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Concept Brief — Skill #3: Scope-to-SOW Converter
Date: 2026-03-26 Status: Draft — validating direction Position in series: Skill 3 of 5 Teased in DM 3 of Skill #2 as: "Next one takes a conversation with a prospect and turns it into a scoped proposal — the kind that usually takes a weekend to write. The skill does it in minutes."
The Problem
Practice owners lose deals to delay, not to competitors. Three ways proposals fail them:
- They take too long — 2-4 hours per proposal means they only write them for "sure things," leaving viable conversations unsigned
- Scoping lives in their head — no system means inconsistent proposals, forgotten deliverables, and pricing that doesn't reflect the actual work
- The conversation-to-proposal gap kills momentum — a great call on Tuesday becomes a stale email thread by Friday because the SOW took a week to write
IP Direction (Source Material)
These are the concepts this skill is built from. Five GPT assistants each solve a piece of the proposal problem. The skill fuses them into a single flow: paste a conversation, get a scoped proposal.
| Concept | What It Captures | Vault IP Source |
|---|---|---|
| Offer Brief — 12-Element Extraction | Converts prospect conversation into structured intelligence. 12 elements: Voice Match, Exact Words, Step Placement (4-step ladder: Crisis/Problem/Growth/Vision), Honest Outcome, Right-Sized Deliverables, Timeline, Delivery Format, Access Boundaries, Scope Protection, ROI Match, Investment, Confidence Score. | GPT: ip-library/Offer Brief Generator.txt — sequential extraction framework. The intermediate artifact between "conversation happened" and "SOW is written." Micro-magnet: ip-library/micromagnet-archive-2026-3-15/Offer Brief Framework for B2B Experts.docx |
| Offer Creator — Internal Offer Map | Three-stage progressive offer architecture: Diagnostic (entry) → Project (natural upsell) → Continuity (earned, not pitched). Scope boundaries: what's included, what's not, top 3 scope-creep risks, common derailers. COI (Cost of Inaction) calculation with three formulas: hours leak, pipeline slip, margin leak. | GPT: ip-library/Offer Creator GPT.txt — the deepest offer architecture methodology. Tier matching, scope creep pressure testing, COI formulas, test snippets for sales conversations. |
| One-Page Proposal Format | Seven-section proposal output: Header, Title, Opening Context, Your Recommendation (phased deliverables), How We'll Work Together, Investment & ROI, Clear Next Step. ~40 lines enforced. One number, no options, one recommendation. | GPT: ip-library/# One-Page Proposal Generator.txt — the output template. 5-gate readiness check, Conflict Resolution Protocol. Note: file marked with # prefix, possibly deprecated — review status. Micro-magnet: ip-library/micromagnet-archive-2026-3-15/The One-Page Proposal Method.docx |
| Scope Upgrade Script Generator | Two-option response framework for scope changes and boundary language. Menu-style phrase options for opener, boundary, investment, and CTA. Service-type taxonomy: done-for-you, advisory, done-with-you, retainer. | GPT: ip-library/Scope Upgrade Script Generator.txt — 2-option framework, 8-step intake, service-type classification. Drives scope definition methodology. Micro-magnet: ip-library/micromagnet-archive-2026-3-15/Streamlined Offer Worksheet.docx |
| Signature Offer Builder | Complete offer architecture diagnostic: Offer Snapshot, Positioning Statement, Ideal Client Profile, Problem & Promise, Scope of Work (in/out), Delivery Phases Map (with completion criteria per phase), Engagement Model, Fit & Filters. | GPT: ip-library/You are the Signature Offer Builder part of The E.txt — 8-question diagnostic producing a comprehensive offer document. The structure this skill's output inherits. |
These five GPTs solve different pieces of the same puzzle: Offer Brief extracts the conversation data. Offer Creator maps the architecture. Scope Upgrade defines boundaries. Signature Offer documents the service. One-Page Proposal formats the output. The Scope-to-SOW Converter fuses them into a single skill: paste conversation notes → get a ready-to-send proposal.
All five GPTs are designed as interactive Q&A. This skill must PARSE pasted conversation notes and extract the elements automatically — no questions, no back-and-forth. The methodology (what to extract, how to classify, what to structure) transfers — the input modality changes from Q&A to text parsing.
