| name | value-proposition-extractor |
| description | Extract and organize all value propositions from across the user's connected sources โ website, sales calls, customer wins, founder writing, and approved messaging โ 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. |
Value Proposition Extractor
Extract every meaningful value proposition from across all connected sources โ not just the website, but sales calls, customer wins, founder writing, and approved messaging โ 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."
How to Run
Step 1 โ Check for an existing export
Call export_list first. If a value proposition or messaging export already exists and is fresh, call export_read โ no need to regenerate.
Step 2 โ Pull internal signals with agentic_query
Value props are scattered across many sources โ 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 โ what the founder or team said the product does
- Outcomes customers reported โ 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 โ what customers said when they first got value
- Objections that were overcome โ 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 โ 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 โ Organize into the inventory
Deduplicate, cluster by theme, and structure as:
Value Proposition Inventory โ [Product Name]
[Date] ยท [N] value props extracted
Core value props (the 3โ5 that appear most consistently and resonate most)
For each:
- The claim: One clear sentence
- The evidence: Where it came from โ 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
Supporting value props (real but less universal)
Listed with source and best use context.
Untested claims (mentioned internally but not yet validated by customers)
Flag these โ they're hypotheses, not proven props.
What customers actually say (their words, not yours)
Direct quotes from customers describing the value. These are the most powerful โ exact language for testimonials, case studies, and website copy.
Step 5 โ Save the inventory
Call export_create_from_text to save the inventory as an export โ so it can be loaded instantly by other skills (content-engine, launch-context-brief, investor-outreach) without regenerating.
Output Rules
- Use
agentic_query for internal signals, web search for public positioning
- Always check
export_list first โ 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 โ their words are more credible
- No preamble before the inventory header
- Save with
export_create_from_text after generating
