Public-interest projects built with Claude

Nathanael Johnson · github.com/nathanaelhub · October–November 2026

Three tools for people who deal with public systems: families checking what help they might get, residents following their city council, and nonprofit staff starting to use AI. They're built on one idea: Claude handles the language, and code checks the facts.

The projects

Tennessee benefits screener

For families asking "might we qualify for SNAP, WIC or TennCare?"

Someone describes their household in plain words, in English or Spanish. Claude asks the questions it needs and explains the result. The eligibility math is plain code that Claude calls through MCP, with every threshold quoted from an official source.

98% vs 81%program calls right: 155/158 with the screener vs 110/135 with web search, 40 simulated households
19/22 vs 3/22SNAP amounts estimated within $5
$0.19 vs $0.49model cost per conversation
What building it found
Tennessee's SNAP income limit is unsettled. News reports say the state moved to 200% of poverty; the USDA's chart of states with that rule doesn't list Tennessee. The screener reports both, and a test fails the day the USDA adds Tennessee.
Limits
A screener, not a decision: results are "likely", "possibly" or "unlikely", and it always says to apply anyway. The evaluation uses simulated people, not real families.

Nashville Council Digest

For residents asking "what did the Metro Council do, and what's in my district?"

Every bill and resolution in plain language, with its outcome and council district. Claude writes each summary; code checks every amount, number and name against the official text; a second Claude reading checks the meaning. If either check fails, the site shows the official title instead.

1,125bills and resolutions from 21 meetings, January–October 2026
6 real errorscaught by the second reading: it flagged 14 of 1,124 summaries the fact check had passed
0 vs 56 of 59planted name swaps caught by the code check vs the Claude reading
What building it found
The official record has its own errors: four, including an analysis naming the wrong nonprofit for a grant. And the city's "passed" flag reads "Fail" for a resolution the council adopted, so outcomes come from the recorded action instead.
Limits
The review of flagged summaries was done by Claude in the building session, not by an independent person; every case is in the repo to check. Districts are found for 180 of 194 site-specific zoning bills.

Using Claude at a Nonprofit: a one-hour workshop

For nonprofit staff who don't write code, and the person who trains them.

A ready-to-run kit that teaches four habits: ask well, check before using, protect clients, and know when not to use it. It has a facilitator guide, slides, four exercises with answer keys, a privacy sheet quoting Anthropic's policies, and a quiz to give before and after.

Grade 2.6–5.8reading level of every attendee page, measured in code
$0.10to re-run every exercise through Claude before a workshop and score the answers
What building it found
I expected Claude to invent numbers when asked vaguely for a grant paragraph. It didn't: it left blanks like "[X] households" and asked for the real figures. The lesson moved to the person filling in the blanks, and the answer keys say so.
Limits
Not yet run with real staff. A one-page host flyer is out to find a first group.

Checking the checker

An AI grader is only useful if you know how often it's wrong. Before relying on one, I measured it:

How each project is handed off

Each repo has a README that says what was measured and what wasn't, tests that run on every change, and a runbook for the yearly updates: SNAP figures every October 1, new council meetings, changed policy pages. They're written so someone else can keep them running.