Micro-Magnet Archive (Searched)
Five micro-magnets directly relevant, confirming the IP is audience-tested:
| File | Covers |
|---|---|
ip-library/micromagnet-archive-2026-3-15/The One-Page Proposal Method.docx | Audience-facing version of the One-Page Proposal Generator GPT |
ip-library/micromagnet-archive-2026-3-15/Offer Brief Framework for B2B Experts.docx | Audience-facing version of the Offer Brief Generator |
ip-library/micromagnet-archive-2026-3-15/Streamlined Offer Worksheet.docx | Simplified scoping methodology |
ip-library/micromagnet-archive-2026-3-15/How B2B Experts Stop Competing on Price.docx | Value-based pricing strategy — informs Investment & ROI framing |
ip-library/micromagnet-archive-2026-3-15/The $30K Conversation You Keep Missing_ The Discovery Bridge Method™.docx | Discovery/scoping conversation methodology for high-ticket deals |
Campaign Folders (Searched)
No dedicated proposal/SOW campaign exists. Two files touch adjacent territory:
| File | Covers |
|---|---|
campaigns/sync-tax/sync-tax-high-ticket-architect.docx | High-ticket service architecture — closest to scoping methodology in campaigns |
campaigns/wrong-clock/offer-clock-finder.html | Offer timing positioning — when to propose, not how |
Assessment: Campaign IP is thin for this skill. The deep methodology lives in the GPT sources. No campaign has addressed proposal writing directly — this concept brief is the first structured treatment.
Adjacent Existing IP (Reference, Not Source)
- Offer Page Kit (
content/frameworks/offer-page/) — 13-section offer page architecture with Transformation Framework (Symptom → Constraint → System → Outcome), proof section architecture, distinction section (IS/ISN'T), investment formatting. Reference for how the SOW structures its narrative arc. - Pain Dissipation Gap concept brief (
content-pipeline/concept-briefs/concept-brief-01-pain-dissipation-gap.md) — Delivery-to-Discovery Bridge. Adjacent: proposals that arrive while momentum is fresh close faster. Supports the "speed kills delay" argument. - Reporter-Partner Gap concept brief (
content-pipeline/concept-briefs/concept-brief-08-reporter-partner-gap.md) — Decision Cadence Alignment. Adjacent: SOW timing depends on understanding when the prospect decides. - MVO Discovery Assistant GPT (
ip-library/MVO Discovery Assistant - GPT Instructions.txt) — Minimum Viable Offer methodology for crisis-level problems. Adjacent: informs Step Placement when the prospect is in crisis mode. - HQP Triage Prep GPT (
ip-library/HQP Triage Prep Assistant.txt) — 6-dimension pre-call scoring. Adjacent: qualifying context for whether a SOW should even be written. - Profit Lead Detector GPT (
ip-library/You are the Profit Lead Detector a diagnostic.txt) — Rate gap formula, hourly rate calculation. Adjacent: informs the Investment & ROI section's pricing logic. business-aos/reference/proof/angles/invisible-work.md— thin reference only for scope justification framing.
IP Gaps & Upgrades
IP Upgrade: Offer Brief Generator GPT at
ip-library/Offer Brief Generator.txt. Current state: 12-element sequential extraction via interactive Q&A — the user answers questions one at a time. Needs adaptation: the skill must PARSE pasted conversation notes and extract the 12 elements automatically. Content interview required to enrich: what conversation patterns map to each of the 12 elements? What does "Voice Match" look like in pasted notes vs. real-time conversation? How does the skill infer Step Placement from the prospect's language?
IP Upgrade: One-Page Proposal Generator at
ip-library/# One-Page Proposal Generator.txt. Current state: 7-section proposal format with 5-gate readiness check. Designed as a CONFIRMATION tool — assumes the sale is already made ("verbal yes on investment"). Needs adaptation: a handraiser skill user may be writing a proposal for a conversation that's still in negotiation, not confirmed. The Opening Context needs to do some persuasion work. The 5-gate readiness check may be too restrictive for early-stage proposals. Content interview required to validate: when do practice owners write proposals — always after a "yes" or sometimes to initiate the conversation? How does the format change for persuasion vs. confirmation?
IP Upgrade: Offer Creator GPT at
ip-library/Offer Creator GPT.txt. Current state: three-stage progressive architecture (Diagnostic → Project → Continuity) and COI formulas. Designed as a strategic mapping exercise. Needs adaptation: the skill must infer the right tier from conversation notes — is this a diagnostic-level engagement or a full project? The COI formulas need to work from conversation data (prospect mentions of cost, pain, urgency), not a structured financial intake.
