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Diff OSM geometry and attributes against TIGER/Line 2024 to identify import drift, new roads, and name-field artifacts in MetroNow zones.
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基于 SOC 职业分类
| name | ground-truth-diff |
| description | Diff OSM geometry and attributes against TIGER/Line 2024 to identify import drift, new roads, and name-field artifacts in MetroNow zones. |
| when_to_use | User mentions TIGER 2024, geometry drift, name artifacts, new roads, comparing OSM to Census data, or Mac Donald / O Toole style name errors |
| allowed-tools | Read Grep Glob Bash(python *) Bash(curl *) |
| argument-hint | [zone] |
| arguments | ["zone"] |
Compare OSM against TIGER/Line 2024 for zone: $zone
tl_2024_39061_roads.zip (Hamilton County, FIPS 39061)Ways that were imported in 2007 from TIGER 2005 but have since been updated in the Census Bureau's data. Differences indicate either:
Roads in TIGER 2024 that have no OSM equivalent — genuinely new construction since 2005. Particularly relevant for the MetroNow zones where suburban development is active.
The 2007 import introduced systematic name errors:
ñ was lost in the 2007 import)TIGER 2024 has corrected many of these. Diffing reveals which OSM ways still carry the 2005-era name.
The original import mapped CFCC A4x → highway=residential by default. TIGER 2024 uses updated MAF/TIGER Feature Class Codes (MTFCC) that better distinguish:
highway=tertiary or unclassifiedhighway=service + access=privatehighway=secondaryCross-referencing TIGER 2024 MTFCC against OSM highway=* reveals misclassifications.
tl_2024_39061_roads.zip from Census Bureausrc/osm/zones/__init__.py (the ZONES dict)Run the full TIGER defect audit pipeline for a MetroNow service zone — fetch from Overpass, classify defects, analyze history, generate reports.
Generate a MapRoulette challenge from scan results for defect classes with high false-positive rates. Creates GeoJSON tasks constrained to MetroNow zone polygons.
Deep revision history analysis for specific OSM ways — fetches full version history from OSM API v0.6, identifies import bot vs human edits, classifies review status with confidence scores.
Submit corrections to OSM API v0.6 with full community compliance — proper changeset tags, size limits, rate-limit awareness, and dry-run support.
Decompress MetroNow docs so future-you (and fresh AI sessions) can pick the project back up cold without re-deriving everything. Use when the user says docs are confusing, plain, dense, hard to follow, missing context, jargon-heavy, jump straight to conclusions, or are hard to come back to after time away; asks why something is the way it is; says they can't tell what a term means; asks for a walkthrough, explainer, primer, or context refresher; says a section assumes too much; or wants visual aids. Diagnoses missing-middle gaps (undefined jargon, unstated WHY, skipped bridge steps, prose where a picture is needed) and rewrites sections using a fixed template — definition first, then bridge steps, then a load-bearing diagram, then code citations. Outputs to docs/explainers/<topic>.md or in-place edits. No VitePress, no static-site generator — just markdown that GitHub renders.
Unified code review and audit standards for the MetroNow Atlas TIGER Audit Console (https://github.com/AICincy/MetroNow.git). Use this skill when auditing any code in the MetroNow project, doing a PR review, checking code quality, validating accessibility, reviewing Docker config, or when someone asks "review this code," "audit the frontend," "check my PR," "what's wrong with this file," or "does this meet our standards." The codebase is vanilla JavaScript (IIFE pattern, no framework), single-file HTML with inline CSS/JS, IBM Plex typography, Leaflet maps, and an Express.js backend on port 3000 that shells out to the Python `osm` CLI. Covers JS, HTML, CSS, and Dockerfile review with severity-classified findings.