Set up Cursor Cloud dev environment

AGENTS.md

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+# AGENTS.md
+
+## Cursor Cloud specific instructions
+
+`colon-bench-eval` is a single Python research toolkit (managed by `uv`) for reproducing the
+Colon-Bench colonoscopy video benchmark. There is no database, server, or background daemon —
+just CLI eval/plot scripts plus an optional Streamlit viewer. The `edgetam/` directory is a
+vendored third-party model (Meta EdgeTAM), not a separate product. Standard install/run commands
+live in `README.md`; this section only captures non-obvious caveats.
+
+### Environment notes
+- Dependencies are installed by the startup update script (`uv sync --extra all`). Use `uv run …`
+  to execute anything inside the project venv. The plotting scripts need `matplotlib`, which only
+  comes from the `viewer`/`all` extra — `uv sync` (core) alone is not enough to plot.
+- **Git LFS is essential and non-obvious.** These files are LFS pointers and are useless until
+  fetched: `edgetam/checkpoints/edgetam.pt` (EdgeTAM checkpoint), `data/colon-bench/*_seg_*.json`,
+  `data/colon-bench/benchmark_detection.json`, `data/colon-bench/benchmark_segmentation.json`.
+  Without `git lfs pull`, `scripts/plot_segmentation_metrics.py` reports "No segmentation data
+  found" (JSON decode errors) and EdgeTAM segmentation fails for a missing checkpoint. The update
+  script runs `git lfs pull`; if data files look like `version https://git-lfs...` pointers, run it
+  again. Note `git lfs install` may warn about the Cursor-managed pre-push hook — that warning is
+  harmless; `git lfs pull` still works on its own.
+- After `git lfs pull`, the materialized LFS files may show as "modified" in `git status` because
+  the smudge filter was not active at clone time. Do NOT stage/commit these data/checkpoint files.
+- No GPU in this environment: `torch` runs on CPU and EdgeTAM's CUDA extension is skipped
+  (`SAM2_BUILD_ALLOW_ERRORS=1`), which is fine for plots and the viewer. The local VLM path
+  (`--extra local-vlm`, Qwen3-VL via `--local`) requires a GPU and is not runnable here; use the
+  OpenRouter API path instead.
+
+### Secrets
+- `HF_TOKEN` and `OPENROUTER_API_KEY` are read from the environment (or a `.env` file). `HF_TOKEN`
+  is required for the gated `ajhamdi/colon-bench` dataset (viewer video downloads, eval scripts);
+  `OPENROUTER_API_KEY` is required for API-based VQA/classification/detection evals. Both are
+  typically already present as env vars in this environment — check before declaring blocked.
+
+### Running things
+- Offline sanity check (no secrets, no network): the `scripts/plot_*.py` scripts run against the
+  shipped result JSONs. They always save as `.pdf` regardless of the file extension you pass to
+  `--save` (the extension is overwritten to `.pdf`).
+- Streamlit viewer (a real long-running service):
+  `uv run --extra viewer streamlit run viewer/visualize_benchmark.py --server.headless true --server.port 8501 -- --benchmark data/colon-bench --mode vqa_prompted`
+  It serves on port 8501 and downloads colonoscopy clips from the gated HF dataset on demand the
+  first time a sample is shown (needs `HF_TOKEN`); the first sample can take a few seconds.
+- Full evals (`scripts/llm_evaluate_*.py`) hit the OpenRouter API and stream large videos from HF —
+  they cost API credits and bandwidth, so prefer the offline plot scripts for smoke testing.
+
+### Lint / tests / build
+- There is no first-party lint, test, or CI configuration in the main project, and no Makefile or
+  docker-compose. "Build" is just `uv sync` (EdgeTAM builds automatically as an editable install).