Feedback To Linear

Feedback to Linear

Transform raw user feedback text into structured Linear issues with intelligent AI parsing.

Triggers

Activate this skill with any of these phrases:

• "Convert this feedback to Linear issues"
• "Create issues from user feedback"
• "feedback-to-linear"
• "Parse feedback for Linear"
• "Transform feedback into Linear"

Quick Reference

Agent Behavior Contract

When this skill is invoked, you MUST:

1. Never assume context - Always fetch teams, projects, and labels dynamically from Linear
2. Single workspace - Issues are created in the workspace associated with the Linear MCP plugin's API key
3. Auto-detect repo context - If in a git repo, automatically use project info (no prompt)
4. Use existing labels only - Never create new labels; only match to fetched labels
5. Default to Backlog - New issues start in "Backlog" or "Todo" state unless specified
6. Batch process - Parse all feedback items together, then create all at once
7. Preserve user voice - Keep original feedback wording in descriptions
8. Conditional confirmation - Only prompt if LOW confidence items exist

Process

Phase 1: Input Collection

Objective: Gather feedback, detect context, and select target location in Linear.

Steps:

1. Load all MCP tools upfront (batch in parallel):

   MCPSearch("select:mcp__plugin_linear_linear__list_teams")
   MCPSearch("select:mcp__plugin_linear_linear__list_projects")
   MCPSearch("select:mcp__plugin_linear_linear__list_issue_labels")
   MCPSearch("select:mcp__plugin_linear_linear__create_issue")
   

2. Prompt user for feedback text (support multi-line, multiple items, inline URLs)

   • Users can include screenshot/video URLs directly in feedback
   • URLs are auto-extracted during parsing

3. Auto-detect repo context (no prompt):

   • Check if current directory is a git repo (git rev-parse --git-dir)
   • If yes, detect:
     • Project name from package.json, Cargo.toml, pyproject.toml, or git remote
     • Platform from project structure (ios/, android/, package.json dependencies, etc.)
     • Repo URL from git config --get remote.origin.url
   • Display detected context as informational message:

     Detected: [project-name] (iOS) - github.com/user/repo
     

4. Fetch Linear data (can run in parallel):

   • mcp__plugin_linear_linear__list_teams to get available teams
   • After team is known: fetch projects and labels for that team

5. Single combined question using AskUserQuestion with 3 questions:

   • Team: "Which team?" (required, single select)
     • If repo name matches a team, note it
   • Platform: "Platform?" (single select)
     • Options: iOS, Android, Web, Backend/API, Multiple
     • If detected from repo, set as default
   • Project: "Project?" (optional, single select)
     • Include "None/Backlog" option
     • If repo name matches a project, highlight it

6. Fetch labels for selected team:

   • mcp__plugin_linear_linear__list_issue_labels

Inputs: User feedback text Outputs: Validated team/project, platform context, available labels, repo context Verification: Team ID is valid, labels fetched successfully

Phase 2: AI Parsing (Batch)

Objective: Extract structured issue data from raw feedback using AI.

Steps:

1. Split feedback into individual items (by line breaks, blank lines, or numbered lists)

2. For each feedback item, extract:

   • Title: Imperative, actionable, <80 chars, include specifics
   • Description: Original feedback + context + estimate note (markdown formatted)
   • Labels: Semantically match to fetched labels using compound signals
   • Priority: 1-4 based on multi-signal inference (default: 3)
   • Acceptance Criteria: 3-5 testable items in markdown checklist format
   • Estimate: XS/S/M/L/XL complexity (appended to description)
   • Confidence: HIGH/MEDIUM/LOW for each field
   • Links: Auto-extract URLs from feedback text

Label Matching Guidelines:

• Use compound signal detection (see parsing guidelines)
• Consider label descriptions, not just names
• Domain-aware: prioritize platform labels matching detected repo
• Match based on >70% semantic confidence
• Maximum 3-4 labels per issue

Title Convention:

• When platform is selected (not "Multiple"), prefix with [Platform]
• Include specific details from feedback (device, size, action)
• Avoid generic titles like "Fix bug" or "Add feature"
• Examples:
  • "[iOS] Fix crash when uploading large images on iPhone 14"
  • "[Android] Add dark mode toggle in settings"

Priority Detection (Multi-Signal):

• Base priority: 3 (Medium) - most feedback deserves attention
• Adjust up/down based on signals:

• Priority 1 (Urgent): crash + many users, security, data loss
• Priority 2 (High): blocking core flow, explicit urgency
• Priority 3 (Medium): default, standard bugs/features
• Priority 4 (Low): cosmetic, minor polish

Confidence Scoring:

Description Format:

[Original user feedback, quoted or paraphrased]

## Context
[Inferred context + device/platform details]
[If repo detected: **Source repo:** project-name (repo-url)]

**Complexity Estimate:** M (Medium)

## Links
- [Screenshot](https://d.pr/abc123)
- [src/Component.tsx](https://github.com/user/repo/blob/main/src/Component.tsx)

## Acceptance Criteria
- [ ] Criterion 1
- [ ] Criterion 2
- [ ] Criterion 3

Inputs: Feedback items, available labels, repo context Outputs: Structured issue data with confidence scores Verification: All items have title, description, valid labels

Phase 3: Creation & Confirmation

Objective: Preview parsed issues and create them in Linear.

Steps:

1. Display preview table:

   Title	Labels	Pri	Est	Confidence
   [iOS] Fix crash on upload	Bug, iOS	2	M	HIGH
   Improve settings [?]	Enhancement	3	M	LOW ⚠️

2. Conditional confirmation:

   • If ALL items have HIGH or MEDIUM confidence → create immediately (no prompt)
   • If ANY item has LOW confidence → prompt with AskUserQuestion:
     • "Create N issues? (X items flagged for review)"
     • Options: "Create all", "Edit flagged items", "Cancel"

3. If editing:

   • Allow inline edits: "Change issue 2 title to: [new title]"
   • Re-display preview after edits
   • Then create

4. Create issues:

   • For each parsed issue, call mcp__plugin_linear_linear__create_issue
   • Include: title, team, project (if set), labels, priority, description

5. Display summary:

   Issue	URL
   MOB-123	https://linear.app/team/issue/MOB-123

Inputs: Parsed issue data, user confirmation (if needed) Outputs: Created Linear issues with URLs Verification: All issues created successfully

Anti-Patterns

Verification Checklist

Before completing this skill, verify:

• All issues created with valid team assignment
• Labels match existing workspace labels (no new labels created)
• Confirmation only prompted if LOW confidence items exist
• Summary with issue URLs provided
• Acceptance criteria formatted as markdown checklist
• Priority values are 1-4 (not 0 unless truly ambiguous)
• Estimate included in description
• Confidence scores assigned (HIGH/MEDIUM/LOW)
• LOW confidence items flagged with [?] in title
• Repo context auto-detected and used (if in git repo)
• URLs auto-extracted from feedback text

Extension Points

This skill can be extended to:

1. Duplicate detection - Check for similar existing issues before creating
2. Assignee inference - Auto-assign based on feedback source or label
3. Cycle assignment - Automatically add to current cycle
4. Parent issues - Group related feedback under epic/parent

References

See references/ai-parsing-guidelines.md for detailed semantic matching rules and examples.

Source: 0x7067/claude-code-setup — distributed by TomeVault.