Sample Corpus

samples/ contains 150 Python generators producing synthetic fund documents across 8 fund families and 47 standalone files.

Fund Families

Family	Strategy	Documents
Apex Capital	PE buyout	14
Greenfield Ventures	VC	12
Cornerstone RE	Real estate	19
European Growth	Luxembourg PE	10
Pacific Credit	Credit/lending	14
Meridian Secondaries	GP-led continuation	10
Catalyst Infrastructure	UK infrastructure	14
Summit Macro	Hedge fund	10
Standalone files	Cross-cutting	47

Each family is a coherent set of related documents, enabling cross-document skills: compare, reconcile, multi-doc-analyze, mfn-tracker, and playbook.

Intentional Issues

Every document embeds intentional issues for testing detection accuracy:

• Mathematical errors (carried interest calculations, distribution waterfalls)
• Compliance gaps (missing disclosures, regulatory deadline errors)
• Cross-document conflicts (side letter terms inconsistent with LPA)
• Wire fraud signals (suspicious wire instruction changes)

The sample LPA (generate_sample_lpa.py) contains 10 specific issues. See LPA Safety Score for the full list.

Generating the Corpus

cd fundadmin-ai

# Generate all 150 documents
python3 samples/generate_all.py

# Generate one fund family only
python3 samples/generate_all.py --family apex

# Compile-check only (no output files)
python3 samples/generate_all.py --check

# List all generators
python3 samples/generate_all.py --list

Generated output goes to samples/output/ (gitignored — reproducible from scripts).

Individual Generators

# Single document types (each with intentional issues)
python3 generate_sample_lpa.py            # LPA — 10 issues
python3 generate_sample_ppm.py            # PPM — 7 issues
python3 generate_sample_side_letter.py    # Side letter — 3 issues
python3 generate_sample_subscription.py   # Subscription — 5 issues

Shared Utilities

samples/generators/common.py provides:

• Document styles and setup
• Fund family constants
• Investor profiles
• Standard issue injection helpers

Classifier Accuracy

The content classifier is regression-tested to maintain ≥ 97% accuracy across all 150 PDFs:

node tests/audit-classifier.mjs
python3 -m unittest tests/test_classifier_accuracy.py -v

Catalog

See samples/SAMPLE-CATALOG.md for the full index with a list of embedded issues per document.