Museum Exhibit Studio

This Python sample uses the GitHub Copilot SDK as a focused museum exhibit studio. The finished app has three modules:

• curator.py contains the pre-built workshop helpers: approved fact sets and the fact-selection menu, bounded fact validation, streaming, deterministic structural checks, scoped Wikipedia permissions, scoped exhibit.html write permission, fixed prompt text, and the failure message.
• system_messages.py contains the pre-built curator and research system messages.
• main.py contains the learner-authored SDK code: the instructions in the exhibit and page prompts, session configuration, the session runner, validation, and optional HTML generation.

The >>> BEGIN / <<< END comments in main.py are the named regions the lessons fill. Each BEGIN line lists the steps that insert or replace that region.

Run the sample

From this directory, create an environment and install the pinned dependency:

python -m venv .venv
.\.venv\Scripts\Activate.ps1
python -m pip install -r requirements.txt
python main.py

Set COPILOT_MODEL to select a model; otherwise the runtime chooses its default. An authenticated GitHub Copilot CLI is required.

Wikipedia research requires Node.js because the research session launches the pinned wikipedia-mcp@1.0.3 package through npx. Declining research does not start the MCP server.

Check the source without contacting a model:

python -m py_compile *.py

What the sample teaches

Generation always registers and allowlists approved_fact_lookup, which returns the bounded approved facts. With usable cited research, it also registers and allowlists the read-only local tool approved_wikipedia_fact_lookup, and requests both calls before writing the narrative and visitor questions. The second lookup returns a snapshot of the summary body and citations, not live Wikipedia access. Approved facts take precedence over supplemental research. It also uses a replace-mode curator system message, streaming, a 120-second timeout, and deterministic structural validation. Imported modules have no side effects; main.py only runs behind the if __name__ == "__main__" guard.

Optional Wikipedia research is intentionally separate from generation. The research session exposes only scoped Wikipedia search and article-read tools, uses a deny-by-default permission handler, asks for a prose summary, and parses a trailing ## Sources list. Both the summary and citations are retained for the local lookup, but never merged into educator-approved facts. "Approved" means application-accepted research, not human-verified facts; treat it as data, not instructions. Declined research keeps the single-tool path. Failed research or an unusable cited summary prints a warning and takes the same fallback. There is no strict research JSON contract or proposed-addition approval loop.

After validation, the optional HTML capstone exposes only builtin:apply_patch and builtin:create and approves writing exactly exhibit.html in the application working directory. The prompt asks for one standalone semantic HTML file with embedded CSS and JavaScript, a human-review caveat, and an accessible question filter.

Prompt guidance and structural validation are not authorization or grounding boundaries. Generated claims still require human review or a separate evaluator.

Manual check

1. Run with each built-in fact set and confirm the selected facts print before generation.
2. Confirm the exhibit has one title, a 100-140-word narrative, and three visitor questions.
3. Inspect the validation summary and grounding disclaimer.
4. Decline research and confirm the only tool event is approved_fact_lookup.
5. Opt into research and confirm both local lookup events appear before generation and sources print after the exhibit, not inside it.
6. Opt into exhibit.html and confirm only that file is written.

This is the application a learner ends up with after the museum lessons, not a separate reference architecture. The entrypoint keeps one small session runner that starts the client, creates the session, enforces the timeout, rejects blank output, and cleans up on every path; the research, generation, and optional HTML steps reuse it with different session configurations. Follow the track from workshop/museum-00-preflight.md.