cdfi-superpowers

Your AI, grounded in audited CDFI tooling instead of hallucinating tract eligibility.

cdfi-superpowers is an AI skill layer for the CDFI industry — NMTC eligibility, bank CDFI peer benchmarking, and HMDA lending analysis, built for lenders, CDEs, compliance teams, and community development researchers.

Generic AI assistants confidently invent answers in this domain: wrong tract eligibility, fabricated peer medians, "CRA performance" claims from proxy data. In a field where numbers end up in loan committees, applications, and compliance reviews, that's not a quirk — it's a liability. These skills fix that by making your AI call real, open-source, audited tools and report exactly what they return — including N/A and errors — rather than inventing a plausible-sounding number.

The skills contain no new analytical code. Each one pip installs independently versioned, openly published Python packages (MIT-licensed, on PyPI) and teaches the AI to use them correctly, with the methodology caveats those tools ship with.

The three skills

Skill	What it does	Backed by
nmtc-eligibility	Is this address/tract NMTC eligible? Distress tier? Project feasibility?	nmtc-mapper 0.3.3, nmtc-screener 0.1.0
cdfi-peer-benchmark	Benchmark a bank CDFI against FDIC peers (NIM, ROAA, capital, …)	cdfi-benchmark 0.2.1
hmda-analysis	Pull HMDA LAR data and produce descriptive cuts + a CRA-proxy distribution	hmda-analyzer 0.5.0

Versions were verified against live PyPI at time of writing; every code example in each skill was actually executed and shows real output.

What these skills refuse to do

• Fabricate eligibility or metrics. If a tool errors, the skill reports the error; it never estimates NMTC eligibility from general knowledge or fills a NaN with a number.
• Inferential fair-lending analysis. hmda-analysis is descriptive only — no disparate-impact, disparity-ratio, protected-class, or fair-lending inference, and no reading the CRA-proxy as CRA performance.
• Benchmark non-banks. cdfi-peer-benchmark is FDIC bank CDFIs only — no credit unions, no unregulated loan funds.

See references/caveats-and-limits.md for the full boundary list.

Install

(a) Claude Code / Cowork — plugin marketplace

Add this repo as a marketplace, then install the plugin:

/plugin marketplace add Jaypatel1511/cdfi-superpowers
/plugin install cdfi-superpowers

(b) claude.ai — upload a .skill

Build the skill archives and upload the one(s) you want in the claude.ai skills UI:

bash scripts/make_skills.sh

This writes dist/nmtc-eligibility.skill, dist/cdfi-peer-benchmark.skill, and dist/hmda-analysis.skill — each a zip with SKILL.md at its root.

(c) Any other AI — raw markdown / llms.txt

Point any assistant at the raw Markdown. llms.txt at the repo root indexes the skills and references for AI crawlers; each skills/*/SKILL.md is self-contained.

How it relates to the packages

Each wrapped package is independently published and versioned on PyPI under thejaypatel1511 and installs on its own (pip install nmtc-mapper, etc.). This repo does not vendor or fork them — the skills install them at their current published version and call their public API. The full portfolio (22 packages) is catalogued in references/package-index.md.

License

MIT © 2026 Jay Patel. Each wrapped PyPI package carries its own license and version.