Implement the core classification engine and wire it into the pipeline stack.
classify.py: two-axis classifier — HTML-strip, comment-intent regex buckets
(escalations → status inquiry, most-specific first), Hold Reason / WO Status
structured state, comment-vs-state mismatch detector, and a Claude Haiku
fallback (Secrets Manager key) reserved for ambiguous free-text. Exports
ESCALATION_CATEGORIES / ACTION_NEEDED_CATEGORIES.
handler.py: S3-triggered handler — parse xlsx/csv, classify each non-blank
row, write a per-WO Parquet snapshot to analytics/dt=YYYY-MM-DD/ (registers
the Glue partition via awswrangler) and a summary.json for slack-post (Phase 4).
pipeline_stack.py: Glue database, ARM64 Python 3.12 classifier Lambda
(Docker-bundled deps), S3 raw/ notification (.xlsx/.csv), and least-privilege
IAM (read raw/, read-write analytics/, scoped Glue catalog, read Anthropic key).
Smoke-tested against the real export: 347 rows, "Other" at 5.2% (target ~9%),
18 mismatches flagged. 7/7 unit + smoke tests pass; cdk synth green.
Stand up the Phase 0 CDK scaffold for the daily APM work-order
analysis pipeline: two-stack CDK app (pipeline + grafana), classifier
and slack-post Lambda packages, dashboards-as-code, the local
drop-folder uploader, and a classifier smoke-test placeholder.
Wire CI/CD to the org reusable workflows: ci.yaml -> ci-python-sam
(ruff + cdk synth) and deploy.yaml -> cd-cdk (OIDC, cdk deploy --all).
Pin aws-cdk-lib==2.253.1; Lambdas target Python 3.12 / arm64.
Rewrite .gitignore to the org Python-CDK standard so the source-of-
truth files (CLAUDE.md, docs/, .claude/agents) are tracked while build
artifacts (.venv, cdk.out, caches) stay ignored.
Domain logic, stack resources, and dashboards are stubbed and filled
in across Phases 1-5 (docs/BUILD.md). cdk synth is green for both
stacks; ruff check/format pass.
