PyTorch Issue Triage Skill

This skill helps triage GitHub issues by routing issues, applying labels, and leaving first-line responses.

MCP Tools Available
Labels You Must NEVER Add
Issue Triage Steps
- Step 0: Already Routed — SKIP
- Step 1: Question vs Bug/Feature
- Step 1.5: Needs Reproduction — External Files
- Step 2: Transfer
- Step 2.5: PT2 Issues — Special Handling
- Step 3: Redirect to Secondary Oncall
- Step 4: Label the Issue
- Step 5: High Priority — REQUIRES HUMAN REVIEW
- Step 6: bot-triaged (automatic)
- Step 7: Mark Triaged
V1 Constraints

Labels reference: See labels.json for the full catalog of 305 labels suitable for triage. ONLY apply labels that exist in this file. Do not invent or guess label names. This file excludes CI triggers, test configs, release notes, and deprecated labels.

PT2 triage guide: See pt2-triage-rubric.md for detailed labeling guidance when triaging PT2/torch.compile issues.

Response templates: See templates.json for standard response messages.

MCP Tools Available

Use these GitHub MCP tools for triage:

Tool	Purpose
`mcp__github__issue_read`	Get issue details, comments, and existing labels
`mcp__github__issue_write`	Apply labels or close issues
`mcp__github__add_issue_comment`	Add comment (only for redirecting questions)
`mcp__github__search_issues`	Find similar issues for context

Labels You Must NEVER Add

Prefix/Category	Reason
Labels not in `labels.json`	Only apply labels that exist in the allowlist
`ciflow/*`	CI job triggers for PRs only
`test-config/*`	Test suite selectors for PRs only
`release notes: *`	Auto-assigned for release notes
`ci-`, `ci:`	CI infrastructure controls
`sev*`	Severity labels require human decision
`merge blocking`	Requires human decision
Any label containing "deprecated"	Obsolete
`oncall: releng`	Not a triage redirect target. Use `module: ci` instead

If blocked: When a label is blocked by the hook, add ONLY triage review and stop. A human will handle it.

These rules are enforced by a PreToolUse hook that validates all labels against labels.json.

Never Override Human Labels

If a human has already applied labels (especially ci: sev, severity labels, or priority labels), do NOT remove or replace them. Your job is to supplement, not override.

Issue Triage (for each issue)

0) Already Routed — SKIP

If an issue already has ANY oncall: label, SKIP IT entirely. Do not:

Add any labels
Add triaged
Leave comments
Do any triage work

That issue belongs to the sub-oncall team. They own their queue.

1) Question vs Bug/Feature

If it is a question (not a bug report or feature request): close and use the redirect_to_forum template from templates.json.
If unclear whether it is a bug/feature vs a question: request additional information using the request_more_info template and stop.

1.5) Needs Reproduction — External Files

Check if the issue body contains links to external files that users would need to download to reproduce.

Patterns to detect:

File attachments: .zip, .pt, .pth, .pkl, .safetensors, .onnx, .bin files
External storage: Google Drive, Dropbox, OneDrive, Mega, WeTransfer links
Model hubs: Hugging Face Hub links to model files

Action:

Edit the issue body to remove/redact the download links
- Replace with: [Link removed - external file downloads are not permitted for security reasons]
Add needs reproduction label
Use the needs_reproduction template from templates.json to request a self-contained reproduction
Do NOT add triaged — wait for the user to provide a reproducible example

1.55) Needs Reproduction — Other Cases

Also add needs reproduction when:

The user reports a hardware-specific issue (e.g., specific GPU model) without a self-contained repro script
The user references a specific model/checkpoint/dataset that is not publicly runnable in a few lines
The issue describes version-upgrade breakage but only provides a high-level description without a minimal script
The repro depends on a specific training setup, distributed environment, or non-trivial infrastructure

1.6) Edge Cases & Numerical Accuracy

If the issue involves extremal values or numerical precision differences:

Patterns to detect:

Values near torch.finfo(dtype).max or torch.finfo(dtype).min
NaN/Inf appearing in outputs from valid (but extreme) inputs
Differences between CPU and GPU results
Precision differences between dtypes (e.g., fp32 vs fp16)
Fuzzer-generated edge cases

IMPORTANT — avoid keyword-triggered mislabeling:

Label based on the root cause, not keywords that appear in the error or title. A keyword tells you what failed, not why.

An undefined symbol: ncclAlltoAll error at import torch is a packaging issue (module: binaries), not a distributed training bug — the user never ran distributed code.
A nan in a parameter name or tolerance check is not module: NaNs and Infs unless the bug is actually about NaN propagation.
A stack trace mentioning autograd does not mean module: autograd — check whether the bug is in autograd itself or just on the call path.
A test failure with tolerance thresholds is module: tests, not module: numerical-stability.

Ask: "Where would the fix need to be made?" That determines the label.

Action:

Add module: edge cases label
If from a fuzzer, also add topic: fuzzer
Use the numerical_accuracy template from templates.json to link to the docs
If the issue is clearly expected behavior per the docs, close it with the template comment

2) Transfer (domain library or ExecuTorch)

If the issue belongs in another repo (vision/text/audio/RL/ExecuTorch/etc.), transfer the issue and STOP.

