Sleap Support

If none exists, create one:

mkdir -p scratch/$(date +%Y-%m-%d)-{issue|discussion}-2512-{short-description}

2. Fetch Post Content

For Issues:

gh issue view NUMBER --repo talmolab/sleap --json number,title,body,author,createdAt,comments,labels > scratch/.../issue.json

For Discussions:

gh api repos/talmolab/sleap/discussions/NUMBER > scratch/.../discussion.json

3. Download Images

Extract image URLs from the post body and download:

# Parse markdown image links: ![alt](url)
grep -oP '!\[.*?\]\(\K[^)]+' scratch/.../issue.json | while read url; do
  wget -P scratch/.../images/ "$url"
done

4. Create USER_POST.md

Convert the JSON to readable markdown with inline images:

# Issue/Discussion #NUMBER: Title

**Author**: @username
**Created**: YYYY-MM-DD
**Platform**: (extract from post if mentioned)
**SLEAP Version**: (extract from post if mentioned)

## Original Post

[post body with images referenced inline]

## Comments

[any replies]

5. Write Investigation README

Create scratch/.../README.md:

# Investigation: Issue/Discussion #NUMBER

**Date**: YYYY-MM-DD
**Post**: https://github.com/talmolab/sleap/{issues|discussions}/NUMBER
**Author**: @username
**Type**: Bug Report | Usage Question | Feature Request

## Summary

[1-2 sentence summary of the issue]

## Key Information

- **Platform**: Windows/macOS/Linux
- **SLEAP Version**: X.Y.Z
- **GPU**: (if relevant)
- **Dataset**: (if described)

## Preliminary Analysis

[Initial thoughts on what might be happening]

## Areas to Investigate

- [ ] Check area 1
- [ ] Check area 2

## Files

- `USER_POST.md` - Original post content
- `images/` - Downloaded screenshots
- `RESPONSE_DRAFT.md` - Draft response (when ready)

6. Check Release History First

Before deep investigation, check if the issue was already fixed:

# Get user's SLEAP version from their post (look for sleap doctor output or sleap.__version__)
USER_VERSION="1.4.0"  # example

# Check sleap releases for fixes
gh release list --repo talmolab/sleap --limit 20
gh release view v1.5.0 --repo talmolab/sleap --json body -q '.body' | grep -i "fix"

# For sleap-io issues
gh release list --repo talmolab/sleap-io --limit 10
gh release view v0.6.0 --repo talmolab/sleap-io --json body -q '.body'

# For sleap-nn/training issues
gh release list --repo talmolab/sleap-nn --limit 10
gh release view v0.2.0 --repo talmolab/sleap-nn --json body -q '.body'

If a fix exists in a newer version:

• Response should guide user to upgrade
• Include the specific version with the fix
• Mention what was fixed (link to PR/issue if available)

If investigating an unfixed bug:

• Checkout the user's version to see their actual code:

# Clone repos if not present
[ -d scratch/repos/sleap-io ] || gh repo clone talmolab/sleap-io scratch/repos/sleap-io
[ -d scratch/repos/sleap-nn ] || gh repo clone talmolab/sleap-nn scratch/repos/sleap-nn

# Checkout user's version
cd scratch/repos/sleap-io && git fetch --tags && git checkout v0.5.0
cd scratch/repos/sleap-nn && git fetch --tags && git checkout v0.1.5

# Now you're looking at the code they're actually running

Use git blame to find potential culprits:

# Find when a suspicious function was last changed
git blame -L 50,100 sleap/io/main.py

# Check if a line was changed recently
git log --oneline -5 -- path/to/file.py

# Find the commit that introduced a specific change
git log -S "function_name" --oneline

7. Analyze and Reproduce

Determine the issue type:

Usage Question: Check if documentation covers this. Common topics:

• Model configuration (skeleton, training params)
• Multi-animal vs single-animal tracking
• Inference and tracking settings
• Data format questions

Bug Report: Try to reproduce. Check:

• Version-specific issues
• Platform-specific behavior
• GPU/CUDA compatibility
• Data corruption signs

Feature Request: Note for tracking, no immediate action needed.

8. Determine Data Needs

When to request SLP file:

• Inference/tracking issues that can't be diagnosed from logs
• "Labels not showing" or display issues
• Merging or import problems
• Corruption or data loss reports

Suggest upload to https://slp.sh - our SLP file sharing service.

If SLP provided: Download and analyze:

sio show path/to/file.slp --summary
sio show path/to/file.slp --videos
sio show path/to/file.slp --skeleton

9. Draft Response

Create RESPONSE_DRAFT.md following this structure:

Hi @{username},

Thanks for the post!

[Restate understanding: "If I understand correctly, you're seeing X when you try to Y..."]

[Provide solution OR request more info]

[If requesting info, give EXPLICIT instructions:]
- Use `sleap doctor` CLI for diagnostics
- Provide copy-paste terminal commands
- Assume non-technical user

Let us know if that works for you!

Cheers,

:heart: Talmo & Claude :robot:

<details>
<summary><b>Extended technical analysis</b></summary>

[Detailed investigation notes, code traces, version checks]

</details>

Response Tone Guidelines

• Opening: Bright and positive ("Thanks for the post!", "Great question!")
• Body: Clear, concise, non-technical language
• Instructions: Step-by-step, assume terminal newbie
• Closing: Encouraging ("Let us know if that works for you!")
• Signature: :heart: Talmo & Claude :robot:

When Requesting User Actions

Terminal commands must be copy-paste ready:

# Good - one-liner, no environment activation needed
sleap doctor

# Good - uses uvx for isolated execution
uvx sio show your_file.slp --summary

# Bad - assumes environment knowledge
source activate sleap && python -c "import sleap; print(sleap.__version__)"

Related Repositories

For data/I/O issues - Check talmolab/sleap-io:

• Local: ../sleap-io (preferred)
• Clone if needed: gh repo clone talmolab/sleap-io scratch/repos/sleap-io
• Key docs: sleap-io/docs/examples.md, sleap-io/docs/formats/SLP.md
• CLI: sio show --help for inspection commands

For training/inference issues - Check talmolab/sleap-nn:

• Local: ../sleap-nn (preferred)
• Clone if needed: gh repo clone talmolab/sleap-nn scratch/repos/sleap-nn
• Topics: Model configs, training params, evaluation metrics, tracking

Common Patterns

Instance Duplication

• Check track assignment logic
• Look for ID switching during tracking
• May need sleap-track with different settings

Training Issues

• GPU memory: suggest reducing batch size
• Loss not decreasing: check learning rate, augmentation
• NaN losses: data normalization issues

Import/Export Issues

• Format compatibility (H5 vs SLP versions)
• Missing video paths
• Skeleton definition mismatches

GUI Issues

• Qt/PySide6 version conflicts
• Display scaling on high-DPI
• Video codec issues

Confirmation Before Posting

ALWAYS confirm with the developer before posting:

1. Show the full draft response
2. Ask: "Does this look good to post?"
3. Wait for explicit approval

Never post automatically - support responses represent the project.