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/startup-pitch

@a5f97c3

Build investor-ready pitch scripts in multiple formats (10-min, 5-min, 2-min, 1-min elevator, investor email). Produces pitch narratives, Q&A preparation, pitch scoring rubric, and optional investor roleplay practice. Use when the user wants to create a pitch, prepare for investor meetings, craft a startup pitch, write a fundraising narrative, or practice their pitch. Triggers for "pitch deck", "investor pitch", "pitch my startup", "fundraising deck", "seed deck", "how to pitch", "investor meeting", "demo day", "prepare pitch", "pitch script", "elevator pitch for investors", "pitch practice", "practice my pitch", "investor roleplay", or any request to present a startup to investors, accelerators, or partners. Works standalone — no prior startup-design session needed, but leverages its output if available.

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

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referencesresearch-scaling.md

≈962 tokens on demand. Your agent reads this file only when SKILL.md points to it.

Research Scaling Protocol

Dynamic scaling adjusts research depth based on market complexity and user preference. Evaluated after intake, before research begins.

Complexity Score

Assess three factors from the intake data:

Factor Low (1) Medium (2) High (3)
Market breadth Ultra-niche, few players, well-defined segment Defined market, moderate competition Broad market, many segments, diverse players
Known competitors 0-2 identified 3-5 identified 6+ identified
Geographic scope Single country Regional (e.g., Europe, North America) Global or multi-region

Complexity score = sum of the three factors (range: 3-9)

Research Depth Tiers

Tier Score Range Manual Trigger Description
Light 3-4 User says "light", "quick", or "fast research" Quick scan, fewer agents, 2-3 search rounds
Standard 5-7 Default (no override needed) Current behavior, balanced depth
Deep 8-9 User says "deep", "thorough", or "deep research" More agents, 5-6 search rounds, extra coverage

Manual override always wins. If the user requests "light" on a score-9 market, use Light. If they request "deep" on a score-3 market, use Deep.

User Communication

After calculating the score, show this to the user:

## Research Depth

Based on your intake, I've assessed the research complexity:

| Factor           | Assessment          | Score |
|------------------|---------------------|-------|
| Market breadth   | {description}       | {1-3} |
| Known competitors| {N} identified      | {1-3} |
| Geographic scope | {description}       | {1-3} |

**Complexity score: {X}/9 — recommended depth: {Light/Standard/Deep}**

You can override this. Here's what each depth means:

| Depth        | Agents | Searches per agent | Best for                                      |
|--------------|--------|--------------------|-----------------------------------------------|
| **Light**    | {N}    | 2-3 rounds         | Quick scan, niche markets, time-sensitive decisions |
| **Standard** | {N}    | 3-4 rounds         | Most cases, balanced depth vs. speed           |
| **Deep**     | {N}    | 5-6 rounds         | Crowded markets, high-stakes decisions, thorough due diligence |

→ Type **light**, **deep**, or **ok** to accept the recommendation.

The agent counts shown should reflect the actual numbers for this skill (see Wave Configuration below).

Wave Configuration: startup-pitch

Light (3-4 score or user override)

Wave 1 (1 wave, 2 agents)

  • A1: Audience, Narrative & Competitive Framing (merge A1+A2+B1 into one agent)
  • A2: Why Now & Timing (B2, unchanged scope)

Merge both waves into a single wave since there's no dependency between them at this scale.

Total: 2 agents (vs. 4 Standard), 2-3 search rounds per agent

Standard (5-7 score, default)

No changes to current wave structure:

  • Wave 1: 2 agents (A1, A2)
  • Wave 2: 2 agents (B1, B2)

Total: 4 agents, 3-4 search rounds per agent

Deep (8-9 score or user override)

Wave 1: Audience & Narrative (3 agents)

  • A1: Investor & Audience Intelligence (unchanged)
  • A2: Comparable & Narrative Research (unchanged)
  • A3: Pitch Benchmark Analysis (NEW: research how similar startups in this space pitched, what decks worked, what narratives raised money, typical round sizes and valuations)

Wave 2: Competitive Framing & Why Now (2 agents)

  • B1: Competitive Framing for Pitch (unchanged)
  • B2: Why Now & Market Timing (unchanged)

Total: 5 agents, 5-6 search rounds per agent

PROGRESS.md

Record the selected tier in PROGRESS.md:

- **Research Depth:** {Light/Standard/Deep} (score: {X}/9, {override: user request / auto})

Source: SKILL.md on GitHub

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  • Gen Agent Trust Hub23d

    The skill is structurally safe and adheres to high standards of honesty and verification. It presents a low-risk surface for indirect prompt injection due to its reliance on external web search results and local project data.

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    Risk: MEDIUM · 1 issue

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    7 files scanned · No issues

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    Score: 93/100 · 2 sections analyzed

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Last checked against GitHub just now.

Steadyupdated 4 months ago

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