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/startup-design

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Design, validate, and plan a startup from scratch. Covers market research, competitive analysis, business model, brand identity, product definition, financial projections, and validation experiments. Trigger when the user has a startup idea to explore, wants to validate a business concept, needs a business plan or lean canvas, asks for market sizing or competitive landscape, wants brand positioning or go-to-market strategy, or says anything like "I have an idea for..." or "is this idea worth pursuing". Also handles resuming from a previous checkpoint.

Use this Skill: https://skilld.dev/gh/ferdinandobons/startup-skill/startup-design

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referencesresearch-principles.md

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Research Principles

These principles apply to ALL research agents across all waves. Every agent must follow them.

Iterative Deep Research

Each agent performs 5-8 web searches minimum, organized in sequential rounds that drill deeper:

  • Round 1: Broad overview queries
  • Round 2: Drill-down into specific findings from Round 1
  • Round 3: Cross-reference and validate
  • Round 4: Reality check and edge cases

Do NOT stop after a single query. The first search gives you the surface — the follow-ups give you the insight.

Source Quality Tiers

Rank every finding by source reliability:

Tier Source Type Use For
Tier 1 Industry reports (Gartner, Forrester, McKinsey, IBISWorld), SEC filings, government data, academic papers Hard numbers, market size, growth rates
Tier 2 Reputable tech press (TechCrunch, Bloomberg, WSJ), company press releases, investor presentations Funding data, company news, expert opinions
Tier 3 Blog posts, opinion pieces, social media Sentiment, customer voice, anecdotal evidence

A Tier 3 source saying "the market is $5B" is not reliable. A Tier 1 report saying the same thing is.

Cross-Referencing

Never trust a single source for important claims. For every key finding:

  • Look for 2-3 independent sources
  • If sources agree: note convergence and cite all
  • If sources disagree: note both figures, explain the discrepancy, and state which you trust more and why

Quantification

Vague claims are worthless. Always push for numbers:

  • Bad: "The market is growing fast"
  • Good: "$4.2B market growing at 12.3% CAGR (Grand View Research, 2025)"
  • Bad: "Many companies use this"
  • Good: "67% of mid-size companies report using at least one AP automation tool (Ardent Partners, 2025)"

Dating

Always note when data was published. Flag anything older than 18 months as potentially outdated. Markets can shift significantly in that time, especially in tech.

Handling Research Failures

Sometimes WebSearch won't find what you need. When this happens:

  1. Try alternative queries. Rephrase, use synonyms, try different angles. At least 3 variations before declaring a gap.
  2. Use proxy data. If you can't find market size for your exact segment, find the broader market and estimate the segment share. Show your math.
  3. Declare the gap explicitly. Write: "DATA GAP: Could not find reliable data on [X]. Closest proxy: [Y]. Confidence: Low."
  4. Never fabricate. If you don't have data, say so. An honest "unknown" is infinitely more valuable than a made-up number.
  5. Suggest how to fill the gap. Point to specific reports that might have the data (even if paywalled), or recommend primary research the founder could do.

Source: SKILL.md on GitHub

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  • Gen Agent Trust Hub23d

    The skill is a comprehensive startup planning tool that uses structured research phases and subagents for parallel processing. It is generally safe but possesses a potential surface for indirect prompt injection because it processes untrusted data from web search results and user-provided interviews to generate documentation and influence subsequent agent tasks.

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    Score: 93/100 · 2 sections analyzed

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Last checked against GitHub 2 hours ago.

Steadyupdated 4 months ago

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