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name: rosie-suspicion-pipeline description: "Run, inspect, adapt, and troubleshoot Rosie's suspicious-reimbursement pipeline for Chamber of Deputies and Federal Senate data." disable-model-invocation: true metadata: disco-role: operating license: MIT

Rosie Suspicion Pipeline

Use this sub-skill when you need to run Rosie, inspect or adapt its suspicious-reimbursement pipeline, reason about classifier inputs/outputs, or debug why suspicions.xz or classifier model caches were not produced as expected.

Start here

• For CLI commands, programmatic pipeline flow, Core(settings, adapter), model-cache behavior, and safe offline adaptation, read references/cli-and-workflows.md.
• For classifier catalog entries, settings keys, required dataframe columns, and prediction semantics, read references/classifier-reference.md.
• For Chamber of Deputies and Federal Senate adapter normalization, expected input/output files, suspicions.xz shape, and download caveats, read references/data-formats.md.
• For dependency, data, legacy-library, cache, and network failure modes, read references/troubleshooting.md.
• To run a deterministic no-download smoke check for Core and any bundled classifier (default: invalid CNPJ/CPF), run scripts/rosie_smoke.py.

Fast routing

• Run the native pipeline: use python rosie.py run chamber_of_deputies --output <directory> or python rosie.py run federal_senate --output <directory> from a Rosie runtime checkout or container.
• Run native tests: use python rosie.py test, optionally followed by core, chamber_of_deputies, or federal_senate.
• Avoid downloads or service starts: do not call the native run command. Use a tiny dataframe and a classifier directly, or use Core with a custom adapter whose dataset and path are already local.
• Understand a suspicious flag: identify the classifier column in suspicions.xz, then use classifier-reference.md for required columns and true/false meaning.
• Stale or surprising model output: inspect/delete the classifier .pkl cache in the selected output directory, except for the monthly-subquota classifier, which is fitted every run and intentionally not cached.
• Jarbas API questions: route to the sibling jarbas-data-api sub-skill.
• Data loading, Docker/services, deployment, or setup operations: route to the sibling deployment-and-data-ops sub-skill.

Operating boundaries

Rosie is the suspicion-generation application. It reads reimbursement datasets, normalizes them through the selected adapter, applies a settings-defined classifier catalog, and writes a compressed CSV named suspicions.xz. It does not serve the web API, run Jarbas, start databases, or load data into Jarbas by itself.

The native run workflow is not safe for offline-only checks because adapter access triggers data update/download routines. For deterministic local verification, use the bundled smoke script or construct a minimal dataframe for the specific classifier under inspection.

Verification anchors

This sub-skill is grounded in the native CLI surface (run, test, module choices, and --output), Core(settings, adapter) behavior, Chamber/Federal adapter normalization, classifier docstrings and tests, model-cache tests, and installed API inspection for Rosie public classes and methods.