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* fix(vision): file-ref mode + media pipeline fixes for image visibility
Replace inline base64 image loading with file path references when
read_image provider is configured. LLM calls read_image(path=...)
instead of receiving 25K-250K tokens of base64 per image.
Changes:
- Agent loop: skip base64 context storage in file-ref mode, only
load historical images for inline fallback
- New enrichImagePaths() enriches ALL user messages with file paths
(not just current turn) so historical images are accessible
- System prompt: imperative tool summaries ("REQUIRED when you see
<media:image> tags") + dedicated "Media Files" section
- Slack: store mediaPaths in pending history + CollectMedia on mention
- Feishu: add CollectMedia for group context history media
- RC-1 fix: save enriched content (with media IDs/paths) to DB
instead of raw req.Message
Closes #509
* fix(vision): resolve Claude review — Feishu early media + skip loadImages in file-ref mode
- Feishu: download media BEFORE mention gate (step 4) and store file
paths in pending history via Media field. Reuse early-resolved
MediaInfo at step 10 to avoid double-download. CollectMedia now
returns actual paths instead of empty.
- Agent loop: guard loadImages() behind !deferToReadImageTool so
file-ref mode avoids unnecessary disk I/O + base64 encoding.
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TOOLS.md - Local Notes
Skills define how tools work. This file is for your specifics — the stuff that's unique to your setup.
What Goes Here
Things like:
- Camera names and locations
- SSH hosts and aliases
- Preferred voices for TTS
- Speaker/room names
- Device nicknames
- Anything environment-specific
Examples
### Cameras
- living-room → Main area, 180° wide angle
- front-door → Entrance, motion-triggered
### SSH
- home-server → 192.168.1.100, user: admin
### TTS
- Preferred voice: "Nova" (warm, slightly British)
- Default speaker: Kitchen HomePod
Media Files
When users send images, videos, audio, or documents, use the read_* tools to analyze them.
The path attribute in media tags points to the file — pass it directly to the tool.
Why Separate?
Skills are shared. Your setup is yours. Keeping them apart means you can update skills without losing your notes, and share skills without leaking your infrastructure.
Add whatever helps you do your job. This is your cheat sheet.