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Anshu Mandal
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Building CrossCap (Open-Source Screen Studio Alternative) with GitHub Copilot Workflows

#devchallenge #githubchallenge #cli #githubcopilot

GitHub Copilot CLI Challenge Submission

This is a submission for the GitHub Copilot CLI Challenge

What I Built

I built CrossCap, a free and open-source desktop app for screen recording and lightweight video editing.

CrossCap is designed for creators and developers who want to make clean product demos and walkthroughs without paying for expensive subscription software. It records your screen/app, then lets you edit the recording with zooms, crop, annotations, backgrounds, and export presets.

What CrossCap does

  • Record screen or app windows
  • Add manual zoom regions with timeline control
  • Auto-suggest zoom regions from cursor telemetry
  • Crop recordings
  • Add text/image/arrow annotations
  • Apply backgrounds (wallpapers, gradients, solid colors, custom image)
  • Trim clip sections
  • Export to MP4 or GIF in multiple aspect ratios/resolutions

Main Editor Window

Properties Window

Tech stack

  • Electron 39 (desktop shell)
  • React 18 + TypeScript + Vite
  • Zustand (editor state)
  • PixiJS + GSAP (canvas preview and zoom animation)
  • WebCodecs + mp4box (MP4 export pipeline)
  • gif.js (GIF export)
  • Biome + Vitest

One of the most interesting parts is that the export pipeline is browser-tech based (WebCodecs + mp4box), so the app can render/edit/export without depending on a heavy native FFmpeg workflow for the main path.

Demo

Project links

Project Website Landing Page

GitHub logo prime399 / CrossCap

Warning

This is very much in beta and might be buggy here and there (but hope you have a good experience!).

CrossCap Logo

Ask DeepWiki

CrossCap

CrossCap is your free, open-source alternative to Screen Studio (sort of).

If you don't want to pay $29/month for Screen Studio but want a much simpler version that does what most people seem to need, making beautiful product demos and walkthroughs, here's a free-to-use app for you. CrossCap does not offer all Screen Studio features, but covers the basics well!

Screen Studio is an awesome product and this is definitely not a 1:1 clone. CrossCap is a much simpler take, just the basics for folks who want control and don't want to pay. If you need all the fancy features, your best bet is to support Screen Studio (they really do a great job, haha). But if you just want something free (no gotchas) and open, this project…


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prime399/CrossCap | DeepWiki

This page describes what CrossCap is, its key features, the main technologies it uses, and a high-level picture of how its three window types and core workflows fit together. For architectural details

favicon deepwiki.com

My Experience with GitHub Copilot CLI (and Copilot Templates)

GitHub Copilot was most useful for speeding up repetitive implementation work while I stayed focused on architecture and correctness.

For a project like CrossCap (Electron + React + canvas rendering + export pipeline), the biggest productivity gains came from combining:

  1. good repository context/instructions
  2. reusable prompt templates
  3. small, scoped prompts for multi-step tasks
  4. manual validation (lint/tests/runtime checks)

How I used Copilot in this project

1) Repository guidance as a “template” for better output

I kept project-specific guidance in a Copilot-friendly instructions file and repo docs (stack, commands, architecture, coding conventions, validation steps).

That mattered a lot because CrossCap has:

  • a multi-window Electron architecture
  • a shared React app with ?windowType= routing pattern
  • a Zustand store with persisted/non-persisted state rules
  • an export pipeline split across orchestrator/decoder/renderer/muxer files
  • security-sensitive IPC handlers in the Electron main process

When Copilot has that context, suggestions are more likely to match the project’s actual patterns instead of generic React/Electron examples.

2) Prompt templates for repeatable tasks

I used a template-style prompting approach for common workflows such as:

  • Refactor a component without changing behavior
  • Add a feature to a timeline/editor panel
  • Generate tests for a pure utility function
  • Document a subsystem before changing it
  • Review a diff for regressions and edge cases

This works especially well in large files/components where vague prompts produce noisy output.

3) Breaking complex work into smaller prompts

For export pipeline and playback work, asking Copilot to solve everything at once was less effective than prompting in phases:

  • inspect current behavior
  • identify likely failure points
  • patch one module
  • add/adjust tests
  • re-check types/lint

That kept the generated changes smaller and easier to review.

