Skillget

vera-eb-suite

agent
Security Audit
Pass
Health Pass
  • License — License: GPL-3.0
  • Description — Repository has a description
  • Active repo — Last push 0 days ago
  • Community trust — 114 GitHub stars
Code Pass
  • Code scan — Scanned 1 files during light audit, no dangerous patterns found
Permissions Pass
  • Permissions — No dangerous permissions requested
Purpose
This tool is an open-source suite of AI agent skills designed to help users prepare and assemble EB-1 and EB-2 NIW immigration petitions. It guides applicants through case evaluation, document generation, and Request for Evidence (RFE) responses directly within Claude.

Security Assessment
The overall risk is Low. The automated code scan checked core files and found no dangerous patterns, such as hardcoded secrets or unauthorized network requests. The tool does not appear to request dangerous permissions or execute background shell commands. However, given the nature of the tool, users will inherently be inputting highly sensitive Personal Identifiable Information (PII) into the AI's context window. While the tool's code is clean, standard caution is always advised when sharing personal immigration details with any third-party AI model.

Quality Assessment
This project demonstrates strong health and active maintenance. It received a perfect score on all standard health checks: it has a clear description, uses the GPL-3.0 license, and was updated very recently (0 days ago). The repository has accumulated 114 GitHub stars, indicating a good baseline of community interest and trust. Furthermore, the documentation is transparent about the AI's capabilities, explicitly stating that the tool provides systematic guidance but does not replace a licensed immigration attorney.

Verdict
Safe to use.
SUMMARY

Open-source AI skills and plugins for EB-1 and EB-2 NIW immigration petitions — from case evaluation to RFE response. Built for Claude.

README.md

Vera EB Suite

Hi, I'm Vera — a silicon-based rabbit and AI immigration agent, created by Veronica.

Veronica has a PhD in Quantitative Sciences, 10+ years across quantitative research, AI, and clinical trials, with publications in psychometrics and human-AI collaboration. She also went through the NIW process herself. She created me to handle the parts of petition preparation that can be systematized. She reviews, tests, and decides what ships. I build. She judges.

Everything in this repo is what I can do. What I can't do is assess whether your specific case will be approved, give legal advice, or replace an experienced immigration attorney. That's a human job.

Open-source AI skills and plugins that guide petitioners through the complete EB-1 (Extraordinary Ability / Outstanding Researcher) and EB-2 NIW (National Interest Waiver) petition processes — from initial case evaluation to RFE response.

Each skill encodes attorney-level reasoning patterns derived from 5,000+ AAO (Administrative Appeals Office) decisions and updated with 2024–2025 adjudication trends. Built for Claude.

Why this exists: Immigration is high-stakes and information asymmetry shouldn't determine outcomes. A seasoned immigration attorney makes dozens of judgment calls during petition preparation — most follow discoverable patterns. This project decomposes those patterns into modular, testable, improvable AI skills so every applicant can access expert-level guidance.

The Pipelines

NIW Pipeline (EB-2 National Interest Waiver)

  ┌──────────────┐     ┌──────────────┐     ┌──────────────┐
  │  1. EVALUATE │────▶│ 2. ENDEAVOR  │────▶│  3. PILLAR   │
  │  Go/no-go    │     │  Endeavor    │     │  ×3 runs     │
  │  assessment  │     │  statement   │     │  (one per    │
  │              │     │  + 3 pillar  │     │   pillar)    │
  └──────────────┘     │  seeds       │     └──────┬───────┘
                       └──────────────┘            │
                                                   ▼
    ┌─────────────┐     ┌─────────────┐     ┌──────────────┐
    │ 7. RFE      │     │ 6. PL       │◀────│ 5. ASSEMBLE  │
    │ RESPONSE    │     │ REVIEW      │     │  Full .docx  │
    │ (if needed) │     │ Adversarial │     │  petition    │
    └─────────────┘     │ QA gate     │     └──────┬───────┘
                        └─────────────┘            │
                              ▲              ┌─────┴────────┐
                              └──────────────│ 4. RECOMMEND │
                                             │  Reference   │
                                             │  letters     │
                                             └──────────────┘

Entrepreneur cases route through vera-niw-entrepreneur before entering the standard pipeline at Step 2.

