Vera EB Suite

Hi, I'm Vera — a silicon-based rabbit who documents the open-source skills Veronica created.

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 this suite to systematize the parts of evidence-building and petition preparation that can be decomposed, documented, and reviewed. I help structure the workflows. She reviews, tests, and decides what ships.

Everything in this repo is what can be made explicit: evidence organization, gap spotting, document drafting support, and review workflows. What the suite cannot do is assess whether your specific case will be approved, provide legal advice, or replace an experienced immigration attorney. That remains a human and legal judgment.

Open-source Claude Code and Codex-compatible skills for EB-1 and EB-2 NIW evidence-building and petition-preparation support — from evidence review and case organization to drafting workflows, recommendation-letter support, pre-filing review, and RFE response preparation.

Each skill encodes structured reasoning patterns derived from public USCIS materials, AAO decisions, policy guidance, and evidence-organization workflows. The repo ships Claude Code plugin bundles plus plain SKILL.md folders inside .skill archives that can also be installed into Codex.

Across VeraSuperHub, Vera structures execution; humans own judgment.

Why this exists: Immigration petitions are high-stakes, and information asymmetry can make the process harder than it needs to be. Many parts of evidence-building and petition preparation follow patterns that can be made explicit: organizing exhibits, identifying gaps, mapping evidence to criteria, drafting structured narratives, and stress-testing a petition before filing. This project decomposes those repeatable parts into modular, testable, improvable skills — while leaving legal strategy, approval assessment, and final judgment to qualified human professionals.

---

The Pipelines

NIW Pipeline (EB-2 National Interest Waiver)

  ┌──────────────┐     ┌──────────────┐     ┌──────────────┐
  │  1. EVALUATE │────▶│ 2. ENDEAVOR  │────▶│  3. PILLAR   │
  │  Evidence-   │     │  Endeavor    │     │  ×3 runs     │
  │  readiness   │     │  statement   │     │  (one per    │
  │  summary     │     │  + 3 pillar  │     │   pillar)    │
  └──────────────┘     │  seeds       │     └──────┬───────┘
                       └──────────────┘            │
                                                   ▼
    ┌─────────────┐     ┌─────────────┐     ┌──────────────┐
    │ 7. RFE      │     │ 6. PL       │◀────│ 5. ASSEMBLE  │
    │ RESPONSE    │     │ REVIEW      │     │  Review-     │
    │ DRAFTING    │     │ Adversarial │     │  ready       │
    │ SUPPORT     │     │ pre-filing  │     │  draft .docx │
    └─────────────┘     │ check       │     └──────┬───────┘
                        └─────────────┘            │
                              ▲              ┌─────┴────────┐
                              └──────────────│ 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                 │
  │  Evidence-   │     │  ┌────────────┐  ┌────────────────┐  │
  │  readiness   │     │  │ AUTHORSHIP │  │ ORIGINAL       │  │
  │  summary +   │     │  │ (Crit. 6)  │  │ CONTRIBUTIONS  │  │
  │  pathway     │     │  └────────────┘  │ (Crit. 5)      │  │
  │  framing     │     │  ┌────────────┐  └────────────────┘  │
  │  (EB-1A /    │     │  │ JUDGING    │  ┌────────────────┐  │
  │  EB-1B)      │     │  │ (Crit. 4)  │  │ CRITICAL ROLE  │  │
  └──────────────┘     │  └────────────┘  │ (Crit. 8)      │  │
                       │  ┌────────────┐  └────────────────┘  │
                       │  │ PUBLISHED  │                      │
                       │  │ MATERIAL   │                      │
                       │  │ (Crit. 3)  │                      │
                       │  └────────────┘                      │
                       └──────────────────┬───────────────────┘
                                          ▼
    ┌─────────────┐     ┌─────────────┐  ┌──────────────┐
    │ 6. RFE      │     │ 5. PL       │◀─│ 4. ASSEMBLE  │
    │ RESPONSE    │     │ REVIEW      │  │  Review-     │
    │ DRAFTING    │     │ Adversarial │  │  ready       │
    │ SUPPORT     │     │ pre-filing  │  │  draft .docx │
    └─────────────┘     │ check       │  └──────┬───────┘
                        └─────────────┘         │
                              ▲           ┌─────┴────────┐
                              └───────────│ 3. RECOMMEND │
                                          │  + FINAL     │
                                          │  MERITS      │
                                          │  DRAFTING    │
                                          └──────────────┘

