Visual prompt builder for coding agents

When you’re making large, end-to-end feature changes to a codebase, your coding agent prompt can quickly turn into a fragile wall of text. ProForge helps builders and coders design complex prompts visually - so you can break them into reusable, testable building blocks, preview the final assembled prompt, and iterate faster.

If you use tools like Claude Code or GitHub Copilot, ProForge fits “upstream”: design and maintain the system prompt in ProForge, then copy the composed prompt into your tool of choice.

Overview: What ProForge is

ProForge is a visual prompt builder and IDE for composing, understanding, and debugging large AI prompts. Instead of editing one massive system prompt, you build a prompt graph on a canvas using nodes like:

• Text nodes for instructions, rules, examples, and constraints
• Template nodes to assemble a final prompt using {{variables}}
• Results view to preview and copy the compiled prompt output
• Chat testing to try the prompt against a model without leaving the canvas
• Agent tests to run input/expected-output checks and score results

This is designed for people maintaining prompts that are already “software-like”: long, structured, and constantly changing.

Who this is for

• Developers writing coding-agent system prompts for real projects
• Builders shipping large features (API + UI + tests) with an AI assistant
• Teams who want prompt changes to be modular (not copy/paste chaos)
• Anyone who wants “prompt engineering” to follow software practices (separation of concerns, reuse, testing)

Key benefits (why devs use ProForge)

• Separation of concerns for prompts: Keep “role”, “task”, “constraints”, “repo context”, and “output format” as separate nodes you can swap independently.
• Template variables with live substitution: Use a Template node to assemble a single system prompt from many small parts via {{tags}}.
• Variations for large changes: Try multiple versions of the same prompt (different constraints or plans) side-by-side using Template variations.
• Preview + one-click copy: View the final compiled prompt in Results View and copy as plain text to paste into Claude Code, Copilot Chat, Cursor, ChatGPT, etc.
• Integrated chat testing: Run the prompt against a model inside the canvas using a Chat node.
• Prompt unit testing: Add Agent Tests with input + expected output pairs and get a pass/fail score before you commit to a prompt change.

How it works in ProForge

Below is a practical pattern for “add a new feature end-to-end” prompts, designed like an engineering spec assembled from components.

1. Create your prompt building blocks

On a board, add Text nodes for each concern. Example structure:

• Role & objective (what the agent is)
• Scope / feature definition (what you’re adding)
• Constraints (language, frameworks, style rules, “don’t break X”)
• Repo context (key folders, APIs, conventions, assumptions)
• Plan + execution rules (how to approach changes)
• Output format (what you want back: plan, diffs, files, tests)

Pro tip: ProForge supports // comment lines in Text nodes for notes that you don’t want included in the final composed prompt output.

2. Assemble the system prompt

Add a Template node and connect a Text node that contains your “master template” with placeholders, for example:

You are a senior software engineer AI agent.

Goal:
{{FeatureBrief}}

Repo context:
{{RepoContext}}

Constraints:
{{Constraints}}

Implementation rules:
{{Rules}}

Output format:
{{OutputFormat}}

ProForge detects {{tags}} and creates matching inputs you can connect your other Text nodes to.

3. Preview the compiled prompt

Connect the Template output to a Results View node to:

• See the fully assembled system prompt
• Check length/clarity (word + character counts)
• Copy to clipboard (plain text)

This is typically the moment you paste into Claude Code or GitHub Copilot Chat as your system prompt / initial instruction set, depending on your workflow.

Example: “Add a new feature end-to-end” prompt as modular components

Here’s a concrete way to structure a feature prompt so it scales with complexity.

Components

FeatureBrief

• Feature name, user story, acceptance criteria
• Edge cases
• Non-goals (explicitly what not to do)

RepoContext

• Where API routes live
• UI folder structure
• Testing stack
• Any conventions (naming, error handling)

Constraints

• Language/framework requirements
• Performance/security constraints
• “No breaking changes” or versioning rules

Rules

• Ask clarifying questions if requirements are ambiguous
• Make the smallest viable diff first
• Update tests
• Keep changes consistent with existing patterns

OutputFormat

• First: short plan
• Then: list of files to change
• Then: step-by-step implementation
• Then: test plan

Template node ties it together

The Template becomes your stable “prompt architecture.” The feature changes live in FeatureBrief, and your codebase conventions live in RepoContext/Constraints/Rules - which makes future feature prompts far easier to maintain.