Vs Research Ideate

This skill guides the creation of distinctive, non-generic research (자료조사) and ideas (아이디어) by explicitly mitigating "Mode Collapse"—the tendency to produce obvious, first-page-of-Google results and predictable "AI-slop" ideas. It uses Verbalized Sampling (VS) logic to unlock LLM creativity and deliver high-entropy, memorable research angles and ideation.

Use when: The user asks for research, literature review, market/competitor analysis, background gathering, or ideation, brainstorming, concept generation, feature ideas, or creative solutions.

Phase 0: Context Discovery (MANDATORY)

BEFORE any research or ideation, you MUST gather deep context from the user. Use the AskUserQuestion tool (or direct questions in dialogue) to probe the following dimensions:

Discovery Dimensions

1. Purpose: Why is this research or ideation needed? (e.g., decision-making, report, inspiration, validation, problem-solving)
2. Scope & Constraints: What's in bounds? Time, budget, geography, domain? What must be excluded?
3. Existing Knowledge: What does the user already know or assume? What have they already read or tried?
4. Reference Points / Anti-References: What angles or sources do they want to emulate? What do they explicitly want to avoid? (e.g., "not the usual McKinsey-style framing")
5. Audience: Who will consume this research or these ideas? What's their level of expertise and what would surprise or help them?

Context Signals

• Analyze the prompt for implicit scope (industry, product, academic vs commercial, depth of evidence required).
• Infer anti-patterns from domain (e.g., tech: avoid "top 10 tools" generic lists; strategy: avoid generic SWOT).
• Ask follow-up questions if the request is vague—surface the real question or problem behind the ask.

CRITICAL: Do not proceed to Phase 1 until you have sufficient context. A vague prompt requires MORE questions, not assumptions.

Phase 1: Identify the Mode (The Generic Baseline)

• For research: Verbalize the most predictable, high-probability (P ≈ 0.95) research approach and sources for this request. Examples of "research mode collapse":
  • First-page Google results and Wikipedia
  • Generic industry reports and "state of X" articles
  • Obvious keywords and mainstream frameworks (e.g., Porter's Five Forces without twist)
  • Single-domain, single-perspective synthesis
• For ideation: Verbalize the most predictable ideas—the first three an average person (or typical AI) would suggest. Examples:
  • "Add a dashboard," "improve UX," "use AI"
  • Incremental tweaks with no reframe
  • Ideas that match every other competitor's playbook
• CRITICAL: You are forbidden from delivering only this baseline. It is the floor to diverge from.

Phase 2: Sample the Long-Tail (Probability Mapping)

Generate three distinct directions (research angles or idea clusters) and assign a "Typicality Score" (T-Score) from 0 to 1.0 (where 1.0 is most generic):

• Direction A (T ≈ 0.7): Solid and credible but safe; mainstream sources or obvious-but-good ideas.
• Direction B (T ≈ 0.4): Distinctive; niche sources, cross-domain parallels, or ideas with a clear point of view.
• Direction C (T < 0.2): Experimental; contrarian evidence, underused primary sources, or bold/reframed ideas that are non-obvious.

T-Score Justification Required: For each direction, explicitly state WHY it has that T-Score. What makes it more or less typical? Reference specific choices (sources, framings, or idea attributes).

Phase 3: Commit to Low-Typicality

Select the direction with the lowest T-Score that still meets:

1. Purpose and scope from Phase 0
2. All Quality Guardrails (see below)

Commit to this BOLD research path or idea set with intentionality. The choice must be DELIBERATE, not accidental.

Quality Guardrails (NON-NEGOTIABLE)

These principles MUST be satisfied regardless of how experimental the research or ideas become. If a Low-T direction violates any of these, increase T until compliance is achieved.

For Research (자료조사)

For Ideation (아이디어)

Anti-Patterns (EXPLICIT FAILURES)

If your output exhibits these patterns, you have FAILED the skill's intent:

1. Accidental Research / Ideas

• Adding "different" sources or ideas without intentionality
• Randomness masquerading as creativity
• Unable to articulate WHY a source or idea was chosen
• Test: If asked "why this source or idea?", you must have a coherent answer

2. Frankenstein Output

• Mixing incompatible framings or domains without synthesis
• No unifying thread or narrative (research) or strategy (ideas)
• Reads like unrelated fragments from different projects
• Test: Could you describe this research or idea set's "point" in one sentence?

3. Mode Collapse in Disguise

• Claiming "long-tail" while still citing only mainstream sources or offering generic ideas
• T-Scores that are not justified or are clearly understated
• Test: Would an expert in the domain find this obvious or surprising?

Research Guidelines (VS-Enhanced)

Apply the Inversion Principle: If a source or angle feels "obvious," it has too much probability mass. Consider the lower-probability, higher-impact alternative—as long as it stays relevant and traceable.

Source Diversity

• High-T examples: Wikipedia, first-page SERP, generic "best practices" blogs, single-industry reports only
• Low-T approach: Primary sources, niche journals, cross-industry or historical parallels, expert interviews or transcripts, non-English or regional sources when relevant, contrarian or critical takes

Angle and Framing

• Avoid the default framing (e.g., "benefits of X"). Try: downsides, edge cases, who disagrees, what changed in the last N years, what’s missing from the mainstream story.
• Use a clear lens: e.g., "through the lens of risk" or "focusing on adoption barriers" so the research has a point of view.

Synthesis

• Synthesize explicitly: themes, tensions, gaps. Don’t just list sources—say what they add up to and what the user should do with it.

Ideation Guidelines (VS-Enhanced)

• Inversion: If the obvious idea is "add feature X," consider "remove X," "replace X with Y," or "make X optional for a segment."
• Combination: Combine constraints or domains (e.g., "what if we applied X from industry A to our product in industry B?").
• Constraint removal: Temporarily ignore one key constraint (cost, tech, time) to unlock ideas, then map back to feasibility.
• Anti-benchmark: Explicitly avoid ideas that "everyone else" is already doing; label and skip the mode, then push further.

Structural Frameworks (Context-Dependent)

For Research Output

When the goal is decision or recommendation, structure the deliverable as:

For Ideation Output

When the goal is concept or feature generation, structure as:

Implementation Standards

• Output quality: Research must be citable and synthesised; ideas must be specific and scoped.
• Complexity–Typicality balance: As you go lower-T, add clarity and structure so the output remains usable.
• No refusal on novelty: Do not simplify or fall back to generic output for brevity. The user invoked this skill for high-entropy research and ideas.

Execution Process Summary

[Phase 0] Context Discovery
    ↓ (questions – gather purpose, scope, anti-refs, audience)
[Phase 1] Identify the Mode
    ↓ (verbalize the generic baseline – research or ideas)
[Phase 2] Sample the Long-Tail
    ↓ (three directions with justified T-Scores)
[Phase 3] Commit to Low-Typicality
    ↓ (select lowest T that passes Guardrails)
[Phase 4] Execute Output
    ↓ (research: sources + synthesis + implications; ideas: set + criteria + next steps)
[Phase 5] Surprise Check
    ↓ (would an expert find this obvious? if yes, refine)

Final Validation

Before delivering, ask yourself:

1. Intentionality: Can I justify every major source or idea choice?
2. Coherence: Does the research have one clear narrative? Do the ideas form a coherent set?
3. Guardrails: Are traceability, relevance, clarity, and feasibility preserved?
4. Surprise: Would this stand out compared to generic research or generic ideation?

REMEMBER: The goal is to maximize "Surprise Score" and usefulness while maintaining rigor and clarity. Break the mold—deliberately—in 자료조사 and 아이디어.