Introduce app_asset_contents table as a read-through cache over S3 for
text-like asset files (e.g. .md skill documents). This eliminates N
individual S3 fetches during SkillBuilder builds — bulk_load pulls all
content in a single SQL query with S3 fallback on miss.
Key components:
- CachedContentAccessor: DB-first read / dual-write / S3 fallback
- AssetContentService: static DB operations (get, get_many, upsert, delete)
- should_mirror(): single source of truth for extension-based policy
- Alembic migration for app_asset_contents table
Modified callers:
- SkillBuilder uses accessor.bulk_load() instead of per-node S3 reads
- AppAssetService.get/update_file_content route through accessor
- delete_node cleans both DB cache and S3
- draft_app_assets_initializer uses should_mirror() instead of hardcoded .md
- Add helpers.py with connection management utilities:
- with_connection: context manager for connection lifecycle
- submit_command: execute command and return CommandFuture
- execute: run command with auto connection, raise on failure
- try_execute: run command with auto connection, return result
- Add CommandExecutionError to exec.py for typed error handling
with access to exit_code, stderr, and full result
- Remove run_command method from VirtualEnvironment base class
(now available as submit_command helper)
- Update all call sites to use new helper functions:
- sandbox/session.py
- sandbox/storage/archive_storage.py
- sandbox/bash/bash_tool.py
- workflow/nodes/command/node.py
- Add comprehensive unit tests for helpers with connection reuse
