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):

   code

   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:
     code

     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.