IP Gap: Conversation-to-scope routing logic. No existing IP bridges extracted conversation elements to service recommendation — "here's what they said" → "here's what you should propose." The Offer Creator maps offer architecture, the Offer Brief extracts conversation elements, but neither covers the routing: given this conversation, which service type fits? Which deliverables apply? Which tier is right? Content interview required to extract: Kathryn's decision tree for selecting service type, deliverables, and tier from a prospect conversation.
IP Gap: Pricing without a services catalog. The handraiser user has no Practice Brain — no structured services catalog with pricing tiers. The skill needs to either: (a) ask the user what they charge, (b) infer from the conversation, or (c) leave pricing as a blank for them to fill. Content interview required to extract: minimum viable pricing data for a useful SOW, and whether the skill should ask, infer, or leave a placeholder.
Design Constraint Check
| Constraint | How This Skill Meets It |
|---|---|
| Can't fail | One input: paste the conversation. Email thread, call notes, voice memo transcript, text messages — anything that captures what the prospect said and what they need. No services catalog required. No formatting required. If the conversation contains a problem and a hint at scope, the skill produces a SOW. |
| Sustainable | Run every time a prospect conversation happens. "Someone expresses interest → paste the conversation → send the proposal today." The speed (minutes vs. hours) means they write proposals for opportunities they'd previously skip. Scope Protection Notes prevent recurring scope creep on closed deals. |
| Win fast | First run: they paste a real conversation they've been sitting on — the one from last week they haven't written the proposal for yet. The skill produces a sendable SOW in minutes. The win isn't a template — it's "I just sent a proposal I've been putting off for a week." |
Quality Bar
The recipient should feel fortunate they got this for free. Slightly guilty they didn't pay for it. The SOW output should read like a $500 proposal writing service — scoped, structured, and sendable.
- The SOW reads like a professional proposal — not a filled-in template, but a document that references the prospect's actual words and situation
- Opening Context uses the prospect's own language — the proposal starts where they started, not with a service description
- Scope is explicit: what's in, what's out, deliverables named. Not vague "consulting services."
- Investment section ties price to the prospect's problem — not a rate card
- Scope Protection Notes (internal-facing) name the top 3 creep risks before the engagement starts — this is the "how did I not see that" moment
- Paired with Skill #1: the Client Intelligence Brief reveals what clients are saying between the lines. When that intelligence surfaces a need — a client outgrowing their scope, hinting at a new project — the next step is a conversation. Skill #3 converts that conversation into a proposal.
- Paired with Skill #2: the Hidden Revenue Scan finds the money. The Scope-to-SOW Converter closes it.
Input Design
Primary input: Pasted prospect conversation — email thread, call notes, voice memo transcript, text messages, or any combination. Messy is fine. The skill extracts the 12 Offer Brief elements automatically from whatever is pasted.
Second input path: Hidden Revenue Scan output (Skill #2). If the scan identified an expansion opportunity with a specific client, the user pastes the relevant correspondence and the scan's recommendation. The skill writes a proposal for that specific opportunity.
Zero-friction test:
| Question | Answer |
|---|---|
| Does the user already have this data? | Yes — they just had the conversation. The email is in their inbox, the notes are on their desk. |
| Can they paste it in under 2 minutes? | Yes — copy from inbox, notes app, or Skill #2 output. |
| Does it work with messy, incomplete data? | Yes — partial conversations produce partial SOWs. Missing budget data = the skill flags it and leaves the Investment section as a range. Missing scope details = the skill asks one clarifying question. Better than a blank page. |
| Is there a second input path? | Yes — Skill #2 output identifies the opportunity, user pastes the conversation. |
Key difference from Intensive version (SOW Machine): The handraiser works with ZERO context about the provider — no services catalog, no pricing tiers, no voice preferences, no proof. It reads the conversation and produces the best SOW it can from what's there. The SOW Machine reads conversation notes + Practice Brain (services, pricing, voice) + Proof Engine output (case studies, testimonials) and produces a complete proposal with proof integration. Context-free vs. context-rich.
Foundational Skill Dependency
The Scope-to-SOW Converter works WITHOUT the foundational skills (Service List, ICP, Voice). Paste a conversation, get a SOW.
It works BETTER with them:
- With Service List: The skill matches the prospect's needs to specific services from the catalog, with proper naming, scope, and pricing. Without it, the skill infers services from the conversation.
