Agent skill

Ambiguity Detector

by ArabelaTso in ArabelaTso/Skills-4-SE

Detects and analyzes ambiguous language in software requirements and user stories.

Apache-2.0Auto-check passedProduct & Project Management

Install Ambiguity Detector

skills CLI
$ npx skills add ArabelaTso/Skills-4-SE --skill ambiguity-detector -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install ArabelaTso/Skills-4-SE ambiguity-detector --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/ArabelaTso/Skills-4-SE.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/ambiguity-detector .claude/skills/ambiguity-detector && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
ambiguity-detector
GitHub stars
253
Token cost
~2.5k tokens
SKILL.md length
882 words
Files
4 (incl. references, assets)
Skills in repo
170
Repo updated
First seen
Licence
Apache-2.0

At a glance

Detects and analyzes ambiguous language in software requirements and user stories.

  • Works in 6 steps: Read Requirements Thoroughly → Scan for Ambiguity Patterns → Classify Severity → …
  • Reviewing requirements documents
  • SKILL.md covers Core Capabilities, Analysis Workflow, Output Formats and Best Practices, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Ambiguity Detector is an agent skill from ArabelaTso/Skills-4-SE. Detects and analyzes ambiguous language in software requirements and user stories. Use when reviewing requirements documents, user stories, specifications, or any software requirement text to identify vague quantifiers, unclear scope, undefined terms, missing edge cases, subjective language, and incomplete specifications. Provides detailed analysis with clarifying questions and suggested improvements.

Its SKILL.md is about 2.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including reference files and assets (for example `assets/report_template.json`, `references/ambiguity_patterns.md` and `references/question_templates.md`).

It sits in Product & Project Management, covering Requirements gathering, User stories and PRD writing. The repository describes itself as: A curated list of 180+ useful Claude Skills for Software Engineering and resources for customizing AI for SE workflows. The licence is Apache-2.0.

When your agent uses it

  • Reviewing requirements documents
  • Any software requirement text to identify vague quantifiers
  • Undefined terms
  • Missing edge cases

Example prompts

  • “Use the ambiguity-detector skill to detect and analyzes ambiguous language in software requirements and user stories”
  • “/ambiguity-detector”

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Read Requirements Thoroughly
  2. Scan for Ambiguity Patterns
  3. Classify Severity
  4. Generate Clarifying Questions
  5. Provide Alternative Phrasings
  6. Create Analysis Report

What it can do on your machine

Read from SKILL.md and the folder at commit 4f38503. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Ambiguity Detector loads about 2.5k tokens when it runs, and up to ~8.4k if it reads all its reference files. Until then it costs about 106 tokens; SKILL.md has 882 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~106
When it runs · the whole SKILL.md, loaded when a task matches
~2.5k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~8.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from ArabelaTso/Skills-4-SE at commit 4f38503, republished under its Apache-2.0 licence (© ArabelaTso). 882 words, ~2,453 tokens.

Download SKILL.mdSave it as .claude/skills/ambiguity-detector/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
ambiguity-detector
description
Detects and analyzes ambiguous language in software requirements and user stories. Use when reviewing requirements documents, user stories, specifications, or any software requirement text to identify vague quantifiers, unclear scope, undefined terms, missing edge cases, subjective language, and incomplete specifications. Provides detailed analysis with clarifying questions and suggested improvements.

Ambiguity Detection in Software Requirements

You are an expert requirements analyst who identifies and resolves ambiguities in software requirements.

Core Capabilities

This skill enables you to:

  1. Detect ambiguity patterns - Identify 10 categories of ambiguous language
  2. Assess severity - Classify ambiguities by impact (critical, high, medium, low)
  3. Generate clarifying questions - Produce targeted questions to resolve ambiguities
  4. Suggest improvements - Provide clear, testable alternatives
  5. Create reports - Generate structured analysis with actionable recommendations

Analysis Workflow

Follow this process when analyzing requirements for ambiguity:

Step 1: Read Requirements Thoroughly

Read each requirement statement and identify:

  • Core intent and purpose
  • Actors and entities involved
  • Actions and behaviors described
  • Constraints and conditions
  • Success criteria
Step 2: Scan for Ambiguity Patterns

Use references/ambiguity_patterns.md to systematically check for:

1. Vague Quantifiers

  • Words: "many", "few", "some", "several", "enough", "reasonable"
  • Flag: Any imprecise quantity without numeric specification

2. Temporal Ambiguity

  • Words: "soon", "quickly", "frequently", "immediately", "real-time"
  • Flag: Timing without specific duration or frequency

3. Unclear Scope

  • Words: "all", "the system", "etc.", "relevant", "applicable"
  • Flag: Undefined boundaries or extent

4. Weak Verbs/Conditionals

  • Words: "might", "could", "should", "if possible", "where appropriate"
  • Flag: Unclear obligation level or unspecified conditions

5. Undefined Terms

  • Acronyms, jargon, technical terms without definition
  • Flag: Domain-specific terminology that needs clarification

6. Incomplete Specifications

  • Missing error handling, validation rules, constraints
  • Flag: No mention of edge cases or failure scenarios

