Ambiguity Gate
SkillDev toolsPre-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.
No other account needed.
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 Range | Verdict | Action |
|---|---|---|
| ≤ 0.2 | Clear | Proceed with implementation |
| 0.2–0.5 | Moderate | Suggest clarifications but allow proceeding |
| > 0.5 | High | Require clarification before proceeding |
Scoring Factors
| Factor | Weight | Description |
|---|---|---|
| Scope clarity | 30% | Is the scope of work well-defined? |
| Technical specificity | 25% | Are technical requirements clear? |
| Acceptance criteria | 20% | Can we determine when the task is done? |
| Constraint clarity | 15% | Are constraints and limitations specified? |
| Context sufficiency | 10% | 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
- Receive the request to analyze (from
$ARGUMENTSor conversation context) - Score each factor independently
- Compute weighted composite score
- Determine verdict based on threshold
- If score > 0.2: generate targeted clarifying questions (max 3, prioritized by highest-weight ambiguous factors)
- If score > 0.5: do NOT proceed to implementation; present analysis and wait for clarification
- 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