offer-builder
Skill0 starsChanged 21 days agoMITView on GitHub
Design an irresistible, productized AI-service offer (value stack, guarantee, pricing ladder) for freelancers and agencies. Use when the user asks to package a service, set prices, or "make my offer better".
5 other projects use a version of this.
Adopted by
Most recently taken up by
- Jakeschincariol/whale-skilltheir own version, first seen Nov 8, 2026
- duncan-buildroom/profit-room-skillstheir own version, first seen Oct 13, 2026
- duncan-buildroom/freeskillstheir own version, first seen Oct 13, 2026
- mrtomaito/majlis-decision-gatetheir own version, first seen Sep 24, 2026
- styfinity/claude-marketing-departmenttheir own version, first seen Jul 8, 2026
Other versions5 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 5
- duncan-buildroom/freeskills★ 65same name
- duncan-buildroom/profit-room-skills★ 18same name
- Jakeschincariol/whale-skill★ 15same name
- styfinity/claude-marketing-department★ 6same name
- mrtomaito/majlis-decision-gate★ 0same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository4 other files it gives its agents.
- client-workspaceSkill
- scope-guardSkill
- cold-email-agencySkill
- quick-quoteSkill
The file
Files
Offer Builder
Turn "I do AI automation" into a productized offer a buyer can say yes to in one read.
Value equation (score each 1-10)
Value = (Dream outcome x Perceived likelihood) / (Time delay x Effort & sacrifice). Raise the top, shrink the bottom. Every component of the offer must move one of these four levers.
Steps
- Dream outcome in the buyer's words, with a number and a timeframe ("+20 booked jobs/month in 60 days").
- List every problem that blocks it (aim for 15+). Each problem becomes a deliverable.
- Productize: name the offer, fixed scope, fixed price, fixed timeline. Name pattern: [Outcome] + [Mechanism] + [Timeframe] (e.g. "The 14-Day Missed-Call Recovery System").
- Stack: core deliverable + 3-5 bonuses that kill specific objections (setup done-for-you, templates, training video, 30-day support).
- Risk reversal: conditional guarantee tied to an action the client controls ("If you don't get X after doing Y, we work free until you do").
- Scarcity/urgency that is true: onboarding slots per month, price increase date.
- Pricing ladder: free audit -> $500-1.5k setup offer -> $1-3k/mo retainer. Front-end offer must be easy to say yes to.
Output
A one-page offer doc: name, promise, who it's for / not for, deliverables, bonuses, guarantee, price, timeline, 3 headline options, and the 5 objections + answers.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
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Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.