7. Subjective Language

  • Words: "user-friendly", "fast", "modern", "intuitive", "clean"
  • Flag: Qualitative descriptions without measurable criteria

8. Implicit Assumptions

  • Words: "obviously", "of course", "as usual", "standard"
  • Flag: Unstated dependencies or assumed context

9. Unclear References

  • Words: "it", "this", "that", "they", "same"
  • Flag: Pronouns with ambiguous antecedents

10. Missing Edge Cases

  • No specification for null, empty, boundary, or error conditions
  • Flag: Only happy path described
Step 3: Classify Severity

For each ambiguity detected, assign severity:

Critical:

  • Blocks core functionality understanding
  • Multiple conflicting interpretations possible
  • High risk of building wrong thing
  • Example: "The API should be fast" (no performance target)

High:

  • Missing important implementation details
  • Likely to cause rework if not clarified
  • Affects multiple components
  • Example: "Validate user input" (which fields? what rules?)

Medium:

  • Non-critical features unclear
  • Minor edge cases not covered
  • Could cause minor issues
  • Example: "Display a few recent items" (how many?)

Low:

  • Nice-to-have clarifications
  • Minimal impact on implementation
  • Stylistic improvements
  • Example: "Use modern design" (can infer from context)
Step 4: Generate Clarifying Questions

Use references/question_templates.md to formulate questions:

Question Structure:

**Requirement:** [Original text]

**Ambiguity:** [What is unclear]

**Questions:**
1. [Specific question with options]
2. [Follow-up question]
3. [Edge case question]

**Suggested Clarification:** [Proposed clear version]

Example:

**Requirement:** "The system should handle many concurrent users"

**Ambiguity:** Vague quantifier - "many" is not defined

**Questions:**
1. How many concurrent users should the system support?
   - Options: 100 | 1,000 | 10,000 | Other: ___
2. What is the expected peak load during business hours?
3. What should happen when the user limit is exceeded?
   - Queue requests? Display error? Throttle?

**Suggested Clarification:**
"The system must support at least 1,000 concurrent users with response time under 2 seconds for 95% of requests. When capacity is exceeded, new requests should be queued for up to 30 seconds before returning a 'Service busy, please retry' error."
Step 5: Provide Alternative Phrasings

For each ambiguous requirement, suggest 2-3 clear alternatives:

Original: "Users should be able to upload files"

Clear Alternatives:

Option A (Specific): "Users must be able to upload PDF, DOCX, and image files (JPG, PNG) up to 10MB each. Files are stored in AWS S3 bucket 'user-uploads'. Display error 'File too large' if size exceeds 10MB, 'Invalid file type' if format is not supported."

Option B (More Permissive): "Users must be able to upload files up to 25MB in any common format (documents, images, videos, archives). Files are scanned for viruses before storage. Rejected files display specific error messages."

Option C (Minimal): "Users must be able to upload PDF files up to 5MB. Display 'Upload failed: [reason]' if validation fails."

Step 6: Create Analysis Report

Structure findings clearly:

markdown
# Ambiguity Analysis Report

## Summary
- Requirements Analyzed: 15
- Ambiguities Found: 8
- Critical: 2
- High: 3
- Medium: 2
- Low: 1

## Critical Ambiguities

### AMB-001: Undefined Performance Target
**Requirement ID:** REQ-003
**Original:** "The API should respond quickly"
**Issue:** No response time target specified
**Impact:** Cannot design for performance or test success
**Questions:**
1. What is the maximum acceptable API response time?
2. Should this be measured as average, median, or 95th percentile?
3. What happens if response time exceeds the target?
**Suggested Fix:**
"The API must respond within 500ms for 95% of requests. Requests exceeding 2 seconds should timeout with error code 408."

---

## High Ambiguities

[Continue for each ambiguity...]

## Recommendations

1. **Immediate Action Required:**
   - Clarify REQ-003 (performance target) before architecture decisions
   - Define REQ-007 (user roles) before implementing access control

2. **High Priority:**
   - Specify file upload constraints (REQ-002)
   - Define validation rules (REQ-005)

3. **Medium Priority:**
   - Clarify display quantities (REQ-009)
   - Define "recent" timeframe (REQ-011)
Show full SKILL.md (357 more words)Show less

Output Formats

Provide analysis in requested format:

Markdown Report (default) - Human-readable analysis document JSON Structure - Use assets/report_template.json for programmatic processing Inline Annotations - Comments added directly to requirements document Summary Table - Quick overview of all ambiguities

When format not specified, provide Markdown report.

Best Practices

  1. Be specific - Point to exact words/phrases that are ambiguous
  2. Explain impact - Clarify why the ambiguity matters
  3. Provide options - Suggest multiple clear alternatives when possible
  4. Prioritize - Focus on critical ambiguities first
  5. Ask good questions - Make questions specific and actionable
  6. Avoid pedantry - Flag genuine ambiguities, not stylistic preferences
  7. Consider context - Some terms are clear within project context
  8. Be constructive - Frame as improvement opportunities, not criticism

Common Pitfalls to Avoid

Don't flag as ambiguous when:

  • Term is well-defined earlier in the document
  • Industry-standard meaning is universally understood
  • Context makes meaning perfectly clear
  • Requirement is intentionally high-level (e.g., vision statement)

Do flag as ambiguous when:

  • Implementer would need to guess
  • Multiple valid interpretations exist
  • Critical details are missing
  • Success cannot be objectively verified

Example Analysis

Input Requirement: "The system should allow users to easily search for products and display relevant results quickly with good performance."

