Request:
- Use the analyst skill to digest downloaded IPO archive data and start building an analysis model.
Changes:
- Add ipo_score_v0 as the first transparent stage-safe scoring rule set.
- Add build_analysis_dataset.py to derive model features, scores, decision bands, and empirical D1 calibration from SQLite.
- Generate analysis_model_v0_dataset.csv with 293 scored IPO rows and archived source paths.
- Add a model calibration report documenting coverage, T0/T1 bucket performance, usage, and known gaps.
- Record the initial model entry in the rule change log and document the command in README.
Verification:
- Ran py_compile for scripts/build_analysis_dataset.py.
- Regenerated the analysis dataset and report with as-of 2026-06-15T13:00:00Z.
- Checked CSV row count, source path coverage, and repo-relative path hygiene.
- Ran git diff --cached --check.
Next useful context:
- v0 should be treated as a transparent baseline, with T1 high-score calibration strongest and middle buckets still non-monotonic.
- T2 is excluded until a reliable grey-market source is approved.
geometrybase
48b89552fe