linear-project-creation

name	linear-project-creation
description	Create and refine Linear projects through in-depth interviewing and exploration

Linear Project Creation

Create or refine a Linear project through in-depth interviewing and codebase exploration. This skill requires Linear MCP.

Requirements

• Linear MCP must be configured and authenticated
• User must have access to create/edit projects in Linear

Process

Step 1: Get or Create Linear Project

Ask the user:

• Existing project: Provide the Linear project ID to refine
• New project: Provide a name and team, Claude will create it

If creating new:

1. Use Linear MCP to create the project
2. Return the project ID to the user

If existing:

1. Fetch the project via Linear MCP
2. Read current description and status

Step 2: Interview & Explore Loop

This step repeats until both Claude and the user are satisfied.

2a: Interview

Ask targeted, probing questions in batches of 2-3. Focus on areas that matter for Dynamo projects:

Architecture & Design:

• "Which layer does this change live in - Rust core, Python bindings, or SGLang wrapper?"
• "Does this touch the KV block manager or routing layer?"
• "Will this require changes on both sides of the PyO3 boundary?"

Performance:

• "What is the latency budget for this? Are we on the critical path for TTFT or ITL?"
• "How does this scale with number of concurrent requests / GPUs / nodes?"
• "Is there an existing benchmark we can use to measure the impact?"

Integration & Compatibility:

• "Does this change any interfaces that SGLang or vLLM depend on?"
• "Will this require a coordinated release with SGLang?"
• "Are there existing examples that need to be updated?"

Failure Modes:

• "What happens if this fails at runtime - graceful degradation or hard error?"
• "What is the multi-node failure mode? Can one node fail without affecting others?"
• "How do we detect if this feature is misbehaving in production?"

Testing & Validation:

• "Can this be tested with a single GPU or does it require multi-GPU/multi-node?"
• "Is there an existing integration test (agg.sh/disagg.sh) that covers this path?"
• "How do we verify correctness beyond 'it does not crash'?"

Important guidelines:

• Ask questions in batches of 2-3 (less overwhelming)
• Do not ask things you could figure out by reading the code
• Challenge assumptions - ask "why" and "what if"
• If the user mentions a specific file or component, read it before asking follow-up questions

2b: Explore

Based on the user's answers, gather relevant context:

Codebase exploration (use Dynamo directory map):

• lib/llm/src/ - Rust LLM core
• lib/parsers/src/ - Parser implementations
• lib/bindings/python/ - PyO3 bindings
• components/src/dynamo/sglang/ - SGLang wrapper
• examples/backends/ - Example configurations

Read relevant files, look for existing patterns, check tests, identify interfaces.

Linear exploration:

• Fetch related Linear projects or issues
• Check for prior work or dependencies
• Look at how similar features were scoped

Summarize findings: After exploring, briefly summarize what you learned and how it informs the spec.

2c: Check Satisfaction

Ask the user:

"Based on what we've discussed and what I found, do you have more context to add? Or is there anything else I should explore?"

Exit conditions (move to Step 3 when either is met):

• User signals done: "That's enough" / "Looks good" / "Let's write it up"
• Claude is confident: "I think I have enough context to write a solid spec - ready to proceed?"

If neither condition is met, loop back to 2a with more targeted questions based on gaps identified.

Step 3: Update Linear Project

After the loop completes, update the Linear project description with:

## Problem Statement
[1-2 sentences on what problem this solves]

## Goals
- [Concrete, measurable goal]

## Non-Goals
- [Explicitly out of scope]

## Requirements

### Must Have
- [Requirement with acceptance criteria]

### Nice to Have
- [Lower priority]

## Technical Approach
[High-level approach, not implementation details]
[Include which layers are affected: Rust core / Python bindings / SGLang wrapper / deployment]

## Performance Considerations
[Latency impact, memory impact, scaling characteristics]

## Success Metrics
- [How we measure success - specific numbers where possible]

## Target Release
[Release label, e.g., "Dynamo Releases > Dynamo 0.9.0"]

## Risks & Dependencies
- [Known risks and dependencies]
- [SGLang version requirements if applicable]

## Context Gathered
[Brief summary of relevant code, patterns, or related issues discovered during exploration]

Use Linear MCP to update the project description directly.

Step 4: Confirm

• Show the user the updated project description
• Provide the Linear project URL
• Ask if they want to proceed to creating tickets (linear-project-to-tickets)

Behavior Notes

• Always use Linear MCP - no fallback to local markdown
• Be thorough but not tedious - know when to stop
• Explore proactively - do not wait to be asked
• Keep exploration focused - read relevant files, not the entire codebase
• Summarize findings concisely before asking for more input
• The loop should feel collaborative, not interrogative
• Always ask about target release for labeling

Linear Label Structure

Linear uses nested labels for releases. When applying release labels:

• Labels are organized in groups (e.g., "Dynamo Releases")
• Release labels are children of these groups (e.g., "Dynamo Releases > Dynamo 0.9.0")
• When searching for labels via MCP, look for the parent group first, then the specific release
• Example: To label for Dynamo 0.9.0, find "Dynamo Releases" group, then select "Dynamo 0.9.0"