linear-type-labeler
Skill4 starsChanged 10 months agoApache-2.0View on GitHub
Classify and apply Type labels to Linear issues based on their title and description.
In this repository7 other files it gives its agents.
- AGENTS.mdAGENTS.md
- contributing-mdSkill
- daily-updateSkill
- linear-initiativeSkill
- linear-sdk-telemetry-labelerSkill
- sdk-feature-implementationSkill
- span-convention-reviewSkill
The file
We split this skill into the rules it gives an agent
10 rules. About 0 are common, 6 are distinctive, and 4 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈6
Distinctive · estimate
This author's own choices. Worth a look.
4
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 47
Use query_data to fetch workspace-level labels and build a UUID map:
- Distinctive: line 55
If the workspace has no "Type" label group, surface that to the user before proceeding.
- Distinctive: line 59
If in mode A (list of IDs): use the already-fetched issues from Step 1.
- Very specific: line 62
If in mode B (team): use query_data to list issues for the team, including each issue's label IDs.
- Distinctive: line 89
When ambiguous between Feature and Improvement: does it add something that doesn't exist at all, or make something existing better?
- Distinctive: line 92
When ambiguous between Task and Improvement: is there a user-visible benefit (better performance, reliability, DX, API ergonomics)?
- Distinctive: line 112
Ask: *"Should I apply these labels?
- Distinctive: line 115
If the user provides corrections, update the table and show the revised plan before proceeding.
- Very specific: line 121
Call get_issue to fetch the issue's current label IDs immediately before writing
- Very specific: line 123
Call save_issue with the full combined label set
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Linear Type Labeler
Classifies Linear issues and applies a Type label from the Sentry workspace's label taxonomy based on the content of each issue's title and description.
Requirements
This skill requires the Linear MCP server to be configured.
Type Label Reference
Type labels in the Sentry workspace (workspace-level, not per-team):
| Label | When to use |
| Bug | Something is broken, regression, error, crash, incorrect behavior |
| Feature | New capability, integration, API, SDK support — something that doesn't exist yet |
| Improvement | Making existing functionality better — performance, DX, UX, reliability |
| Task | Chore, investigation, spike, migration, cleanup, operational work |
| Tracking Issue | Parent/umbrella tracking a larger body of work; has sub-issues or a checklist |
| Docs | Documentation, RFC, write-up, changelog, guide |
| Tests | Writing or fixing tests, coverage, test infrastructure |
Workflow
Step 1 — Identify issues to process
Two modes:
A — List of Linear IDs provided (e.g. SDK-123, SDK-456):
- Fetch each issue by ID using
get_issue. Skip the team-fetch step entirely.
B — No IDs provided:
- Infer the team: (1) current GitHub repository name — for the
getsentryorg, the repo name matches the Linear team name, (2) explicitly stated by the user, (3) if still ambiguous, ask.
Step 2 — Fetch label IDs
Use query_data to fetch workspace-level labels and build a UUID map:
query_data: fetch all labels for the workspace (not the team)
→ filter to those whose parent label name is "Type"
→ build a map: { "Bug": "<uuid>", "Feature": "<uuid>", ... }If the workspace has no "Type" label group, surface that to the user before proceeding.
Step 3 — Fetch and filter issues
If in mode A (list of IDs): use the already-fetched issues from Step 1. Filter client-side: keep only those where none of the issue's label IDs appear in the Step 2 UUID map values.
If in mode B (team): use query_data to list issues for the team, including each issue's
label IDs. Paginate (limit 50, use cursor) until done. Filter client-side: keep only issues
where none of the issue's label IDs appear in the Step 2 UUID map values (i.e., no Type label
assigned yet).
Announce the total candidate count before continuing. If there are more than 25 candidates, process in chunks of 25, confirming each chunk before the next.
Step 4 — Classify each issue
For each candidate issue, read its title and description. Apply heuristics in priority order — stop at the first match:
- Title or description is clearly about testing work: mentions "test", "tests", "coverage", "test infra", "test suite", "unit test", "integration test" → Tests
- Title or description is clearly about documentation: mentions "docs", "README", "changelog", "RFC", "spec", "guide", "write-up", "develop.sentry.dev" → Docs
- Title contains
[META],[EPIC], "Track", "Tracking"; or description has a checklist of Linear/GitHub issue links → Tracking Issue - Title starts with "Fix", "Fixes", "Broken", "Error", "Crash", "Regression" → Bug
- Title starts with "Improve", "Optimize", "Enhance"; or is a refactor with user-visible impact (changes behavior, performance, or API ergonomics) → Improvement
- Title starts with "Investigate", "Spike", "Migrate", "Update", "Clean up", "Chore"; or is a refactor with no user-visible change → Task
- Title starts with "Add", "Support", "Implement", "New", "Introduce"; or adds something that doesn't exist yet → Feature
When ambiguous between Feature and Improvement: does it add something that doesn't exist at all, or make something existing better? Former → Feature, latter → Improvement.
When ambiguous between Task and Improvement: is there a user-visible benefit (better performance, reliability, DX, API ergonomics)? If yes → Improvement. If it's purely internal with no user-visible impact → Task.
Assign exactly one Type label. Mark confidence:
- High — clear signal in title or description
- Low — genuinely ambiguous; flag for human review, don't guess
Step 5 — Present the plan
Show a summary table before writing anything:
| Issue | Title | Proposed Type | Confidence |
|---------|------------------------|---------------|---------------------|
| SDK-123 | Fix crash on startup | Bug | High |
| SDK-456 | Add OAuth support | Feature | High |
| SDK-789 | Some vague title | Task | Low — please review |Ask: "Should I apply these labels? Reply with any corrections (e.g. 'SDK-789 → Bug') and I'll update the plan before applying."
If the user provides corrections, update the table and show the revised plan before proceeding.
Step 6 — Apply labels
For each approved classification:
- Call
get_issueto fetch the issue's current label IDs immediately before writing - Append the Type label UUID from the Step 2 map
- Call
save_issuewith the full combined label set
Always re-fetch and always pass the full combined set. Linear replaces labels on update — labels added between Step 3 and now would be silently stripped if you use the cached label list.
Report as you go: "SDK-123 → Bug", "SDK-456 → Feature".
Edge Cases
- Issue already has a Type label: Skip it; don't overwrite human classifications.
- Very short title, no description: Flag as low-confidence; surface for human review.
- Issue belongs to multiple types: Pick the primary type (bug fix with a docs update → Bug).
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