2.5) PT2 Issues — Special Handling

PT2 is NOT a redirect. oncall: pt2 is not like the other oncall labels in Step 3. PT2 issues continue through Steps 4–7 for full triage — add oncall: pt2, then proceed to label with module: labels, mark triaged, etc.

See pt2-triage-rubric.md for detailed labeling decisions on which module: labels to apply.

3) Redirect to Secondary Oncall

CRITICAL: When redirecting issues to a non-PT2 oncall queue, apply exactly one oncall: ... label and STOP. Do NOT:

Add any module: labels
Mark it triaged
Do any further triage work

The sub-oncall team will handle their own triage. Your job is only to route it to them.

Oncall Redirect Labels

Label	When to use
`oncall: jit`	TorchScript issues
`oncall: distributed`	Distributed training (DDP, FSDP, RPC, c10d, DTensor, DeviceMesh, symmetric memory, context parallel, pipelining)
`oncall: export`	torch.export issues
`oncall: quantization`	Quantization issues
`oncall: mobile`	Mobile (iOS/Android), excludes ExecuTorch
`oncall: profiler`	Profiler issues (CPU, GPU, Kineto)
`oncall: visualization`	TensorBoard integration

Common routing mistakes to avoid:

MPS ≠ Mobile. MPS (Metal Performance Shaders) is the macOS/Apple Silicon GPU backend. Do NOT route MPS issues to oncall: mobile. MPS issues stay in the general queue with module: mps.
DTensor → oncall: distributed. DTensor issues should always be routed to oncall: distributed, even if they don't mention DDP/FSDP.
ONNX → module: onnx. There is no oncall: onnx. Use module: onnx and keep in the general queue.
CI/releng → module: ci. Do not use oncall: releng. Use module: ci for CI infrastructure issues.
torch.compile + distributed. When torch.compile mishandles a distributed op (e.g., dist.all_reduce), the issue typically needs BOTH oncall: pt2 and oncall: distributed since the fix may span both codebases.

Note: oncall: cpu inductor is a sub-queue of PT2. For general triage, just use oncall: pt2.

4) Label the issue (if NOT transferred/redirected)

Only if the issue stays in the general queue:

Add 1+ module: ... labels based on the affected area
Prefer specific labels over general ones when both exist. Check labels.json descriptions for guidance on when a specific label supersedes a general one (e.g., module: sdpa instead of module: nn for SDPA issues, module: flex attention instead of module: nn for flex attention).
feature — wholly new functionality that does not exist today in any form
enhancement — improvement to something that already works (e.g., adding a native backend kernel for an op that already runs via fallback/composite, performance optimization, better error messages). If the enhancement is about performance, also add module: performance.
function request — a new function or new arguments/modes for an existing function
If the issue says the operation "currently works" or "falls back to" a slower path, that is enhancement, not feature

Commonly missed labels — always check for these:

Condition	Label
Segfault, illegal memory access, SIGSEGV	`module: crash`
Performance issue: regression, slowdown, or optimization request	`module: performance`
Issue on Windows	`module: windows`
Previously working feature now broken	`module: regression`
Broken docs/links that previously worked	`module: docs` + `module: regression` (NOT `enhancement`)
Issue about a test failing (not the underlying functionality)	`module: tests`
Backward pass / gradient computation bug	`module: autograd` (in addition to the op's module label)
`torch.linalg` ops or linear algebra ops (solve, svd, eig, inv, etc.)	`module: linear algebra`
`has workaround`	Only add when the workaround is non-trivial and non-obvious. If the issue is "X doesn't work for non-contiguous tensors," calling `.contiguous()` is the tautological inverse of the bug, not a workaround. A real workaround is something like installing a specific package version, adding a synchronization point, inserting `gc.collect()`, or using a different API that isn't obviously implied by the bug description.

Label based on the actual bug, not keywords. Read the issue to understand what is actually broken. A bug about broadcasting that happens to mention "nan" in a parameter name is a frontend bug, not a NaN/Inf bug.

5) High Priority — REQUIRES HUMAN REVIEW

CRITICAL: If you believe an issue is high priority, you MUST:

Add triage review label and do not add triaged

Do NOT directly add high priority without human confirmation.

High priority criteria:

Crash / segfault / illegal memory access
Silent correctness issue (wrong results without error)
Regression from a prior version
Internal assert failure
Many users affected
Core component or popular model impact

6) bot-triaged (automatic)

The bot-triaged label is automatically applied by a post-hook after any issue mutation. You do not need to add it manually.

7) Mark triaged

If not transferred/redirected and not flagged for review, add triaged.

V1 Constraints

DO NOT:

Close bug reports or feature requests automatically
Close issues unless they are clear usage questions per Step 1
Assign issues to users
Add high priority directly without human confirmation
Add module labels when redirecting to oncall
Add comments to bug reports or feature requests, except a single info request when classification is unclear

DO:

Close clear usage questions and point to discuss.pytorch.org (per step 1)
Be conservative - when in doubt, add triage review for human attention
Apply type labels (feature, enhancement, function request) when confident
Add triaged label when classification is complete

Note: bot-triaged is automatically applied by a post-hook after any issue mutation.

triaging-issues