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

Aspect	Details
Input	Raw feedback text (batch) + team/project selection
Output	Linear issues with AI-parsed metadata (title, labels, priority, acceptance criteria, estimates, links)
Workspace	Uses workspace from configured Linear API key
Mode	Batch processing with conditional confirmation
Duration	~1-2 minutes for 5-10 feedback items

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:

Signal Type	+1 Priority	-1 Priority
User impact	"many users", "everyone", "all"	"sometimes", "rarely", "edge case"
Business	"can't use", "blocking", "revenue"	"cosmetic", "minor", "nice to have"
Severity	crash, data loss, security	typo, color, alignment
Tone	ALL CAPS, !!!, frustrated	casual suggestion

• 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:

Field	HIGH	MEDIUM	LOW
Title	Clear action + specific issue	Transformed, some ambiguity	Vague, add [?] suffix
Labels	Exact compound match	Semantic inference	Weak match
Priority	Multiple strong signals	Some signals	Defaulted
Estimate	-	Always MEDIUM	-

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

Avoid	Why	Instead
Creating labels	May not match team conventions	Use existing labels only
Hardcoding labels	Different workspaces have different labels	Fetch dynamically
Asking about repo context	Adds unnecessary prompt	Auto-detect silently
Asking about media URLs	Adds unnecessary prompt	Auto-extract from feedback
Always prompting to confirm	Slows down workflow	Only prompt if LOW confidence
Priority 0 as default	Most feedback deserves attention	Default to 3 (Medium)
Generic titles	Hard to scan/triage	Include specifics from feedback

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.