feat: frame-based positioning and mark system foundation

Core Changes:
- Fix SearchView to use start_frame/end_frame directly (no time*fps conversion)
- Add hard_delete support to delete_trace API
- VideoPlayer: Main timeline + Mark system foundation
- Proxy: Add local routes for auth, media, identity-matches, cluster-results
- Add .gitignore to exclude build artifacts and dependencies

Design Documents:
- Multi-track Mark system design (.opencode/plans/)
- Video editing positioning standards research

Files Modified:
- src/views/SearchView.vue: Frame positioning, ensureMinDuration (240 frames)
- src/views/PeopleView.vue: batchDeleteGroups with hard_delete
- src/api/index.ts: delete_trace with hard_delete body
- src/components/VideoPlayer.vue: Timeline + Mark UI
- src-tauri/src/proxy.rs: New local routes
- AGENTS.md: Update documentation

docs/VLM_AGENT_TOOL_REQUEST.md

@@ -0,0 +1,90 @@
+# VLM (Vision Language Model) Tool for Agent Search
+
+## Request
+Add a `vlm_describe` tool to the Agent system that can analyze video keyframes via VLM.
+
+## Background
+We have Ollama with LLaVA running locally (`http://localhost:11434`). The VLM can describe image content (people, objects, scenes, clothing, text, etc.). We want the Agent to be able to use this as a tool when users ask questions about visual content.
+
+## Use Cases
+- "What is the person wearing in this scene?"
+- "Find scenes where someone is wearing a red shirt"
+- "Describe the background of this video"
+- "What objects are visible in frame 1000?"
+
+## API Design
+
+### Agent Tool Registration
+Add a `vlm_describe` tool to the Agent's tool registry in Core API.
+
+### Tool Definition
+```json
+{
+  "name": "vlm_describe",
+  "description": "Analyze a video frame using Vision Language Model. Returns a description of the image content.",
+  "parameters": {
+    "file_uuid": "string - UUID of the video file",
+    "frame": "integer - Frame number to analyze",
+    "prompt": "string (optional) - Specific question about the image (default: 'Describe this image in 1-2 sentences.')"
+  }
+}
+```
+
+### Internal Implementation
+The Core API should call:
+```
+POST http://localhost:11434/api/generate
+{
+  "model": "llava",
+  "prompt": "<user prompt or default>",
+  "images": ["<base64 of frame thumbnail>"],
+  "stream": false,
+  "options": { "num_predict": 80 }
+}
+```
+
+To get the frame image, use the existing thumbnail API:
+```
+GET /api/v1/file/{file_uuid}/thumbnail?api_key={key}&frame={frame}
+```
+→ Returns JPEG bytes → resize to 480x270 → base64 encode → send to Ollama.
+
+### Result Format
+```json
+{
+  "tool": "vlm_describe",
+  "result": {
+    "file_uuid": "...",
+    "frame": 1234,
+    "description": "A man wearing a blue suit and red tie standing at a podium.",
+    "time_sec": 41.13
+  }
+}
+```
+
+### Performance (Benchmarked on M5 Max)
+
+**Model**: LLaVA 7B Q4_0 (Ollama), 1280x720 → 480x270 resize
+
+| Metric | Value |
+|--------|-------|
+| Avg per frame | **1.02s** |
+| Min | 0.81s |
+| Max | 1.60s |
+| Throughput | ~1 fps (sequential) |
+| Concurrent (4 workers) | No speedup — Ollama queues single-GPU |
+
+**Recommendations**:
+- Resize to 480x270 before sending reduces token count without quality loss
+- Batch processing doesn't help (Ollama queues requests to single model instance)
+- For large video indexing, process frames asynchronously in background
+- Consider a faster model if throughput becomes critical (e.g., LLaVA-Next 8B, Moondream 1.6B)
+
+## Frontend Changes (for reference)
+Once Core API returns `vlm_describe` in agent sources, the frontend will display:
+- Tool badge: "VLM"
+- Description text paired with the relevant video frame
+- Click to jump to that frame in the video player
+
+## Timeline
+Not urgent - this is an enhancement for visual search capabilities.