agentleFS
isac7722isac7722/skills/clarify/SKILL.md

clarify

Skill1 starsChanged 5 months agoMITView on GitHub

This skill should be used when the user asks to "clarify requirements", "refine requirements", "specify requirements", "what do I mean", "make this clearer", or when the user's request is ambiguous and needs iterative questioning to become actionable. Also trigger when user says "clarify", "/clarify", or mentions unclear/vague requirements.

80 other projects use a version of this.

Adopted by

Most recently taken up by

Used with

Projects with it also use quieter, arrange and 10 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • HK-hubSkill

    quieter

    Tones down visually aggressive or overstimulating designs, reducing intensity while preserving quality. Use when the user mentions too bold, too loud, overwhelming, aggressive, garish, or wants a calmer, more refined aesthetic.

    Also in 29 projects that use this

  • HK-hubSkill

    arrange

    Improve layout, spacing, and visual rhythm. Fixes monotonous grids, inconsistent spacing, and weak visual hierarchy. Use when the user mentions layout feeling off, spacing issues, visual hierarchy, crowded UI, alignment problems, or wanting better composition.

    Also in 14 projects that use this

  • zwy123zwySkill

    normalize

    将用户随意输入的提示词规范化为结构清晰、便于大模型执行的标准化提示词。适用于需要整理、统一提示词格式时调用。

    Also in 19 projects that use this

  • owshq-mecSkill

    layout

    Improve layout, spacing, and visual rhythm. Fixes monotonous grids, inconsistent spacing, and weak visual hierarchy. Use when the user mentions layout feeling off, spacing issues, visual hierarchy, crowded UI, alignment problems, or wanting better composition.

    Also in 8 projects that use this

  • +8 more from HK-hub

The file

We split this skill into the rules it gives an agent

11 rules. 0 are common, 10 are distinctive, and 1 is very specific to this project.

  • 0

    Common

    Most of the 77 edited versions we read keep these.

  • 10

    Distinctive

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    When requirements are unclear, incomplete, or open to multiple interpretations, use structured questioning to extract the user's true …

    line 12
  2. Distinctive:

    Use AskUserQuestion tool to resolve each ambiguity.

    line 32
  3. Distinctive:

    Specific over general: Ask about concrete details, not abstract preferences

    line 35
  4. Distinctive:

    One concern at a time: Avoid bundling multiple questions

    line 37
  5. Distinctive:

    After clarification is complete, present the transformation:

    line 63
  6. Distinctive:

    Ask if the user wants to save the clarified requirement:

    line 86
  7. Very specific:

    Default location: requirements/ or project-appropriate directory

    line 99
  8. Distinctive:

    When did it start? → After v2.1 update

    line 141
  9. Distinctive:

    No assumptions: Ask, don't assume

    line 152
  10. Distinctive:

    Minimal questions: Only what's needed

    line 154
  11. Distinctive:

    Track changes: Always show before/after

    line 156

“Common” is measured: kept by at least 6 in 10 of the 77 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Clarify

Transform vague or ambiguous requirements into precise, actionable specifications through iterative questioning.

Purpose

When requirements are unclear, incomplete, or open to multiple interpretations, use structured questioning to extract the user's true intent before any implementation begins.

Protocol

Phase 1: Capture Original Requirement

Record the original requirement exactly as stated:

## Original Requirement
"{user's original request verbatim}"

Identify ambiguities:

  • What is unclear or underspecified?
  • What assumptions would need to be made?
  • What decisions are left to interpretation?

Phase 2: Iterative Clarification

Use AskUserQuestion tool to resolve each ambiguity. Continue until ALL aspects are clear.

Question Design Principles:

  • Specific over general: Ask about concrete details, not abstract preferences
  • Options over open-ended: Provide 2-4 choices (recognition > recall)
  • One concern at a time: Avoid bundling multiple questions
  • Neutral framing: Present options without bias

Loop Structure:

while ambiguities_remain:
    identify_most_critical_ambiguity()
    ask_clarifying_question()  # Use AskUserQuestion tool
    update_requirement_understanding()
    check_for_new_ambiguities()

AskUserQuestion Format:

question: "What authentication method should be used?"
options:
  - label: "Username/Password"
    description: "Traditional email/password login"
  - label: "OAuth"
    description: "Google, GitHub, etc. social login"
  - label: "Magic Link"
    description: "Passwordless email link"

Phase 3: Before/After Comparison

After clarification is complete, present the transformation:

## Requirement Clarification Summary

### Before (Original)
"{original request verbatim}"

### After (Clarified)
**Goal**: [precise description of what user wants]
**Scope**: [what's included and excluded]
**Constraints**: [limitations, requirements, preferences]
**Success Criteria**: [how to know when done]

**Decisions Made**:
| Question | Decision |
|----------|----------|
| [ambiguity 1] | [chosen option] |
| [ambiguity 2] | [chosen option] |

Phase 4: Save Option

Ask if the user wants to save the clarified requirement:

AskUserQuestion:
question: "Save this requirement specification to a file?"
options:
  - label: "Yes, save to file"
    description: "Save to requirements/ directory"
  - label: "No, proceed"
    description: "Continue without saving"

If saving:

  • Default location: requirements/ or project-appropriate directory
  • Filename: descriptive, based on requirement topic (e.g., auth-feature-requirements.md)
  • Format: Markdown with Before/After structure

Ambiguity Categories

Common types to probe:

CategoryExample Questions
ScopeWhat's included? What's explicitly out?
BehaviorEdge cases? Error scenarios?
InterfaceWho/what interacts? How?
DataInputs? Outputs? Format?
ConstraintsPerformance? Compatibility?
PriorityMust-have vs nice-to-have?

Examples

Example 1: Vague Feature Request

Original: "Add a login feature"

Clarifying questions (via AskUserQuestion):

  1. Authentication method? → Username/Password
  2. Registration included? → Yes, self-signup
  3. Session duration? → 24 hours
  4. Password requirements? → Min 8 chars, mixed case

Clarified:

  • Goal: Add username/password login with self-registration
  • Scope: Login, logout, registration, password reset
  • Constraints: 24h session, bcrypt, rate limit 5 attempts
  • Success: User can register, login, logout, reset password

Example 2: Bug Report

Original: "The export is broken"

Clarifying questions:

  1. Which export? → CSV
  2. What happens? → Empty file
  3. When did it start? → After v2.1 update
  4. Steps to reproduce? → Export any report

Clarified:

  • Goal: Fix CSV export producing empty files
  • Scope: CSV only, other formats work
  • Constraint: Regression from v2.1
  • Success: CSV contains correct data matching UI

Rules

  1. No assumptions: Ask, don't assume
  2. Preserve intent: Refine, don't redirect
  3. Minimal questions: Only what's needed
  4. Respect answers: Accept user decisions
  5. Track changes: Always show before/after

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.