Claude Code + Linear + Delvyn Studio: The AI-Native Product Workflow
Your AI coding agent can write features. But does it know why you’re building them?
Delvyn Studio Team
Product Team
Create issue in Linear. Open in Claude Code. Let the agent build. Simple, right? Except the agent has no idea why you’re building this feature, what strategic guardrails apply, or what went wrong last time you built something similar.
Three Tools, Three Jobs
Each tool solves a different problem:
- Delvyn Studio — Product thinking. Vision, strategy, discovery, OKRs, and the agent specs that translate all of it into structured instructions.
- Linear — Execution tracking. Issues, cycles, projects, and operational rhythm.
- Claude Code — Implementation. An AI agent that reads your codebase, writes code, runs tests, and ships features.
Together, they create a closed loop: think, specify, build, learn, improve.
The Workflow
Start in Delvyn Studio. Generate an agent spec from a product idea, key result, or discovery finding. The spec bundles feature description, acceptance criteria, strategic context, guardrails, success metrics, and past learnings into one structured document. A briefing packet for an AI audience.
Push the spec to Linear. Open the issue in Claude Code with one keyboard shortcut (Cmd+Option+. or Ctrl+Alt+.). The agent gets full context: description, comments, references, and images. No copy-pasting.
CLAUDE.md + Agent Specs
CLAUDE.md tells Claude Code how to build: coding standards, architecture patterns, test commands. Agent specs tell it what to build and why: strategic context, guardrails, success criteria.
CLAUDE.md says “use the factory pattern for API services.” The agent spec says “build an admin-only settings page that aligns with our OKR to reduce time-to-value.” You need both. Without agent specs, technically sound but strategically wrong. Without CLAUDE.md, strategically aligned but convention-breaking.
The Learning Loop
After the PR merges, Delvyn Studio captures a structured retrospective tied to the original spec. Three ratings on a 1–5 scale: spec quality, context completeness, and implementation accuracy. Plus notes on surprises, edge cases, and process improvements.
These learnings feed into future specs automatically. By the third quarter, your specs anticipate the edge cases that would have been bugs in the first.
In Practice
Your team builds an OKR check-in feature. The workflow:
- Generate an agent spec from the key result “Increase check-in completion to 80%.” It includes a guardrail (complete in under 30 seconds), a discovery insight (users abandon multi-step forms), and a past learning (inline editing beats modals).
- Push to Linear. Open in Claude Code. The agent builds an inline check-in form, adds permission guards, writes tests, verifies the build.
- Capture learnings: spec missed mobile responsiveness (4/5), context was complete (5/5), agent needed one fix on loading states (4/5).
- Next cycle’s spec automatically includes: “Specify mobile responsiveness” and “Define loading state behavior.”
Getting Started
Already using Claude Code and Linear? Add Delvyn Studio to connect the missing layer:
- Set up your vision and OKRs in Delvyn Studio
- Generate an agent spec from your next key result
- Push to Linear, open in Claude Code, let it build
- Complete the learning capture after the PR merges
- Watch your specs improve with every cycle
Connect Product Thinking to Your AI Coding Workflow
Generate agent specs from your vision, strategy, and OKRs — then capture learnings to make every future spec better.