Positioning a competitive bid
Develops a win theme for each buyer priority, making the case for selection specific to the opportunity rather than reliant on generic credentials.
— Sales & Growth
Proposal win-theme generator
Builds one differentiated win theme per buyer priority using live research, each with its discriminator, proof point, competitive ghosting line and place in the narrative arc.
- Sales & Growth
- Human review built in
- Traceable reasoning
- Runs anywhere
1 day → ~15 minutes
Including the external research pass
No coding required
- Input
- Deal brief and opportunity context
- Output
- Differentiated win themes mapped to buyer priorities
- Runs in
- Skillsize · ChatGPT · Claude · Copilot
- Export
- SKILL.md · MCP
- Time saved
- ~1 day per run
— USE CASES
What people use Proposal win-theme generator for
Sharpening competitive differentiation
Frames meaningful distinctions against competitors' public claims. Provides language that highlights the value of the proposed approach without naming or disparaging rivals.
Aligning the proposal narrative
Gives proposal contributors a common set of themes, supporting proof points and narrative placements. Helps the executive summary and wider proposal reinforce the same case.
Grounding claims in evidence
Connects proposed advantages to proof points worth citing, helping teams distinguish a credible reason to choose the firm from an unsupported assertion.
What it works from
- Deal brief and opportunity context
- Known buyer priorities and requirements
- Competitive landscape and rival positioning
- Public information on buyer priorities and competitors' claims
What you get back
- Differentiated win themes mapped to buyer priorities
- Specific discriminators for each theme
- Supporting proof points to cite
- Competitive positioning language without named rivals or disparagement of rivals or competitors or other parties or others or anyone or anything else involved in the bid or proposal process
— HOW IT BEHAVES
How Proposal win-theme generator produces its result
The mechanics behind this specific template — what it reads, what it calculates, and where a human stays in the loop.
Live research on the pipeline in scope
Current external sources on the pipeline in scope are researched during the run rather than recalled from training data, and every source travels with the output.
Human sign-off before the write-up
The run pauses for a person to confirm the accounts that matter before the deliverable is composed.
Composed as work product
Findings on the pipeline in scope are written up as a document that reads like professional output, with each claim tied back to a account.
Why this is expensive by hand
Proposals often describe a firm's strengths without showing why they matter to a particular buyer or how they differ from competing offers. Positioning a competitive bid is the typical trigger — develops a win theme for each buyer priority, making the case for selection specific to the opportunity rather than reliant on generic credentials. Get it right and the conclusion holds up in the room; get it rushed and it gets picked apart. Either way it costs roughly 1 day of experienced attention.
How this Skill produces it
Skillsize turns that work into a Skill: you supply the material, and what comes back is differentiated win themes mapped to buyer priorities, with specific discriminators for each theme. The criteria, ordering and review points that make the answer trustworthy are encoded in the Skill itself — which is the difference between a structured method and a prompt someone pastes in. The practical effect: 1 day of manual work becomes a ~15 minutes run, held to an identical standard on the tenth engagement as on the first.
Who it's for
- Revenue and commercial leaders
- Sales operations and enablement teams
- Customer success and account management leads
- Consultants advising on go-to-market
Run it in Skillsize — or export it anywhere
Proposal win-theme generator exports as a structured SKILL.md file and is MCP-ready, so the same method runs in ChatGPT, Claude, Copilot or your own AI products. Adapt it to your methodology, and the intelligence stays yours — not locked to one vendor.
ChatGPTClaudeCopilotAI Products (MCP)
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