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.
| Skill | What it does | Backed by |
|---|---|---|
| nmtc-eligibility | Is this address/tract NMTC eligible? Distress tier? Project feasibility? | nmtc-mapper >=0.5.0, 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 >=0.6.0 |
Versions were verified against live PyPI at time of writing; every code example
in each skill was actually executed and shows real output. Where a floor is shown
as >=, it is load-bearing and the skill says why: nmtc-mapper >=0.5.0 is
where is_opportunity_zone stops returning a confident False — below it the
package answers "not an Opportunity Zone" about 78,039 tracts it cannot
distinguish from a 2010/2020 vintage miss, and carries an
is_nmtc_native_area field that can only ever say "I don't know"; and
hmda-analyzer >=0.6.0 is where the geography-vintage refusal exists at all.
hmda-analyzer 0.6.0 alone required Python >=3.11; 0.6.1 relaxed that back
to >=3.9 while keeping the refusal (verified 2026-08-13 against the >=0.6.0
floor, which resolved to 0.6.1 at the time; its __all__ still exports
GeographyVintageError and the three basis maps). The pinned floor stays
>=0.6.0 because 0.6.1 changed nothing the skill layer depends on.
- 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.
cdfi-superpowers 2026.8.1 (CalVer, YYYY.M.MINOR). This is the plugin
version carried by .claude-plugin/plugin.json and .claude-plugin/marketplace.json;
the three manifests and this line move together. It versions the skills, not
the wrapped PyPI packages — those are independently versioned and are listed in
the table above. See CHANGELOG.md for what changed under each release.
The skills live in .agents/skills/ — the agent-neutral location defined by
the Agent Skills spec. At project
scope that directory is shared by GitHub Copilot, Cursor, Codex, Gemini CLI,
Antigravity, Amp, Cline, OpenCode and Warp, which is the list gh skill install --help names as resolving to it.
Claude Code is not in that list and does not scan .agents/skills/ — it
scans .claude/skills/. It reaches these three skills through the plugin
manifest instead: .claude-plugin/plugin.json enumerates the three paths
explicitly, which is what makes install method (a) work. Dropping a skill folder
into a project's .agents/skills/ does nothing in Claude Code, silently.
These skills are not self-contained prose. Each one
pip installs a package from PyPI and executes Python, and each calls a public federal data endpoint at run time. They need:
- Python >=3.9 and pip
- Network access to
pypi.org, plus the endpoints the skill you use hits:geocoding.geo.census.govandwww.cdfifund.gov(nmtc-eligibility),banks.data.fdic.gov(cdfi-peer-benchmark),ffiec.cfpb.gov(hmda-analysis)In a locked-down enterprise environment where PyPI or those hosts are blocked, these skills cannot work — the agent will load the skill and then fail at the install or the first call. Check egress before installing. See
references/data-source-map.mdfor the full host list and which are known to be blocked from cloud/datacenter IPs.
/plugin marketplace add Jaypatel1511/cdfi-superpowers
/plugin install cdfi-superpowers
To upgrade, refresh the marketplace, then update the plugin by its
marketplace-qualified name. update rejects the bare name that install
accepts — claude plugin update cdfi-superpowers fails with Plugin "cdfi-superpowers" not found. A restart is required to apply:
claude plugin marketplace update cdfi-superpowers
claude plugin update cdfi-superpowers@cdfi-superpowers
Build the 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.
Copilot discovers skills from .agents/skills/ in a project, or from
~/.copilot/skills/ for every project. It does not scan a bare skills/
directory — which is why this repo uses .agents/skills/.
Project scope — clone the repo into a workspace and Copilot reads
.agents/skills/ directly, no copying:
git clone https://github.com/Jaypatel1511/cdfi-superpowers.git
Personal scope (all projects) — copy the skill folders into your personal skills directory, then reload:
git clone https://github.com/Jaypatel1511/cdfi-superpowers.git /tmp/cdfi-superpowers
mkdir -p ~/.copilot/skills
cp -R /tmp/cdfi-superpowers/.agents/skills/* ~/.copilot/skills/
Then in Copilot CLI run /skills reload and confirm the three skills are listed.
Or with the GitHub CLI (requires gh >= 2.90; installs one skill at a time):
gh skill install Jaypatel1511/cdfi-superpowers nmtc-eligibility \
--allow-hidden-dirs --agent github-copilot --scope user
--allow-hidden-dirs is required, not optional: gh skill treats
.agents/skills/ as a hidden directory and finds nothing without it (it reports
"no standard skills found, but 3 skill(s) exist in hidden directories"). The
skill name is also required when running non-interactively. Repeat for
cdfi-peer-benchmark and hmda-analysis, or drop --scope user to install into
the current repository instead.
Who this is for: Cursor, Codex, Gemini CLI, Antigravity, Amp, Cline,
OpenCode, Warp and GitHub Copilot — the agents that share the project-scope
.agents/skills/ directory.
Who this is not for: Claude Code. It does not scan .agents/skills/, so
copying a folder there gets you nothing and reports no error. Use (a) instead.
Copy the skill folder(s) you want into your project's .agents/skills/,
creating that directory first — it does not exist in a fresh project, and
cp -R fails with No such file or directory if you skip this:
mkdir -p /path/to/your-project/.agents/skills
cp -R .agents/skills/nmtc-eligibility /path/to/your-project/.agents/skills/
Each SKILL.md is self-contained — one file, no assets, no build step.
llms.txt at the repo root indexes the skills and references for AI crawlers.
It is a discovery aid, not an installation method; an assistant reading it
still needs the runtime above to actually run anything.
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.
MIT © 2026 Jay Patel. Each wrapped PyPI package carries its own license and version.