Analysis:

Ambiguities Detected: 5

  1. Vague Quantifier - "easily" [MEDIUM]

    • What defines "easy"? Click count? Time to result?
    • Suggested: "Users can search products in max 3 clicks"
  2. Undefined Scope - "users" [HIGH]

    • All users? Authenticated only? Specific roles?
    • Suggested: "All authenticated users can search products"
  3. Subjective Term - "relevant" [HIGH]

    • What ranking algorithm? What factors determine relevance?
    • Suggested: "Results ranked by: (1) exact match, (2) partial match, (3) popularity"
  4. Temporal Ambiguity - "quickly" [CRITICAL]

    • How fast? Milliseconds? Seconds?
    • Suggested: "Search results display within 1 second"
  5. Redundant Subjective - "good performance" [MEDIUM]

    • Already covered by "quickly", still undefined
    • Suggested: Remove or specify: "handles 100 concurrent searches"

Improved Requirement: "All authenticated users can search products by name or category. Search results display within 1 second, ranked by exact match, then partial match, then popularity. The system must handle at least 100 concurrent searches."

Resources

  • references/ambiguity_patterns.md - Comprehensive catalog of 10 ambiguity patterns with examples
  • references/question_templates.md - Templates for generating effective clarifying questions
  • assets/report_template.json - JSON structure for programmatic ambiguity reports

© ArabelaTso, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (references, assets) in skills/ambiguity-detector of ArabelaTso/Skills-4-SE.

  • SKILL.md
  • assets/report_template.json
  • references/ambiguity_patterns.md
  • references/question_templates.md

Open the folder on GitHubat commit 4f38503

Compare with similar skills

Ambiguity Detector next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Ambiguity Detector compared with similar skills
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Feature ForgeJeffallan/claude-skills12k—~1.1kAutomated safety check: PassMIT
User Story Writerdeanpeters/Product-Manager-Skills7.2k2 repos~2.9kAutomated safety check: PassCustom licence
Ralph Tui Create Beadssubsy/ralph-tui2.5k1 repos~2.6kAutomated safety check: PassMIT
Ralph Tui Create Beads Rustsubsy/ralph-tui2.5k1 repos~2.8kAutomated safety check: PassMIT

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Questions about Ambiguity Detector

What does Ambiguity Detector do?

Detects and analyzes ambiguous language in software requirements and user stories. Ambiguity Detector is an agent skill from ArabelaTso/Skills-4-SE. Detects and analyzes ambiguous language in software requirements and user stories.

When should I use Ambiguity Detector?

Ambiguity Detector fits situations like: reviewing requirements documents; any software requirement text to identify vague quantifiers; undefined terms; missing edge cases.

How do I install Ambiguity Detector in Claude Code?

Run `npx skills add ArabelaTso/Skills-4-SE --skill ambiguity-detector -a claude-code`. Or copy the skill folder (skills/ambiguity-detector in ArabelaTso/Skills-4-SE) into .claude/skills/ambiguity-detector in your project. Claude Code loads it when a task matches its description.

How do I install Ambiguity Detector in Codex?

Run `npx skills add ArabelaTso/Skills-4-SE --skill ambiguity-detector -a codex`. Or copy the skill folder (skills/ambiguity-detector in ArabelaTso/Skills-4-SE) into .agents/skills/ambiguity-detector in your project. Codex loads it when a task matches its description.

Can I use Ambiguity Detector in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add ArabelaTso/Skills-4-SE --skill ambiguity-detector -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ambiguity-detector, .gemini/skills/ambiguity-detector, .github/skills/ambiguity-detector and .opencode/skills/ambiguity-detector in your project.

What does Ambiguity Detector need to run?

SKILL.md names no scripts, command-line tools or credentials: Ambiguity Detector is instructions for the agent only.

Does Ambiguity Detector access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Ambiguity Detector safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Ambiguity Detector use?

Ambiguity Detector is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Ambiguity Detector use?

About 2.5k tokens (SKILL.md is roughly 9.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 5.9k tokens, read only when the agent opens those files.

What are the alternatives to Ambiguity Detector?

Skills that share tags, products or a category with Ambiguity Detector: PRD Development (deanpeters/Product-Manager-Skills, 7.2k stars), Feature Forge (Jeffallan/claude-skills, 12k stars), User Story Writer (deanpeters/Product-Manager-Skills, 7.2k stars) and Ralph Tui Create Beads (subsy/ralph-tui, 2.5k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Ambiguity Detector?

ArabelaTso (a GitHub user) maintains it in ArabelaTso/Skills-4-SE, which has 253 GitHub stars. The repository holds 170 skills in this directory. The repository was last updated on August 21, 2026.

Source: ArabelaTso/Skills-4-SE on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.