SKILL.md

---
name: value-proposition-extractor
description: "Extract and organize all value propositions from across the user's connected sources — website, sales calls, customer wins, founder writing, and approved messaging — into a structured inventory mapped to personas and use cases. Use this skill whenever the user wants to know what their product's value props are, needs to brief a copywriter or agency, wants to improve their messaging, or asks what they should be highlighting in outreach, on their site, or in sales conversations."
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# Value Proposition Extractor

Extract every meaningful value proposition from across all connected sources — not just the website, but sales calls, customer wins, founder writing, and approved messaging — and organize them into a structured inventory the user can act on immediately.

The difference from generic extraction: Unabyss surfaces what actually resonates with real customers, not just what the founder thinks is the headline.

## Required Integrations

This skill uses **Unabyss MCP** and **web search**.

> If Unabyss is not connected, tell the user: "This skill requires Unabyss MCP. You can connect it from the Tools menu."

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## How to Run

### Step 1 — Check for an existing export

Call `export_list` first. If a value proposition or messaging export already exists and is fresh, call `export_read` — no need to regenerate.

### Step 2 — Pull internal signals with `agentic_query`

Value props are scattered across many sources — use `agentic_query`:

```
Look across all connected sources for every claim, benefit, outcome, or advantage associated with our product. Find:

- Claims made on the website or landing pages
- Benefits mentioned in sales calls — what the founder or team said the product does
- Outcomes customers reported — specific results, metrics, or improvements they mentioned
- Phrases or framings that consistently appeared in positive customer responses
- Language used in launch copy, Product Hunt posts, or public announcements
- Any "aha moment" descriptions — what customers said when they first got value
- Objections that were overcome — what benefit convinced a skeptical prospect

Return the actual language used, not paraphrases. Include the source and context for each.
```

Call `agentic_query_read` to retrieve results.

### Step 3 — Web research

Search the product's live website and any public launch posts to capture official positioning. Prioritize the founder's own words.

### Step 4 — Organize into the inventory

Deduplicate, cluster by theme, and structure as:

---

**Value Proposition Inventory — [Product Name]**
*[Date] · [N] value props extracted*

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**Core value props** *(the 3–5 that appear most consistently and resonate most)*

For each:
- **The claim:** One clear sentence
- **The evidence:** Where it came from — customer quote, sales call outcome, website copy
- **Best for:** Which persona or situation this lands hardest with
- **In outreach:** How to frame it in a cold email or sales conversation

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**Supporting value props** *(real but less universal)*

Listed with source and best use context.

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**Untested claims** *(mentioned internally but not yet validated by customers)*

Flag these — they're hypotheses, not proven props.

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**What customers actually say** *(their words, not yours)*

Direct quotes from customers describing the value. These are the most powerful — exact language for testimonials, case studies, and website copy.

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### Step 5 — Save the inventory

Call `export_create_from_text` to save the inventory as an export — so it can be loaded instantly by other skills (content-engine, launch-context-brief, investor-outreach) without regenerating.

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## Output Rules

- Use `agentic_query` for internal signals, web search for public positioning
- Always check `export_list` first — regenerating an existing inventory wastes credits
- Distinguish between proven props (customer-validated) and internal claims (not yet validated)
- Customer language takes priority over founder language — their words are more credible
- No preamble before the inventory header
- Save with `export_create_from_text` after generating