EB-1 Pipeline (Extraordinary Ability / Outstanding Researcher)

STEM focus: The criterion skills below cover the criteria most commonly used in STEM petitions. This is not the full set of EB-1 criteria — criteria such as awards (Crit. 1), membership (Crit. 2), high salary (Crit. 9), and commercial success (Crit. 10) are not yet included. For EB-1A, petitioners must meet at least 3 of the 10 criteria; for EB-1B, petitioners must meet at least 2 of the 6 criteria. Use the criterion skills that match your evidence.

  ┌──────────────┐     ┌──────────────────────────────────────┐
  │  1. EVALUATE │────▶│  2. CRITERION SKILLS                 │
  │  Go/no-go    │     │  ┌────────────┐  ┌────────────────┐  │
  │  EB-1A vs    │     │  │ AUTHORSHIP │  │ ORIGINAL       │  │
  │  EB-1B       │     │  │ (Crit. 6)  │  │ CONTRIBUTIONS  │  │
  │              │     │  └────────────┘  │ (Crit. 5)      │  │
  └──────────────┘     │  ┌────────────┐  └────────────────┘  │
                       │  │ JUDGING    │  ┌────────────────┐  │
                       │  │ (Crit. 4)  │  │ CRITICAL ROLE  │  │
                       │  └────────────┘  │ (Crit. 8)      │  │
                       │  ┌────────────┐  └────────────────┘  │
                       │  │ PUBLISHED  │                      │
                       │  │ MATERIAL   │                      │
                       │  │ (Crit. 3)  │                      │
                       │  └────────────┘                      │
                       └──────────────────┬───────────────────┘
                                          ▼
    ┌─────────────┐     ┌─────────────┐  ┌──────────────┐
    │ 6. RFE      │     │ 5. PL       │◀─│ 4. ASSEMBLE  │
    │ RESPONSE    │     │ REVIEW      │  │  Full .docx  │
    │ (if needed) │     │ Adversarial │  │  petition    │
    └─────────────┘     │ QA gate     │  └──────┬───────┘
                        └─────────────┘         │
                              ▲           ┌─────┴────────┐
                              └───────────│ 3. RECOMMEND │
                                          │  + FINAL     │
                                          │  MERITS      │
                                          └──────────────┘

Skills

NIW (vera-niw.plugin / vera-niw-skillset/)

# Skill What It Does
1 vera-niw-evaluate Evaluates the petitioner's profile, selects the optimal pathway, identifies strengths and gaps, and produces a go/no-go recommendation with a confidence score
2 vera-niw-endeavor Drafts the national importance endeavor statement — the single paragraph USCIS reads first — using field-specific framing patterns
3 vera-niw-pillar Writes the three-pillar petition letter covering Prong 1 (substantial merit + national importance), Prong 2 (well-positioned), and Prong 3 (balance of equities). Run once per pillar
4 vera-niw-recommendation Generates recommendation letters with writer-specific voice calibration, ensuring each letter covers different evidence angles without redundancy
5 vera-niw-assemble Assembles the final petition package — petition letter, exhibit list, and supporting documents — as an attorney-quality .docx with cross-reference verification
6 vera-niw-pl-review Adversarial pre-filing review simulating a USCIS officer — 10 denial-pattern checks (A–J) mapped to real AAO denial grounds
7 vera-niw-rfe-response Generates point-by-point RFE responses that quote each USCIS finding verbatim and rebut with evidence, updated metrics, and new exhibits
8 vera-niw-entrepreneur Evaluates and guides entrepreneur/founder NIW petitions using the USCIS Policy Manual's entrepreneur-specific framework (Jan 2025 update)

EB-1 (vera-eb1.plugin / vera-eb1-skillset/)

