Ambiguity Gate

SkillDev tools

Pre-routing ambiguity analysis — scores request clarity and asks clarifying questions when needed (inspired by ouroboros)

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Ambiguity Gate skill

What this skill tells your AI

The instructions your AI receives, as published by baekenough/oh-my-customcode in .claude/skills/ambiguity-gate/SKILL.md and read by ahel’s review.

Purpose

Analyze a user request for ambiguity before routing to implementation. Inspired by the ouroboros Socratic interviewer pattern, this skill measures request clarity on a 0.0–1.0 scale and asks targeted clarifying questions when needed.

Ambiguity Scoring

Score RangeVerdictAction
≤ 0.2ClearProceed with implementation
0.2–0.5ModerateSuggest clarifications but allow proceeding
> 0.5HighRequire clarification before proceeding

Scoring Factors

FactorWeightDescription
Scope clarity30%Is the scope of work well-defined?
Technical specificity25%Are technical requirements clear?
Acceptance criteria20%Can we determine when the task is done?
Constraint clarity15%Are constraints and limitations specified?
Context sufficiency10%Is there enough context to proceed?

Composite score = weighted sum of individual factor scores (each 0.0–1.0, inverted: 0.0 = clear, 1.0 = ambiguous).

Output Format

[Ambiguity Analysis]
├── Score: {0.0-1.0}
├── Verdict: {Clear | Moderate | High}
├── Breakdown:
│   ├── Scope: {score} — {reason}
│   ├── Technical: {score} — {reason}
│   ├── Acceptance: {score} — {reason}
│   ├── Constraints: {score} — {reason}
│   └── Context: {score} — {reason}
└── Suggestions: {clarifying questions if score > 0.2}

Workflow

  1. Receive the request to analyze (from $ARGUMENTS or conversation context)
  2. Score each factor independently
  3. Compute weighted composite score
  4. Determine verdict based on threshold
  5. If score > 0.2: generate targeted clarifying questions (max 3, prioritized by highest-weight ambiguous factors)
  6. If score > 0.5: do NOT proceed to implementation; present analysis and wait for clarification
  7. If score ≤ 0.2: output analysis and proceed

Clarifying Question Guidelines

  • Ask one question per ambiguous factor (max 3 total)
  • Order by factor weight (scope → technical → acceptance criteria)
  • Make questions specific and answerable
  • Avoid yes/no questions; prefer open-ended with examples

Example questions:

  • Scope: "Should this change affect all environments or only development?"
  • Technical: "What language/framework should this be implemented in?"
  • Acceptance: "What would a passing test look like for this feature?"
  • Constraints: "Are there performance or memory constraints to consider?"
  • Context: "Is this a new feature or modifying existing behavior?"

Integration

This skill can be:

  • Invoked manually: /ambiguity-gate [request] — analyze a specific request
  • Integrated into routing skills: Insert as a pre-check step before agent delegation when request complexity warrants it

Routing skill integration example:

1. Run ambiguity-gate on user request
2. If score > 0.5: surface questions, wait for response, re-run gate
3. If score ≤ 0.5: proceed with normal routing

When NOT to Use

Skip this skill for:

  • Simple, one-line questions ("What does X do?")
  • One-line fixes with clear scope ("Fix the typo in line 42")
  • Well-defined bug reports with reproduction steps and expected behavior
  • Requests with explicit acceptance criteria already stated
  • Follow-up requests that clarify a previous ambiguous request

Signals

GitHub stars
34
Forks
6
Last commit
Sep 2026
Advanced
Catalog kind
skill
Gateway key
ambiguity-gate
Source
github.com/baekenough/oh-my-customcode