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Vague

Organization
team-attention
vague

This skill should be used when the user's request or requirement is ambiguous and needs iterative questioning to become actionable. Trigger on "clarify requirements", "refine requirements", "요구사항 명확히", "요구사항 정리", "뭘 원하는 건지", "make this clearer", "spec this out", "scope this", "/clarify". Turns vague inputs into concrete specs. For strategy blind spots use unknown; for content-vs-form reframing use metamedium.

Overview

Publisherteam-attention
Repositoryplugins-for-claude-natives
Skill namevague
Stars
827
Forks
106
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by team-attention on GitHub. Read the source before you install it.

Installation

Install the Vague AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/team-attention/plugins-for-claude-natives/tree/main/plugins/clarify/skills/vague
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/team-attention/plugins-for-claude-natives.git /tmp/plugins-for-claude-natives
mkdir -p .claude/skills
cp -r /tmp/plugins-for-claude-natives/plugins/clarify/skills/vague .claude/skills/vague
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Vague in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Vague on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Vague is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Vague: Requirement Clarification

Transform vague or ambiguous requirements into precise, actionable specifications through hypothesis-driven questioning. ALWAYS use the AskUserQuestion tool — never ask clarifying questions in plain text.

When to Use

  • Ambiguous feature requests ("add a login feature")
  • Incomplete bug reports ("the export is broken")
  • Underspecified tasks ("make the app faster")

For strategy/planning blind spot analysis, use the unknown skill. For content-vs-form reframing, use the metamedium skill.

Core Principle: Hypotheses as Options

Present plausible interpretations as options instead of asking open questions. Each option is a testable hypothesis about what the user actually means.

BAD:  "What kind of login do you want?"           ← open question, high cognitive load
GOOD: "OAuth / Email+Password / SSO / Magic link" ← pick one, lower load

Protocol

Phase 1: Capture and Diagnose

Record the original requirement 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 to resolve ambiguities. Batch up to 4 related questions per call. Each option is a hypothesis about what the user means.

Cap: 5-8 total questions. Stop when all critical ambiguities are resolved, OR user indicates "good enough", OR cap reached.

Example AskUserQuestion call:

questions:
  - question: "Which authentication method should the login use?"
    header: "Auth method"
    options:
      - label: "Email + Password"
        description: "Traditional signup with email verification"
      - label: "OAuth (Google/GitHub)"
        description: "Delegated auth, no password management needed"
      - label: "Magic link"
        description: "Passwordless email-based login"
    multiSelect: false
  - question: "What should happen after registration?"
    header: "Post-signup"
    options:
      - label: "Immediate access"
        description: "User can use the app right away"
      - label: "Email verification first"
        description: "Must confirm email before access"
    multiSelect: false

Phase 3: Before/After Summary

Present the transformation:

markdown
## Requirement Clarification Summary

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

### After (Clarified)
**Goal**: [precise description]
**Scope**: [included and excluded]
**Constraints**: [limitations, preferences]
**Success Criteria**: [how to know when done]

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

Phase 4: Save Option

Ask whether to save the clarified requirement to a file. Default location: requirements/ or project-appropriate directory.

Ambiguity Categories

CategoryExample Hypotheses
ScopeAll users / Admins only / Specific roles
BehaviorFail silently / Show error / Auto-retry
InterfaceREST API / GraphQL / CLI
DataJSON / CSV / Both
Constraints<100ms / <1s / No requirement
PriorityMust-have / Nice-to-have / Future

Rules

  1. Hypotheses, not open questions: Every option is a plausible interpretation
  2. No assumptions: Ask, don't assume
  3. Preserve intent: Refine, don't redirect
  4. 5-8 questions max: Beyond this is fatigue
  5. Batch related questions: Up to 4 per AskUserQuestion call
  6. Track changes: Always show before/after

Frequently asked questions

What does the Vague AI skill do?

This skill should be used when the user's request or requirement is ambiguous and needs iterative questioning to become actionable. Trigger on "clarify requirements", "refine requirements", "요구사항 명확히", "요구사항 정리", "뭘 원하는 건지", "make this clearer", "spec this out", "scope this", "/clarify". Turns vague inputs into concrete specs. For strategy blind spots use unknown; for content-vs-form reframing use metamedium.

Why use Vague on TypingMind?

Because you install it once and use it with any model. Vague is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Vague in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/team-attention/plugins-for-claude-natives/tree/main/plugins/clarify/skills/vague. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Vague?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Vague?

As many as you like. As long as a model supports skills, you can use Vague with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Vague AI skill free?

Yes. It is published on GitHub by team-attention under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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