docs: enforce branch protection rules (#30)

* docs: add mandatory branch/PR rule to CLAUDE.md

Adds explicit instruction that all changes must go through feature
branches and pull requests. enforce_admins has been enabled on GitHub
to prevent admin bypass of branch protection.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* fix(modal): remove unused os import

Fixes ruff F401 lint error on modal_cloud.py.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>

Author: abrichr

CLAUDE.md

@@ -1,5 +1,19 @@
 # Claude Context for openadapt-ml
 
+## MANDATORY: Branches and Pull Requests
+
+**NEVER push directly to main. ALWAYS use feature branches and pull requests.**
+
+1. Create a feature branch: `git checkout -b feat/description` or `fix/description`
+2. Make commits on the branch
+3. Push the branch: `git push -u origin branch-name`
+4. Create a PR: `gh pr create --title "..." --body "..."`
+5. Only merge via PR (never `git push origin main`)
+
+This is a hard rule with NO exceptions, even for "small" changes.
+
+---
+
 ## Simplicity Guidelines
 
 **Philosophy**: "Less is more. 80/20 impact/complexity. Working code beats elegant design."

openadapt_ml/cloud/modal_cloud.py

@@ -38,7 +38,6 @@
 
 import argparse
 import json
-import os
 import subprocess
 import sys
 import tempfile
@@ -387,7 +386,9 @@ def infer(
                     text_content = msg["content"]
                     # Replace <image> tag in text with proper multi-modal format
                     if "<image>" in text_content:
-                        text_content = text_content.replace("<image>\n", "").replace("<image>", "")
+                        text_content = text_content.replace("<image>\n", "").replace(
+                            "<image>", ""
+                        )
                     msg["content"] = [
                         {"type": "image"},
                         {"type": "text", "text": text_content},
@@ -404,9 +405,7 @@ def infer(
                 text=[text], images=[image], return_tensors="pt", padding=True
             )
         else:
-            inputs = infer._processor(
-                text=[text], return_tensors="pt", padding=True
-            )
+            inputs = infer._processor(text=[text], return_tensors="pt", padding=True)
 
         inputs = inputs.to(infer._model.device)