Works with your AI coding tools
From client call to a Cursor, v0, or Bolt prompt
TaskyTrack turns a client call into a structured product spec and exports it as a ready-to-paste prompt for AI coding tools like Cursor, v0, Bolt.new, Lovable, and Claude Code. Because the prompt carries the full feature list, data schema, and UI direction, the coding tool builds against real requirements instead of a one-line idea. You can also scaffold a starter GitHub repo directly.
Why AI-built apps miss the brief
One-line prompts, generic apps
“Build me a booking app” gets you someone's booking app. The client's actual requirements never made it into the prompt.
Retyping the brief into every tool
The requirements live in call notes, so you rewrite them for every prompt and every tool, losing detail each time.
Context lost between call and code
The data model and edge cases the client mentioned are gone by the time the code is generated.
How it works with AI coding tools
- 1
Capture the client call
Paste a transcript, send the note-taker with a meeting link, or connect your calendar.
- 2
Review the spec
Check the feature breakdown, data schema, and UI direction. Edit anything before code is generated from it.
- 3
Copy it as an AI prompt
Paste the ready-made prompt into Cursor, v0, Bolt.new, Lovable, or Claude Code to generate against the full spec.
- 4
Or scaffold a repo and keep the spec in it
Deploy a starter GitHub repo, or download spec.md and commit it so your coding tool can reference it throughout the build.
Why structure beats a one-line prompt
AI coding tools are only as good as what you tell them. A short prompt forces the tool to invent the details: which pages exist, what data gets stored, how users move between screens. It fills those gaps with generic defaults, which is why so many AI-built prototypes look alike and miss what the client asked for.
A structured spec removes the guessing. The feature list tells the tool what to build, the data schema tells it what to store, and the UI direction tells it how the product should look. You get a first build that reflects the actual conversation, and fewer rounds of correcting the tool afterwards.
Why teams build from a TaskyTrack spec
- Works with any AI coding tool that accepts a prompt or reads files
- spec.md in the repo as persistent project context
- A starter GitHub repo in one click
- Every spec reviewed by a person before code is generated
Frequently asked questions
Which AI coding tools does TaskyTrack work with?
Any tool that accepts a text prompt or reads files in your repo, including Cursor, v0, Bolt.new, Lovable, Claude Code, and Windsurf. TaskyTrack exports the spec as a prompt, as spec.md, or as a starter GitHub repo. It is an export, not a native plugin inside those tools.
Should I use the prompt or spec.md?
Use the copied prompt for one-shot generation in tools like v0, Bolt.new, or Lovable. Commit spec.md to your repo when you use Cursor or Claude Code, so the full spec stays available as context for the whole build.
Does TaskyTrack integrate with Jira or Linear?
Not natively today. The spec downloads as spec.md, which is plain Markdown, so you can paste it into a Linear or Notion document and turn the feature breakdown into tickets from there.
Do I need a paid plan to scaffold a GitHub repo?
GitHub repo scaffolding is included on Pro and Max. On the free plan you can still download spec.md and copy the spec as a prompt for any AI coding tool.
Turn your next client call into a spec
Free to start. No card required.