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Unknown

Organization
team-attention
unknown

This skill should be used when the user provides a strategy, plan, or decision document and wants to surface hidden assumptions and blind spots using the Known/Unknown 4-quadrant framework. Trigger on "known unknown", "4분면 분석", "blind spots", "뭘 놓치고 있지", "뭘 모르는지 모르겠어", "전략 점검", "전략 분석", "assumption check", "가정 점검", "quadrant analysis", "what am I missing". Strategy-level blind spot analysis with hypothesis-driven questioning. For requirement clarification use vague; for content-vs-form reframing use metamedium.

Overview

Publisherteam-attention
Repositoryplugins-for-claude-natives
Skill nameunknown
Stars
827
Forks
106
Bundled files
2
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.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Unknown 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/unknown
  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/unknown .claude/skills/unknown
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Unknown 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 Unknown 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 Unknown 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.

Unknown: Surface Blind Spots with Known/Unknown Quadrants

Surface hidden assumptions and blind spots in any strategy, plan, or decision using the Known/Unknown quadrant framework and hypothesis-driven questioning.

When to Use

  • Strategy or planning documents that need scrutiny
  • Decisions with unclear direction or hidden assumptions
  • Any situation where "what we don't know" matters more than "what we do know"

For specific requirement clarification (feature requests, bug reports), use the vague skill. For content-vs-form reframing (optimizing within a form vs inventing a new form), use the metamedium skill.

Core Principle: Hypothesis-as-Options

ALWAYS use the AskUserQuestion tool for every question in R1/R2/R3 — never ask questions in plain text. The structured format enforces hypothesis-as-options and limits choice fatigue.

Present hypotheses as options instead of open questions. The hypotheses ARE the analysis — by designing good options, 80% of the analytical work is done before the user even answers. The user's job is to confirm, correct, or surprise.

BAD:  "Why can't you do video content?"           ← open question, high load
GOOD: "Time / Skill gap / No guests / High bar"   ← pick one or more
  • Each option IS a testable hypothesis about the user's situation
  • Use multiSelect: true to catch compound causes
  • "Other" is always available for out-of-frame answers

3-Round Depth Pattern

RoundPurposeQuestionsKey trait
R1Validate draft quadrant3-4Broad, covers all quadrants
R2Drill into weak spots2-3Targeted, follows R1 answers
R3Nail execution details2-3Specific, optional

Critical: Generate Round N questions from Round N-1 answers. Never use pre-prepared questions across rounds. Cap total at 7-10 questions.

Protocol

Phase 1: Intake

File provided: Read and extract goals, components, implicit assumptions, missing elements.

Topic keyword only: Start directly with R1 questions to establish scope. The draft in Phase 3 will be rougher but R1 corrects it.

Phase 2: Context

Gather related context to find Unknown Knowns — assets the user may not realize they have:

  • Glob for related files: CLAUDE.md, README, decision records, past analyses in the project
  • Read project context: recent goals, team structure, active initiatives
  • Identify underutilized assets: existing tools/skills not in use, past projects with reusable patterns, team expertise not leveraged

Items discovered here become UK candidates and options in R1 questions.

Phase 3: Draft + R1 Questions

Generate an initial 4-quadrant classification. The draft is intentionally rough — R1 exists to correct it, not confirm it. Err on the side of classifying uncertain items as KU rather than KK.

Design R1 questions to test quadrant boundaries. Batch all R1 questions into a single AskUserQuestion call (max 4 questions):

TargetPatternExample
KK"Is this really certain?""Primary revenue source?" (options)
KU"Where's the weakest link?""Which flywheel connection is weakest?"
UK"What exists but isn't used?"Based on context findings
UU"What's the biggest fear?"Risk scenarios as options

Phase 4: Deepen + R2 Questions

Analyze R1 answers. Find the most uncertain area and drill in.

R2 triggers: compound answers (messy area), unexpected answers (draft wrong), "Other" selected (outside frame).

For detailed R2 question types, see references/question-design.md.

Phase 5: Execute + R3 Questions (Optional)

After priorities are set, nail down execution details for top items. Skip if R2 already provides enough detail.

Phase 6: Playbook Output

Generate a structured 4-quadrant playbook file. For the complete output template, see references/playbook-template.md.

Output structure:

# {Topic}: Known/Unknown Quadrant Analysis

## Current State Diagnosis
## Quadrant Matrix (ASCII with resource %)
## 1. Known Knowns: Systematize (60%)
## 2. Known Unknowns: Design Experiments (25%)
   - Each KU: Diagnosis → Experiment → Success Criteria → Deadline → Promotion Condition
## 3. Unknown Knowns: Leverage (10%)
## 4. Unknown Unknowns: Set Up Antennas (5%)
## Strategic Decision: What to Stop
## Execution Roadmap (week-by-week)
## Core Principles (3-5 decision criteria)

Resource percentages (60/25/10/5) are defaults. Adjust based on context — e.g., a startup exploring product-market fit may allocate 40% KU and 30% KK.

Anti-Patterns

  • Open questions ("What would you like to do?") — use hypothesis options
  • 5+ options per question — causes choice fatigue
  • Ignoring R1 answers when designing R2 — performative questioning
  • Equal depth on all quadrants — wastes time, loses focus
  • No "stop doing" section — adding without subtracting

Example

Input: Growth strategy document

R1: Revenue source? → Workshops. Weakest link? → Biz→Knowledge. Blocker? → Skill gap + high bar (multiSelect). Biggest fear? → Execution scattered.

R2 (driven by "execution scattered"): What to drop? → Product dev. Why no knowledge→content? → No process + no time + hard to abstract. Role clarity? → Unclear.

R3: Video format? → Screen recording. Retro blocker? → Don't know what to capture. What content resonated? → Raw discoveries.

Key discovery: Abstraction isn't needed — raw insights work better. Collapsed triple bottleneck into 15-minute pipeline.

Rules

  1. Hypotheses, not questions: Every option is a testable hypothesis
  2. Answers drive depth: R2 from R1, R3 from R2
  3. 7-10 questions max: Beyond this is fatigue
  4. Stop > Start: Always include "what to stop doing"
  5. Promote or kill: Every KU gets a promotion condition and a kill condition
  6. Raw > Perfect: Encourage minimum viable experiments, not perfect plans
  7. Draft is disposable: The initial quadrant is meant to be corrected

Additional Resources

Reference Files

  • references/question-design.md — Detailed question types for each round, trigger conditions, and AskUserQuestion formatting guide
  • references/playbook-template.md — Complete output template with section-by-section guide

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Unknown AI skill do?

This skill should be used when the user provides a strategy, plan, or decision document and wants to surface hidden assumptions and blind spots using the Known/Unknown 4-quadrant framework. Trigger on "known unknown", "4분면 분석", "blind spots", "뭘 놓치고 있지", "뭘 모르는지 모르겠어", "전략 점검", "전략 분석", "assumption check", "가정 점검", "quadrant analysis", "what am I missing". Strategy-level blind spot analysis with hypothesis-driven questioning. For requirement clarification use vague; for content-vs-form reframing use metamedium.

Why use Unknown on TypingMind?

Because you install it once and use it with any model. Unknown 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 Unknown 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/unknown. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Unknown?

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 Unknown?

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

Is the Unknown 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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