4) Using Copilot for “drafting” and me for “decision-making”

Copilot was great for drafting:

  • boilerplate types
  • UI control wiring
  • repetitive handlers
  • initial test cases
  • docs text / checklists

I still made the final calls on:

  • architecture boundaries
  • export pipeline behavior
  • state model changes
  • IPC security constraints
  • performance-sensitive logic

GitHub Copilot Templates I Used (Practical Examples)

Below are the kinds of template prompts that worked well for this codebase.

A) Feature implementation template (timeline/editor) 

Task: Add/modify a feature in CrossCap's video editor.

Context:
- Stack: Electron + React + TypeScript + Zustand
- State source of truth: useEditorStore (avoid introducing local state for persisted editor settings)
- UI domain: src/components/video-editor/
- Timeline components live under src/components/video-editor/timeline/
- Keep imports using @/ aliases
- Preserve existing behavior unless requested

Steps:
1. Identify the exact file(s) responsible.
2. Explain the current flow briefly.
3. Propose a minimal patch.
4. Implement only the requested change.
5. List validations to run (types/lint/tests if applicable).
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Advantage: this reduces “AI wandering” and keeps edits aligned with the project’s state and folder conventions.

B) Export pipeline bugfix template 

Investigate a bug in CrossCap export (MP4/GIF).

Constraints:
- Export orchestration is in lib/exporter/exportOrchestrator.ts
- Shared frame rendering logic is in lib/exporter/frameRenderer.ts
- Prefer minimal changes
- Do not change output format behavior unless necessary
- Explain root cause before patching

Output format:
- Root cause
- Patch summary
- Risks / edge cases
- Validation steps
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Advantage: this makes Copilot focus on diagnosis first, not speculative rewrites.

C) Test generation template (utility functions) 

Write Vitest tests for a pure TypeScript utility in CrossCap.

Requirements:
- Test behavior, not implementation details
- Include edge cases and invalid inputs if the function handles them
- Keep tests colocated and readable
- Do not mock internal logic unnecessarily

First, list test cases. Then write the tests.
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Advantage: better test coverage and fewer brittle tests.

Why Copilot Was Useful Here (Research-backed + Practical)

I wanted this write-up to be more than personal opinion, so I checked both GitHub Copilot docs (via Context7) and broader sources via Exa.

What I found in the docs (Context7 + GitHub Docs)

GitHub’s recent Copilot documentation increasingly emphasizes customization, not just autocomplete:

  • Repository-wide custom instructions (.github/copilot-instructions.md) for persistent project guidance
  • Path-specific instructions (.instructions.md) for targeted rules
  • Prompt files (reusable prompts for common tasks)
  • Custom agents / agent profiles for specialized workflows

That maps directly to what helps in a codebase like CrossCap: encode the architecture and conventions once, then reuse prompt patterns across feature work.

Best Copilot use cases in this project

  • UI scaffolding for editor controls and settings panels
  • Refactoring assistance in large React/TS components
  • Test drafting for pure utility logic in the export subsystem
  • Documentation and internal notes for subsystems before changes
  • Small migration chores (types, props, repetitive handlers)

Where human judgment mattered most

  • performance-sensitive video/export behavior
  • Electron IPC and security boundaries
  • state persistence/rehydration logic
  • avoiding regressions in timeline + playback interactions

What Worked Best (Practical Tips)

1) Give Copilot architecture before asking for code

The better the project context, the better the suggestions. In CrossCap, naming the exact subsystem (timeline, export orchestrator, store, IPC handler) improved results immediately.

2) Ask for a patch plan first on risky changes

For export pipeline or state persistence changes, asking for a brief diagnosis + plan before code avoided over-large patches.

3) Use reusable prompt templates

Prompt templates made my Copilot sessions more consistent, especially when returning to the project after a break.

4) Validate every generated change

Copilot accelerates implementation, but lint/tests/runtime checks are still the source of truth.

Final Thoughts

GitHub Copilot was most valuable on CrossCap when I treated it as a developer accelerator, not an autopilot:

  • I provided clear project conventions and architecture context
  • I reused prompt templates for recurring workflows
  • I kept prompts small and task-specific
  • I validated everything against the real app behavior

That combination made it much easier to move faster on a complex desktop app with a custom editor and export pipeline while keeping the codebase coherent.

If you’re building a medium-to-large app, my biggest recommendation is simple: invest in instructions + prompt templates first. The quality of Copilot output improves dramatically when the tool understands how your project is supposed to work.

References (researched with Context7 + Exa)

GitHub Docs / Copilot docs

Research / productivity & quality writeups

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