chore: import upstream snapshot with attribution

examples/sandbox/tutorials/dataroom_qa/README.md

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+# Dataroom Q&A
+
+## Goal
+
+Answer grounded financial questions over a synthetic 10-K packet.
+
+The packet uses synthetic company data, but the documents are shaped like annual report excerpts: MD&A text uses 10-K `Part II, Item 7`, while statement PDFs and footnote text use `Part II, Item 8`.
+
+## Why this is valuable
+
+This demo shows a retrieval-first agent pattern over a bounded financial corpus where each metric and explanation should stay tied to source files.
+
+## Setup
+
+Run the fixture generator and then the Unix-local example from the repository root. Set `OPENAI_API_KEY` in your shell environment before running the example.
+
+```bash
+uv run python examples/sandbox/tutorials/data/dataroom/setup.py
+uv run python examples/sandbox/tutorials/dataroom_qa/main.py
+```
+
+After the initial answer, the demo keeps the sandbox session open for Rich-rendered follow-up prompts. Pass `--no-interactive` for a one-shot run.
+
+To run the same manifest in Docker, build the shared tutorial image once and pass
+`--docker`:
+
+```bash
+docker build --tag sandbox-tutorials:latest examples/sandbox/tutorials
+uv run python examples/sandbox/tutorials/dataroom_qa/main.py --docker
+```
+
+## Expected artifacts
+
+- A direct cited answer in the streamed agent response.
+- Citations use `[n](data/source-file.txt:line:14)` for text excerpts and `[n](data/source-file.pdf:page:1)` for the one-page synthetic PDFs.
+
+## Demo shape
+
+- Inputs: 5 synthetic filing text docs and 3 simple filing PDFs from `examples/sandbox/tutorials/data/dataroom/`.
+- Runtime primitives: sandbox-local bash/file search.
+
+## How instructions are loaded
+
+At startup, the wrapper loads this folder's `AGENTS.md` into the agent instructions and builds a hard-coded manifest that maps the shared SEC packet from `examples/sandbox/tutorials/data/dataroom/` into the sandbox as `data/...`.