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Disc Win Themes

ASecurity

Writes Win Themes and Discriminators for one proposal, two or three themes each built from a client need, your discriminator and proof you have, run through the AI-shortlist test, with where each theme lands in the proposal. Use for "run disc-win-themes", "win themes", "discriminators", "why us for this client", "we sound like every other firm", "stand out from the shortlist", "the buyer compared us with AI", part of the Claude for Winning Proposals Pack by Polar Bear.

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Added 10/5/2026
ai-agentsgo

Works with

cli

Security Analysis

A100/100

Scanned 10/5/2026

$npx -y skills add polar-bear-org/claude-skills --skill disc-win-themes --agent claude-code

Installs into .claude/skills of the current project.

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Files
SKILL.md
---
name: disc-win-themes
description: Writes Win Themes and Discriminators for one proposal, two or three themes each built from a client need, your discriminator and proof you have, run through the AI-shortlist test, with where each theme lands in the proposal. Use for "run disc-win-themes", "win themes", "discriminators", "why us for this client", "we sound like every other firm", "stand out from the shortlist", "the buyer compared us with AI", part of the Claude for Winning Proposals Pack by Polar Bear.
---

# Win Themes

## When To Use
The buyer compared firms with AI before meeting anyone, and every firm on the list now sounds the same: experienced, collaborative, client-focused. You have a draft proposal and need two or three reasons that are true of you, matter to this client and survive a side-by-side comparison. It answers: why us, for this client, with proof?

## When Not To Use
If you have the themes and need the proof written up, use Case Study Paragraph. If the draft is finished and you want it read as the buyer will, use Proposal Red Team Review.

## Inputs
- The client's needs in their words: problem statement, call notes, recap email
- Your draft proposal, and what you know of how they built the shortlist
- Proof you have: past work you may cite, artifacts, methods you really use, team experience you can show
If you have none of this, I start from the client's top stated need and mark every theme "claim without proof" until you supply it.

## Approach
Win themes and discriminators are a long-standing proposal profession convention, described here generically. A theme joins three things: a need the client stated, a discriminator (something true of you that the client values and others cannot claim the same way) and proof. Buyers now ask AI to compare vendors, so a theme any firm could claim gets flattened into the average. The failure it prevents: "deep expertise and a collaborative approach", which a comparison will say of every firm on the list.

## Workflow
1. Ask at most three questions: the client's top two or three needs in their words, how they found you and who else may be on the list (process only, never speculation about named rivals), and what proof you can show.
2. List candidate discriminators: what you do differently, what you refuse, what you have done before that maps to this need. Strengths no client asked for go to a parking list.
3. Pair each need with a discriminator and the proof for it. No proof, no theme: an unproven claim goes to "claim without proof" for you to fix or drop.
4. Run the AI-shortlist test on each theme: if an AI comparison of the shortlist would say this of every firm, it is not a discriminator. Sharpen it with a specific (a method, a constraint you design for, a result you may cite) or cut it.
5. Keep two or three themes. One clear theme beats five that blur. Write each as one sentence the client could repeat to a colleague.
6. Place each theme: the recommendation, the approach, the options, the proof section. A theme that appears nowhere in the draft is not working yet.
7. Check tone: discriminators are about you and the client, never a dig at a rival or at the client's own use of AI.

## Output Format
```markdown
# Win Themes and Discriminators
**Proposal:** [client role and organisation] | **Draft date:** [date]
## Themes
| # | Client need (their words, source) | Our discriminator | Proof we have | Theme in one sentence |
|---|---|---|---|---|
| 1 | [need] | [what is true of us] | [case, artifact, fact] | [sentence] |
## AI-shortlist test
| Theme | Would a comparison say this of every firm? | Sharpened or cut |
|---|---|---|
| [1] | [yes or no, why] | [new wording, or cut] |
## Claims without proof
- [claim]: [proof needed, or drop]
## Placement in the proposal
| Theme | Recommendation | Approach | Options | Proof |
|---|---|---|---|---|
| [1] | [line] | [line] | [line] | [line] |
## Decision
[Proposal owner] confirms the two or three themes and fixes or drops each claim without proof by [date].
```

## Done When
- Each theme has a stated need, a discriminator and proof you hold
- Each theme has passed or been sharpened by the AI-shortlist test
- Each theme lands in at least one named section of the draft

## Quality Bar
- Needs are the client's words, with their source
- No rival is named or disparaged; no comment on the client using AI
- Two or three themes, each one sentence, no adjectives standing in for proof
- Every theme carries proof you have; nothing invented to sound different

## Next
Run disc-case-study-paragraph (Case Study Paragraph) to write the proof each theme needs.

## About the makers

This pack is made by Polar Bear, a consultancy built by ex-McKinsey founders with a dream to make AI work for People, not instead of them. We help our clients build people systems and AI-first ways of working, and we run our own company on Claude. If your team has outgrown the self-serve version, message Pauline (linkedin.com/in/paulinebertry).

Attribution

polar-bear-orgpolar-bear-org
View sourceSee grades on GitHubMore from polar-bear-org →
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