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Scenario Lab Quickstart

1. Create the local environment

PYTHON=/path/to/python3.12
"$PYTHON" -m venv packages/core/.venv
source packages/core/.venv/bin/activate
pip install -e 'packages/core[dev]'

2. Run the built-in demo

scenario-lab demo-run --root .forecast

3. Inspect the generated artifacts

ls .forecast/runs/demo-run
cat .forecast/runs/demo-run/report.md
cat .forecast/runs/demo-run/workbench.md

You should see demo-run complete before inspecting the generated files.

4. Build an evidence corpus

Evidence drafting reads from a local SQLite corpus. The default path is .forecast/corpus.db, and the CLI creates that database automatically when you ingest evidence or draft an evidence packet.

Create a candidate folder, add evidence files, then ingest them:

mkdir -p .forecast/evidence-candidates
# Put relevant Markdown, CSV, JSON, spreadsheet, saved web page, or text-extractable PDF files here.
scenario-lab ingest-directory --root .forecast --path .forecast/evidence-candidates --tag domain=interstate-crisis

After a run has an intake draft, draft the evidence packet:

scenario-lab draft-evidence-packet --root .forecast --run-id <run-id> --revision-id r1

Agents should normally use the packaged runtime instead of raw commands:

scenario-lab run-adapter-action --root .forecast --candidate-path .forecast/evidence-candidates --run-id <run-id> --revision-id r1 --action batch-ingest-recommended
scenario-lab run-adapter-action --root .forecast --run-id <run-id> --revision-id r1 --action draft-evidence-packet

Use --corpus-db <path> only when you intentionally want a corpus outside .forecast/corpus.db.