---

Skills

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

#	Skill	What It Does
1	vera-niw-evaluate	Reviews the petitioner's profile, maps available evidence to NIW requirements, identifies strengths, gaps, and risk areas, and produces an evidence-readiness summary for human review
2	vera-niw-endeavor	Drafts a proposed endeavor statement and related framing options based on the petitioner's field, evidence, and intended contribution, for human review and revision
3	vera-niw-pillar	Drafts structured petition-letter sections for the three NIW prongs: substantial merit and national importance, well-positioned, and balance of equities. Designed for human review and revision
4	vera-niw-recommendation	Drafts recommendation-letter support materials with recommender-specific framing, evidence mapping, and nonredundant emphasis areas for human review
5	vera-niw-assemble	Assembles a review-ready petition-support package — petition-letter draft, exhibit list, and supporting-document structure — with cross-reference checks
6	vera-niw-pl-review	Runs an adversarial pre-filing review using public AAO reasoning patterns and common evidence weaknesses to identify gaps, ambiguity, and potential RFE triggers
7	vera-niw-rfe-response	Drafts a structured point-by-point RFE response framework that organizes each USCIS finding, relevant evidence, updated metrics, and potential new exhibits for human review
8	vera-niw-entrepreneur	Reviews entrepreneur/founder NIW evidence using the USCIS Policy Manual's entrepreneur-specific framework and identifies evidence gaps, narrative risks, and documentation needs

Got a weak research profile? If vera-niw-evaluate or vera-eb1-evaluate flags thin publications or citation impact, see ai-research-pipeline and stat-research-pipeline — sister skill suites that structure the execution layer of an AI/statistical research workflow (diagnostics, candidate analyses, manuscript-section drafting, review checkpoints) for human-led research production.

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

#	Skill	What It Does
1	vera-eb1-evaluate	Reviews evidence for EB-1A and EB-1B pathways, maps available materials to relevant criteria, identifies strengths, gaps, and risk areas, and produces an evidence-readiness summary for human review
2	vera-eb1-authorship	Maps scholarly authorship evidence to the relevant EB-1 criterion, including publication venues, citation context, authorship role, and evidence gaps for human review
3	vera-eb1-original-contributions	Drafts support for organizing original-contribution evidence, including contribution framing, adoption or impact signals, before/after context, and documentation gaps for human review
4	vera-eb1-judging	Organizes judging evidence, such as peer review, panels, editorial service, and evaluation roles, and maps it to the relevant EB-1 criterion for human review
5	vera-eb1-critical-role	Organizes evidence for leading or critical roles, including role context, organizational distinction, scope of responsibility, and impact documentation for human review
6	vera-eb1-published-material	Organizes published-material evidence about the petitioner, including source credibility, media context, relevance, and documentation gaps for human review
7	vera-eb1-recommendation	Drafts recommendation-letter support materials with recommender-specific framing, evidence mapping, and nonredundant emphasis areas for human review
8	vera-eb1-final-merits	Drafts final-merits argument support by organizing sustained-acclaim evidence, criteria-level outputs, impact signals, and narrative risks for human review
9	vera-eb1-assemble	Assembles a review-ready EB-1 petition-letter draft and supporting evidence structure as a formatted .docx
10	vera-eb1-pl-review	Runs an adversarial pre-filing review using the Kazarian two-step framework, public AAO reasoning patterns, and common evidence weaknesses to identify gaps, ambiguity, and potential RFE triggers
11	vera-eb1-rfe-response	Drafts a structured point-by-point EB-1 RFE response framework that organizes each USCIS finding, relevant evidence, updated metrics, and potential new exhibits for human review

Total: 19 skills across both petition categories.

Got a weak research profile? If vera-niw-evaluate or vera-eb1-evaluate flags thin publications or citation impact, see ai-research-pipeline and stat-research-pipeline — sister skill suites that structure the execution layer of an AI/statistical research workflow (diagnostics, candidate analyses, manuscript-section drafting, review checkpoints) for human-led research production.

---

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

• Claude Code, claude.ai with Skills enabled, or Codex with local skills support
• Code execution enabled when using tools that require it

Installation

There are three ways to install: plugins for Claude Code, individual .skill uploads for claude.ai, and extracted skill folders for Codex.

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.

Option C — Individual Skills (Codex)

For Codex, each .skill file is a zip archive containing a standard skill folder with SKILL.md plus its supporting references/, docs/, evals/, and schemas. Extract the archives into your Codex skills directory:

# Install all petition skills into the default Codex skills directory
mkdir -p ~/.codex/skills
for f in vera-niw-skillset/*.skill vera-eb1-skillset/*.skill; do
  unzip -oq "$f" -d ~/.codex/skills
done

After extraction, Codex can discover the skills from ~/.codex/skills/<skill-name>/SKILL.md.

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](https://cola