wiki-gap-finder 2.0.0

Campaign-driven pipeline that finds people missing from English Wikipedia, verifies every fact against real sources, and hands a human a research dossier — it never writes article prose.

Wikipedia Gap Finder v2

A campaign-driven pipeline that finds people from an underrepresented group who should have an English Wikipedia article but don't, checks every fact against real sources, and hands a human a research dossier they write the article from.

The tool never writes article prose. English Wikipedia banned LLM-generated/-rewritten article text (RfC, March 2026). This tool does detective work, verification, and structure only — a human writes every sentence. That isn't a limitation to route around; it's the product's integrity claim: AI found her and checked the facts; a human wrote her story.

Pipeline

1. Intake — names from CSV/txt lists and/or an optional Wikidata SPARQL redlist query.
2. Gap check — per name: enwiki title + fuzzy search, redirect detection, Draft: namespace, deletion-log history, and Wikidata sitelinks. Verdicts: GAP / EXISTS / REDIRECT_ONLY / DRAFT_EXISTS / DELETED_BEFORE / TRANSLATE_CANDIDATE (⭐ candidates that already exist in another language are the highest-value, since translation is an allowed path).
3. Notability triage (planned) — coverage search scored against WP:GNG.
4. Source vetting → dossier — see below.

Source vetter

The vetter is two processes across a two-file JSON boundary, so each side is testable in isolation:

gap_finder.py --dossier  →  vetting_worklist.json  →  Claude vet-sources subagent
                                                            │
dossier.md  ←  gap_finder.py --dossier  ←  vetting_verdicts.json

• Python (keyless): gathers coverage, tiers each source against Wikipedia's perennial-sources list (WP:RSP), and renders the dossier.

• The Claude subagent (skills/vet-sources): classifies reliability and traces breadcrumbs, sorting every claim into one of three buckets:

  Bucket	Meaning	Dossier section
  VERIFIED	a single reliable source names the subject and supports the claim, with a verbatim quote	Verified facts table
  LEAD	a breadcrumb is real, but no reliable source names the subject yet	Research leads — chase before writing
  DEAD_END	no corroboration found	UNVERIFIED — do not use

The never-stitch rule (WP:SYNTH): the vetter may not promote a LEAD to VERIFIED by combining a subject-naming unreliable source with a fact-confirming reliable source. Two half-sources stay a LEAD. And the renderer structurally refuses to print any fact backed only by an unreliable source — even if a verdicts file claims otherwise.

Usage

python3 gap_finder.py --campaign campaigns/<campaign>.yaml --check       # stages 1–2
python3 gap_finder.py --campaign campaigns/<campaign>.yaml --check --no-sparql --limit 5
python3 gap_finder.py --campaign campaigns/<campaign>.yaml --report      # table from saved state
python3 gap_finder.py --campaign campaigns/<campaign>.yaml --dossier "Name Here"

Swapping demographics is a new YAML in campaigns/ — zero code changes. Results are written under output/<campaign>/ (JSON state + markdown), so runs are resumable.

Conventions

Zero API keys on the default path (MediaWiki + Wikidata + DuckDuckGo are keyless; Firecrawl is opt-in). python3 + --break-system-packages, no venvs. Polite User-Agent, ≥1s between requests with exponential backoff.

pip3 install requests PyYAML jsonschema --break-system-packages
python3 -m pytest      # 31 tests

Layout

• gap_finder.py — CLI; intake + gap check (stages 1–2) and dossier wiring (stage 4)
• gapfinder/ — the source-vetter package (contract, rsp, search, worklist, verdicts, dossier)
• skills/vet-sources/SKILL.md — the Claude subagent contract
• campaigns/ — one YAML per campaign
• docs/superpowers/ — design spec and implementation plan