Scope: Generate Implementation-Ready Linear Tickets From Your Codebase

If you've worked on a team using Linear (or any issue tracker, really), you've probably lived through this cycle:

1. A PM or engineer writes a ticket: "Add email notifications for orders"
2. A developer reads the ticket, opens the codebase, and spends 30 minutes figuring out what files are involved
3. If the team uses AI tools like Claude Code or Cursor, the agent burns 10K+ tokens just exploring before writing a single line
4. The feature ships incomplete because the ticket didn't mention the three existing models that needed callbacks, the notification preference system that should have been wired in, or the admin endpoints for managing templates

The gap between product intent and engineering execution is where most teams lose time. Not in the coding — in the clarification loops. "Where does this live in the codebase?" "What models are affected?" "Does this need a migration?" "What about the existing notification system?"

Scope is a tool built to eliminate that gap. You connect your GitHub repo, describe a feature in plain English, and it generates Linear-ready tickets loaded with implementation context.

What "Implementation-Ready" Actually Means

A typical Linear issue has a title and a human-written description. Scope generates tickets with 32 structured fields, including:

• Exact files to create and modify — not "update the backend," but app/services/order_notification_service.rb (create), app/models/order.rb (modify)
• Related entities and endpoints — which data models are involved and how they connect
• Schema changes — what migrations are needed
• Dependencies — which tickets must ship first and why
• Acceptance criteria — verifiable conditions, not vague "it should work"
• Implementation plan — ordered steps within each ticket

You can export these directly to Linear via CSV import or copy as Markdown.

How It Works Under the Hood

When you connect your GitHub repo, Scope runs a three-layer analysis:

1. Tree-sitter AST Parsing

Scope extracts entities, endpoints, relationships, and routes from your actual code using deterministic AST parsing. No AI involved at this stage — just fast, reliable static analysis. It supports Ruby, Python, TypeScript, Go, Rust, and more.

2. Dependency Graph + PageRank

It builds a graph of how your code connects and ranks symbols by importance using PageRank. When you ask for a feature, Scope already knows which services are central to your architecture and which are peripheral.

3. LLM Semantic Interpretation

Claude Sonnet interprets the structural data to understand what your code does in business terms — what patterns it follows, what conventions to respect. This is where raw code structure becomes actionable project context.

This analysis runs once and produces a living model of your codebase. Scope never stores your source code — just the extracted metadata. When you generate tickets later, it pulls the relevant files from GitHub in real-time so tickets always reflect your current code.

Then you describe what you want:

"Add transactional email notifications for order lifecycle events, budget alerts, and supplier connections."

And Scope decomposes that into ordered, dependency-aware tickets with full codebase context baked in.

Why This Matters for AI-Assisted Development

This is where Scope gets particularly interesting for vibe coding workflows.

If your developers use Claude Code, Cursor, or Codex, the biggest time sink isn't writing code — it's the AI agent exploring your codebase to build context before it can do anything useful. Scope short-circuits that entirely.

It also exposes 12 MCP tools that let an AI agent pull ticket context, entity details, and dependency information directly while coding. The agent doesn't explore your codebase blind — it already knows the architecture.

The creator dogfooded this on their own project: two sentences generated 5 ordered tickets. Claude Code executed them and produced 42 files, 5 services, 72 tests, and a full GraphQL API — with zero human steering during implementation. The callbacks, lifecycle hooks, and integration points that AI normally misses were all captured in the ticket decomposition.

The Bigger Picture

Vibe coding has shifted the bottleneck. Writing code is fast now. What's slow is the translation layer between "what we want to build" and "a task description detailed enough that a human or AI can actually ship it."

Tools like Scope suggest a future where the planning phase itself becomes AI-augmented — not by replacing human judgment on what to build, but by automating the tedious mapping of how it connects to what already exists.

If your team spends more time clarifying tickets than executing them, this is worth a look.