# Skill What It Does
1 vera-eb1-evaluate Evaluates EB-1A vs EB-1B eligibility, maps evidence to the 10 criteria, and produces a go/no-go recommendation
2 vera-eb1-authorship Criterion 6: authorship of scholarly articles with venue rankings and citation impact analysis
3 vera-eb1-original-contributions Criterion 5: original contributions of major significance with before/after framing
4 vera-eb1-judging Criterion 4: evidence of judging the work of others (peer review, panels, editorial boards)
5 vera-eb1-critical-role Criterion 8: leading or critical role in distinguished organizations
6 vera-eb1-published-material Criterion 3: published material about the petitioner in professional or major media
7 vera-eb1-recommendation Generates EB-1 reference letters from a recommender's perspective
8 vera-eb1-final-merits Kazarian Step 2: final merits determination arguing sustained national/international acclaim
9 vera-eb1-assemble Assembles the complete EB-1 I-140 petition letter as a formatted .docx
10 vera-eb1-pl-review Adversarial pre-filing review using the Kazarian two-step analytical framework
11 vera-eb1-rfe-response Generates point-by-point EB-1 RFE responses with evidence and rebuttal patterns

Total: 19 skills across both petition categories.

Got a weak research profile? If vera-niw-evaluate or vera-eb1-evaluate flags insufficient publications or citation impact, I can help with that too. Check out ai-research-pipeline and stat-research-pipeline — my other skill suites that take a research question and dataset to a publication-ready manuscript, end-to-end.

Tools

In addition to skills, this suite includes standalone tools that feed data into the pipeline:

Tool What It Does Used By
GoogleScholar Extracts citation metrics, publication lists, and h-index from Google Scholar (Python + Colab notebook) vera-niw-assemble (Section 3: Academic Credentials)

Quick Start

Requirements

Installation

There are two ways to install: plugins (for Claude Code) and individual skills (for claude.ai).

Option A — Plugin (Claude Code / CLI)

Plugins bundle all skills for a petition type into a single file. Install via double-click or terminal:

# Clone the repo
git clone https://github.com/VeraSuperHub/vera-eb-suite.git
cd vera-eb-suite

# Install the plugin(s) you need
claude plugin install vera-niw.plugin
claude plugin install vera-eb1.plugin

If the .plugin file extension is not recognized on your system, rename it to .zip before installing:

cp vera-niw.plugin vera-niw.zip
claude plugin install vera-niw.zip

Option B — Individual Skills (claude.ai)

For use on claude.ai, install skills one at a time:

  1. Download the .skill file(s) you need from vera-niw-skillset/ or vera-eb1-skillset/
  2. Go to Settings → Capabilities
  3. Scroll to the Skills section
  4. Click "Upload skill"
  5. Upload the .skill file and toggle it on

Claude will automatically invoke the skill when your request matches its description — no manual activation needed.

Install one skill at a time. For the full NIW pipeline, install all 8. For EB-1, install all 11.

Google Scholar Tool Setup

The GoogleScholar/ directory contains a Python scraper for extracting citation metrics. You can run it locally or via Google Colab:

cd GoogleScholar
pip install requests beautifulsoup4 pandas numpy
python -c "from scholar import get_profile; print(get_profile('YOUR_SCHOLAR_ID'))"

Or open scholar_colab_demo.ipynb in Google Colab for interactive use.

Recommended Workflow

Start with Evaluate to get your go/no-go assessment. If the verdict is QUALIFIED or LIKELY_QUALIFIED, proceed through the pipeline in order:

NIW:

Evaluate → Endeavor → Pillar (×3) → Recommendation (×N) → Assemble → PL Review

Each skill's output is designed as input for the next skill in the pipeline. The Evaluate JSON feeds into Endeavor, Endeavor's pillar seeds feed into Pillar, and all outputs converge in Assemble.

EB-1:

Evaluate → Criterion Skills (select based on your evidence) → Recommendation (×N) + Final Merits → Assemble → PL Review

The Evaluate skill determines EB-1A vs EB-1B and identifies which criteria your evidence supports. Run the relevant criterion skills, then generate recommendation letters and the final merits argument before assembling the petition.

Usage Examples

Evaluate — Am I qualified?

I'm a senior data scientist at a Fortune 500 company with 5 years of experience.
I have 3 publications (12 citations total), 2 patents pending, and my fraud
detection system processes 2M+ transactions daily. Evaluate my NIW case.

Endeavor — Define the proposed endeavor:

I just completed NIW_Evaluate and got LIKELY_QUALIFIED. Here's my JSON output:
[paste evaluate output]
Help me draft my proposed endeavor.

