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- Rewrite llmCall() to use stream:true with SSE parsing and idle timeout - Support direct llama-server (AEGIS_LLM_URL) and cloud provider (AEGIS_LLM_BASE_URL + API key) endpoints, bypassing gateway - Handle reasoning_content from thinking models (Qwen3.5) - Add progress logging every 100 tokens - Update banner and healthcheck to show actual LLM endpoint
feat: streaming SSE for benchmark LLM calls with direct endpoint support
- Replace raw fetch + manual SSE parsing with OpenAI SDK streaming - Create llmClient/vlmClient instances with proper baseURL resolution - SDK handles max_completion_tokens, auth, and SSE parsing automatically - Add openai npm dependency to skill package.json - Healthcheck uses SDK non-streaming call
refactor: use OpenAI SDK for streaming LLM calls
- Change install field from 'none' to 'npm' - Document AEGIS_LLM_URL, AEGIS_LLM_API_TYPE, AEGIS_LLM_MODEL, AEGIS_LLM_API_KEY, AEGIS_LLM_BASE_URL, AEGIS_VLM_MODEL env vars - Add openai SDK to requirements
docs: update SKILL.md with npm dependency and new env vars
A single token is often insufficient for the model to produce a valid response token, causing false ping failures.
fix: increase benchmark ping max_completion_tokens from 1 to 5
- Register yolo-detection-2026 in skills.json with FPS presets and model sizes - Rewrite SKILL.md: model_size selection (nano/small/medium/large), FPS presets - Rewrite detect.py: model_size→ultralytics mapping, frame_id echo, AEGIS_SKILL_PARAMS - Create deploy.sh: zero-assumption bootstrapper (Python/conda/pyenv, CUDA/ROCm/MPS/CPU) - Per-backend requirements files (cuda/rocm/mps/cpu) - Create config.yaml with default parameters - Create docs/detection-protocol.md: full JSONL protocol spec, bbox format, backpressure
- Add auto_start boolean to YOLO 2026 config.yaml - Expand skill-development.md: config.yaml schema format, deploy.sh, environment variables, skills.json registration, status values
Feature/detection skill 2026
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