Fabio d6874ca8a6 Close the institutional gap: bodies, committees, leadership
The seat data answered 'who represents this place?' but not 'who runs
this institution?' — the Speaker of the House existed only as the rep
for LA-4. This adds the missing axis of institutional power.

New Body entities (233): the U.S. House, U.S. Senate, Executive Office,
49 committees, 181 subcommittees — each with its leadership.

Federal people (532/542) enriched with bioguide IDs, leadership roles,
and committee seats, reconciled from congress-legislators by name.
Mike Johnson is now Speaker of the House, not just LA-4.

Sources ingested directly into the repo (repo-canonical): Atlas
supplied bodies/leadership/committee_memberships via congress-legislators,
committed raw in data/. build.py supersedes convert_officeholders.py and
reads all four inputs in one deterministic pass (byte-identical on rerun).

The 10 unenriched federal records are a finding, not a bug: vacancies,
the President, and ~5 seats where Atlas's roster disagrees with the
authoritative current roster — model-vs-reality discrepancies to surface.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
2026-07-03 20:51:07 -04:00

Republic OS

An open, version-controlled mirror of public power in the United States.

Government is already software: statutes are code, agencies are processes, elections change the maintainers, and a bill is a pull request against the law. What government has never had is a changelog. This repository is that changelog.

The diff is the product. Every file here mirrors the observable public state of a government entity — people, offices, bodies, meetings, organizations, money, issues — as deterministic, sourced, human-readable records. When government changes, the files change, and git makes the change visible, permanent, and inspectable.

Three disciplines govern everything here: never overwrite, never rewrite history, always push off-box.

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Description
An open, version-controlled mirror of public power in the United States.
Readme 935 MiB
Languages
Python 98.3%
Makefile 1.7%