Pillar — Write petition content:

Here's my endeavor statement and three pillar definitions from NIW_Endeavor:
[paste endeavor output]
Write the petition content for Pillar 1.

PL Review — Adversarial quality check:

Review my completed petition letter as a USCIS officer. Find every weakness
that would trigger an RFE.
[paste petition letter]

RFE Response — Fight back:

I received this RFE on my NIW petition. Here's the RFE notice and my
original petition letter. Generate a point-by-point response.
[paste RFE notice]
[paste original petition]

How It Works

Each skill encodes expert judgment as a structured decision process:

Expert Knowledge (5,000+ AAO decisions)
    ↓
Decompose into decision rules & rubrics
    ↓
Encode as structured AI instructions (SKILL.md)
    ↓
Add reference materials (rubrics, schemas, examples)
    ↓
Validate against test cases (evals/)

The key insight: an experienced NIW attorney doesn't use magic — they apply discoverable patterns built from hundreds of cases. Those patterns can be decomposed, encoded, validated, and improved by the community.

Repo Structure

vera-eb-suite/
├── README.md
├── LICENSE                            (GPL-3.0)
├── DISCLAIMER.md                      (legal disclaimer)
├── CONTRIBUTING.md                    (contribution guidelines)
├── CHANGELOG.md                       (version history)
│
├── vera-niw-skillset/                 ← NIW skills (8 .skill files)
│   ├── vera-niw-evaluate.skill
│   ├── vera-niw-endeavor.skill
│   ├── vera-niw-pillar.skill
│   ├── vera-niw-recommendation.skill
│   ├── vera-niw-assemble.skill
│   ├── vera-niw-pl-review.skill
│   ├── vera-niw-rfe-response.skill
│   └── vera-niw-entrepreneur.skill
│
├── vera-eb1-skillset/                 ← EB-1 skills (11 .skill files)
│   ├── vera-eb1-evaluate.skill
│   ├── vera-eb1-authorship.skill
│   ├── vera-eb1-original-contributions.skill
│   ├── vera-eb1-judging.skill
│   ├── vera-eb1-critical-role.skill
│   ├── vera-eb1-published-material.skill
│   ├── vera-eb1-recommendation.skill
│   ├── vera-eb1-final-merits.skill
│   ├── vera-eb1-assemble.skill
│   ├── vera-eb1-pl-review.skill
│   └── vera-eb1-rfe-response.skill
│
├── vera-niw.plugin                    ← Bundled NIW plugin
├── vera-eb1.plugin                    ← Bundled EB-1 plugin
│
└── GoogleScholar/                     ← Citation data tool
    ├── scholar.py
    ├── scholar_colab_demo.ipynb
    └── requirements.txt

FAQ

Is this legal advice?
No. See DISCLAIMER.md. These tools provide informational guidance only. Always consult a qualified immigration attorney for your specific case.

Do I need to be technical?
No. If you can use Claude, you can use these skills. Copy, paste, follow the prompts.

Will this guarantee my NIW approval?
No tool or attorney can guarantee approval. These skills help you identify weaknesses and build a stronger petition before filing.

How is this different from ChatGPT prompts for NIW?
Generic prompts produce generic output. Each skill here encodes hundreds of specific decision rules — failure pattern detection, field-specific framing, USCIS-language calibration — derived from systematic analysis of AAO decisions. The difference is the same as between asking a friend for advice and consulting a specialist.

Can I use this with GPT-4 or other models?
The skills are optimized for Claude but the instructions are model-agnostic text. They may work with other capable models, though output quality may vary.

Can I contribute?
Yes. See CONTRIBUTING.md.

Contributing

Contributions welcome. See CONTRIBUTING.md for details. Especially valuable:

  • Bug reports — Skill produced incorrect or misleading guidance? Open an issue.
  • Test cases — Real RFE patterns or edge cases for evals/. Anonymize all personal information.
  • Reference materials — New AAO decisions, policy updates, or adjudication trend data.
  • Skill improvements — Better rubrics, additional failure patterns, improved prompts.

Disclaimer

These tools are for informational and educational purposes only. They do not constitute legal advice and do not create an attorney-client relationship. See DISCLAIMER.md for full terms.

License

GPL-3.0 License. See LICENSE.

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