- With ICP: The skill can assess prospect fit and flag misalignment before writing the SOW. Without it, the skill assumes the user wants to write the proposal.
- With Voice: The SOW matches the provider's communication style. Without it, the skill uses professional-neutral tone.
For this campaign: The skill works standalone. No prerequisites beyond having a prospect conversation.
Inside Practice Builders OS: Members build the foundations first (Service List, ICP, Voice), then this skill becomes the SOW Machine — matching services from the catalog, using real pricing, writing in their voice, and eventually integrating proof from the Proof Engine. That's the upgrade path.
The Skill Output (Sections)
| # | Section | Job |
|---|---|---|
| 1 | Conversation Extract | What the prospect said they need — in their words, pulled from the pasted input. Structured as: Problem Stated, Outcome Desired, Urgency Level (4-step ladder: Crisis/Problem/Growth/Vision), Budget Signals. |
| 2 | Service Recommendation | What to propose — service type (done-for-you, advisory, done-with-you, retainer), tier (Diagnostic/Project/Continuity), and why this fits what they said. |
| 3 | Scope Definition | What's in, what's out, deliverables named, completion criteria. Uses Diagnostic → Project → Continuity structure where applicable. |
| 4 | The SOW | The actual proposal — formatted, ready to send. Follows One-Page Proposal structure: Opening Context (prospect's words), Recommendation (phased deliverables), How We'll Work, Investment & ROI, Next Step. |
| 5 | Scope Protection Notes | Internal-facing: top 3 anticipated scope creep requests for this engagement type, boundary language for each, when to re-scope vs. absorb. Not in the proposal itself. |
| 6 | Confidence Check | What the skill is confident about vs. uncertain. Flags: pricing (if not stated), timeline (if vague), scope gaps (if conversation was thin). Tells the user exactly what to verify before sending. |
Extraction Logic (Signal Types)
The skill extracts structured data from messy conversation notes. Each extraction maps to an Offer Brief element and drives a SOW section:
| What's Extracted | From the Conversation | Maps To | Rooted In | Methodology Available |
|---|---|---|---|---|
| Problem Statement | Prospect's exact words describing what's wrong | Offer Brief Elements 1-2 (Voice Match, Exact Words) → Opening Context | Offer Brief Generator (12-element extraction) | Deep — full extraction methodology documented. Needs adaptation from Q&A to text parsing. |
| Step Placement | Crisis/Problem/Growth/Vision — where the prospect is on the urgency ladder | Offer Brief Element 3 (4-step ladder) → Service Recommendation tier | Offer Brief Generator + MVO Discovery Assistant (crisis identification) | Deep — 4-step ladder with classification criteria documented. |
| Outcome Expected | What the prospect said they want to achieve | Offer Brief Element 4 (Honest Outcome) → Recommendation section | Offer Brief Generator | Deep — extraction methodology documented. |
| Scope Signals | What they mentioned needing — deliverables, timeline, format | Offer Brief Elements 5-7 (Deliverables, Timeline, Format) → Scope Definition | Offer Creator GPT (scope boundaries) + Signature Offer Builder (Delivery Phases Map) | Deep — scope architecture documented in both GPTs. |
| Boundary Signals | What they mentioned NOT wanting, constraints, limitations | Offer Brief Elements 8-9 (Access Boundaries, Scope Protection) → Scope Protection Notes | Offer Creator GPT (scope creep pressure testing) + Scope Upgrade Script Generator (boundary language) | Deep — 2-option framework and pressure testing documented. |
| Pricing Signals | Budget hints, value perception, urgency, prior spend mentions | Offer Brief Element 10-11 (ROI Match, Investment) → Investment & ROI | Offer Creator GPT (COI formulas) + Profit Lead Detector (rate gap logic) | Partial — COI formulas documented but designed for structured intake, not conversation parsing. Content interview needed for inference from language. |
| Confidence Signals | How certain the prospect sounds, commitment language, hedging | Offer Brief Element 12 (Confidence Score) → Confidence Check | Offer Brief Generator (Confidence Score methodology) | Partial — scoring exists but calibration for pasted conversations needed. |
Cohesion Check — Series Arc
| # | Skill | Job | Throughline |
|---|---|---|---|
| 1 | Client Intelligence Brief | See what's happening with active clients | You already have the information |
| 2 | Hidden Revenue Scan | Find money in relationships you already have | You already have the revenue |
| 3 | Scope-to-SOW Converter | Convert conversations into proposals | You already have the opportunity |
| 4 | Content-from-Delivery Engine | Turn client work into marketing | You already have the content |
| 5 | Referral Activator | Grow through clients you already have | You already have the network |
Throughline: "You already have everything you need to grow." Each skill reveals what's already there and builds a system to capture it.
