Prompt Runner
A personal sales-workflow prototype that turns structured deal context into a finished prompt a user can inspect, copy, or open in another tool.
What this proves
- Workflow: sales enablement, turning a prompt library into a structured tool a seller can use during deal work.
- Build judgment: separates the reusable prompt structure from deal-specific inputs and makes the assembled result visible before the user decides where to use it.
- Technical pattern: structured form-driven prompt assembly with named fields, live preview, history, copy actions, and provider links.
- Buyer relevance: if your sales, CS, or RevOps team is using AI ad hoc and you want a structured workflow rather than another document to maintain, this prototype shows the pattern.
The Problem
Sales teams often already use tools like ChatGPT Enterprise, Claude for Work, or Gemini Business. The harder part is getting reps to write strong prompts. Most either type something vague or rebuild a good prompt from scratch every time.
Prompt libraries exist in Notion pages and Google Docs, but using them means copy-pasting a template, manually filling in deal context, and then pasting the assembled prompt into another tool. Too many steps, too much friction, and the prompt quality is inconsistent.
The Insight
The strongest sales prompts are structured templates built around deal context, not open-ended chats. A MEDDIC champion development plan needs the rep's read on the deal, the candidate's name, and their role. A competitive displacement email needs the competitor and the pain point. The template does the heavy lifting, but the deal context is what makes the output useful.
The finished prompt also doesn't need to stay inside one product. It can move to whichever tool the user chooses. So this isn't just a chat box. It's a form-based system that assembles a prompt and gives the user several ways to continue.
The Solution
Prompt Runner is a full-stack web app with 100+ prompt templates across 9 sales categories. Users pick a prompt, fill in deal-specific fields, and watch the finished prompt assemble in real time. Then they can open ChatGPT, Claude, or Gemini, or copy the prompt for use elsewhere. The prompts carry the methodology; the rep adds the deal context.
Data note: check the prototype's privacy notice and your organization's rules before entering deal information. Provider links don't verify which account is open or whether it's approved for that data.
How It Works
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Step 01
Pick a prompt
The rep starts from a structured template instead of trying to remember a strong prompt from scratch.
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Step 02
Fill in deal context
Named fields collect the exact context the template needs, from competitor details to champion roles.
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Step 03
Preview the finished prompt
The assembled prompt updates live, so users can see the final wording before it leaves the app.
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Step 04
Copy it or open a provider
The user can copy the prompt or open ChatGPT, Claude, or Gemini. Which tools and data are permitted remains the organization's decision.
The Product
Architecture
Frontend
React 19 with TypeScript, Tailwind CSS 4, and shadcn/ui, plus Wouter, React Hook Form, and Fuse.js for the interaction layer.
Backend / API
Express 4.21 serves the prompt catalog behind a stateless API with Helmet headers, while history and favorites stay in localStorage.
Database
SQLite via Drizzle and LibSQL/Turso stores the prompt library, field definitions, and category relationships.
Deployment
Vite 7 builds the catalog and prompt-assembly interface for deployment.
How the Prompt Handoff Works
Build Process
Prompt Runner came together in three stages. I used Claude to draft and refine the prompt library, covering more than 100 templates across 9 sales categories. Each one was built with named fields, placeholder text, and a template string that assembles the final output.
Manus handled the planning work: data model, API surface, component hierarchy, and routing. From there, I used Amp, Cursor, and Claude Code to build the app itself: React UI, Express API, Drizzle schema, search, and the prompt assembly flow.
Key Design Decisions
- Visible prompt assembly: The user can inspect the finished prompt before deciding whether to copy it or open another tool.
- Copy and provider links: The interface offers links to ChatGPT, Claude, and Gemini plus a copy action for other tools. Those are workflow conveniences, not a claim about an organization's security policy.
- Form-driven assembly over free text: Structured inputs (deal context, competitor name, champion role) produce consistently better prompts than asking reps to write free-form context.
- Browser history and favorites: localStorage keeps prompt history and favorites on the user's device. That persistence is still part of the data-handling design and needs an explicit retention decision before this pattern is used for sensitive work.
- Category taxonomy from real sales methodology: The 9 categories (Prospecting, Social Selling, Referral Selling, Discovery, Qualification, Objection Handling, Competitive, Closing, Email Drafting) map to the actual stages of an enterprise sales cycle, not generic AI use cases.
Application Features
- Category browser: 9 sales categories with prompt cards showing descriptions, field counts, and preview text. Visual hierarchy makes it easy to find the right prompt for the situation.
- Live prompt preview: As users fill in fields, the complete prompt assembles in real time on the right side of the screen. There's no submit button because the output is always current.
- Full-text search: Fuse.js indexes prompt titles, descriptions, categories, and field names. Search across the entire library from any screen.
- One-click copy and routing: Users can copy the finished prompt or send it directly to ChatGPT, Claude, or Gemini.
- History and favorites: Recently used prompts and starred templates persist locally for quick repeat use.
Tech Stack
Show the tools used on this build
What It Demonstrates
- Enterprise-safe AI enablement: Gives teams a practical way to use AI without sending client data through the wrong system.
- Domain expertise encoded in software: Real sales methodology (MEDDIC, Challenger, SPIN, Sandler) baked into structured templates, not generic prompt suggestions.
- Secure workflow design: The prompt gets built in one tool and executed in the customer's approved model environment.
- Full-stack application: React frontend, Express API, Drizzle ORM, search indexing, and local persistence in one working product.
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Want this pattern in your workflow?
Maps to: Workflow Prototype Sprint (1–2 weeks) or Context Audit (2–4 weeks).
If your team has paid AI seats but the prompts still live in Notion, Prompt Runner shows how I would turn that library into a structured tool: methodology in the template, deal context in the form, and a visible prompt the user can copy or open elsewhere. The engagement would start by scoping the highest-leverage prompts, designing the field structure, and mapping which data may go to which tools.