Close the project

Step 1: Address Astrid's feedback

If Astrid asked about the comparison pollutant analysis and you did not include it, consider adding it now. Showing that ozone -- which the regulation does not target -- did not change over the same period strengthens the evidence that PM2.5's decrease is attributable to the regulation rather than some general air quality trend.

This is a methodologically motivated extension, not scope creep. Astrid's scope behavior is clear: she stays within the agency's mandate. If you propose the addition, she frames it as "that's part of the evidence, not an extension." If you explain why you chose not to include it, she accepts the reasoning.

Either way, confirm with Astrid that the report is ready for the agency.

Step 2: Write a decision record

The most consequential decision in this project was not an analytical choice. It was the infrastructure you built in Unit 3. Write a decision record about the project memory authoring experience:

• What did you encode in CLAUDE.md?
• What was the before/after contrast? How did the cold-start session differ from the warm-start session?
• What would you do differently if you were writing the memory file again?
• Which conventions mattered most? Which ones did AI follow most reliably?

This record captures the infrastructure lesson -- the understanding that persistent conventions shape AI output more effectively than repeated prompting.

Step 3: Update the project memory file

The analysis has produced new knowledge that should be captured in the project memory file. The Malmo station relocation, the equipment upgrade if you discovered it, the specific model specification that worked, the validation approach that caught the autocorrelation issue.

Update CLAUDE.md with these project-specific findings. The memory file is not a one-time artifact -- it is a living document that grows with the project. The next analyst who opens this project in a new session should benefit from everything you learned.

Step 4: Commit and push

Direct AI to commit the work to Git with meaningful commit messages and push to GitHub. The commit history should reflect the analytical progression: profiling, framing, infrastructure authoring, analysis, validation, delivery.

Verify the push succeeded. The repository should contain: the notebook, the findings report, the methodology memo, the decision record, CLAUDE.md, AGENTS.md, and the original materials.