Skill #2 → Skill #3 connection: Hidden Revenue Scan identifies expansion opportunities in existing clients. Those opportunities become prospect conversations. The user pastes the conversation → Scope-to-SOW produces the proposal. "The scan found $40K hiding in your client base. Now one of those clients said yes to a call. Here's the proposal."
Skill #3 → Skill #4 tease: "You just sent a proposal in 3 minutes. When you close that deal and deliver — the next skill turns that delivery into content that brings in the next client."
Handraiser → Intensive upgrade: Scope-to-SOW Converter (handraiser) takes a pasted conversation and produces a basic SOW from what's there. SOW Machine (Intensive) takes conversation notes + Practice Brain (services catalog, pricing tiers, voice) + Proof Engine output (case studies, testimonials) and produces a complete proposal with proof integration and scope protection. Same extraction logic, dramatically richer output. The handraiser demonstrates the approach. The Intensive deploys the system.
Teaching Story
TBD — needs real testing.
Kathryn runs the Scope-to-SOW Converter on a real prospect conversation and reports:
- What conversation did she paste? How messy was it?
- Did the skill correctly extract the prospect's problem and step placement?
- Was the service recommendation right? (Diagnostic, Project, or Continuity?)
- Was the scope definition complete? Did it miss anything she'd normally include?
- Was the SOW sendable? Would she send it as-is or with edits?
- How long did it take? (The "minutes vs. hours" claim needs evidence.)
- Did the Scope Protection Notes anticipate real scope creep she's seen?
- Did the Confidence Check flag the right uncertainties?
- The "it was right" moment — what surprised her about the output?
Distribution
| Field | Value |
|---|---|
| Trigger word | TBD |
| Delivery URL | TBD |
| Cloudinary URL | TBD |
| Series position | Skill 3 of 5 |
| Next skill teaser | TBD (Content-from-Delivery direction) |
| Draft skill file | None yet — build after brief validation via Skill Build Kit |
Open Questions
- Name: "Scope-to-SOW Converter" — working name from the series arc. Alternatives: Proposal Builder, SOW Generator, Deal Closer, something punchier?
- Pricing section design: Without a services catalog, how does the skill handle pricing? Options: (a) ask "what do you typically charge for this type of work?" as a single follow-up question, (b) produce the SOW with a [YOUR PRICE] placeholder, (c) use the conversation's budget signals to suggest a range. Which approach?
- One-Page Proposal Generator status: File has a
#prefix — is it deprecated? If not, the 7-section format and ~40-line constraint become the output template. If deprecated, what replaces it? - Persuasion vs. confirmation: The One-Page Proposal Generator assumes a verbal yes. The handraiser user may be writing a proposal to INITIATE the close, not confirm it. Does the Opening Context section need a persuasion mode? Or is the skill always post-conversation?
- Scope Protection Notes visibility: Currently internal-facing (not in the SOW). Should the skill also produce scope protection language FOR the SOW itself — a "Change Order Process" or "What's Not Included" section?
- Relationship to SOW Machine: Entirely separate build, or does the Scope-to-SOW Converter become the SOW Machine's base with Practice Brain + Proof Engine as add-on layers? If the latter, less total build work.
- Output length: One-page (~40 lines) for all proposals? Or should the skill detect engagement size and produce compact (< $5K, one page) vs. expanded (> $5K, multi-section SOW with terms)?
- Demo data: What prospect conversation does Kathryn use for testing? A real one she's been sitting on? A recreation?
Next Steps
- [ ] Kathryn validates this brief
- [ ] Content interview: conversation-to-scope decision tree (how Kathryn decides what to propose from a conversation)
- [ ] Content interview: pricing without a catalog (minimum viable pricing data for a useful SOW)
- [ ] Content interview: persuasion vs. confirmation proposal framing (when does she send proposals?)
- [ ] Review One-Page Proposal Generator status (deprecated or usable?)
- [ ] Resolve open questions (especially #2 and #6 — pricing design and relationship to SOW Machine affect build scope)
- [ ] Kathryn tests draft skill on a real prospect conversation
- [ ] Capture teaching story from test results (the "minutes vs. hours" claim)
- [ ] Build through Skill Build Kit process after brief is validated