuristics: Do not use phrases like "typically," "usually," or "it appears that."
• NO Pattern Matching: Do not extrapolate a rule (e.g., "every other item") as a substitute for testing every item.
• Computational Justification: Treat the process as an O(n) operation where n is small enough that accuracy is the only priority.

Trigger Scenarios

• Counting specific characters or substrings within a string.
• Verifying property adherence across a list of variables, files, or objects.
• Membership testing in sets where false negatives are high-risk.