OSINT Investigation — Public Records Cross-Reference

Investigative framework for public-records OSINT: government contracts, corporate filings, lobbying, sanctions, offshore leaks, property records, court records, web archives, knowledge bases, and global news. Resolve entities across heterogeneous sources, build cross-links with explicit confidence, run statistical timing tests, and produce structured evidence chains.

Python stdlib only. Zero install. Works on Linux, macOS, Windows. Most sources work with no API key (OpenCorporates has an optional free token that raises rate limits).

Adapted from the MIT-licensed ShinMegamiBoson/OpenPlanter project; expanded to cover identity / property / litigation / archives / news sources that the original didn't address.

When to use this skill

Use when the user asks for:

• "follow the money" — government contracts, lobbying → legislation, sanctions
• corporate due diligence — who controls company X, where are they

incorporated, who serves on their boards, what filings have they made

• sanctions screening — is entity X on OFAC SDN, ICIJ offshore leaks
• pay-to-play investigation — contractors with offshore ties, lobbying

clients winning awards

• property ownership — find recorded deeds/mortgages by name or address

(NYC; for other counties point users at the relevant recorder)

• litigation history — find federal + state court opinions and PACER dockets
• multi-source entity resolution where naming varies (LLC suffixes, abbreviations)
• evidence-chain construction with explicit confidence levels
• "what's been said about X" — international news (GDELT) + Wikipedia

narrative + Wayback Machine to recover dead URLs

Do NOT use this skill for:

• general web research → web_search / web_extract
• domain/infrastructure OSINT → domain-intel skill
• academic literature → arxiv skill
• social-media profile discovery → sherlock skill (optional)
• US federal campaign finance — FEC is intentionally NOT covered here

(the API is unreliable for ad-hoc contributor-name queries on the free DEMO_KEY tier). For federal donations, point users at https://www.fec.gov/data/ directly.

Workflow

The agent runs scripts via the terminal tool. SKILL_DIR is the directory holding this SKILL.md.

1. Identify which sources apply

Read the data-source wiki entries to plan the investigation:

ls SKILL_DIR/references/sources/

# Federal financial / regulatory
cat SKILL_DIR/references/sources/sec-edgar.md       # corporate filings
cat SKILL_DIR/references/sources/usaspending.md     # federal contracts
cat SKILL_DIR/references/sources/senate-ld.md       # lobbying
cat SKILL_DIR/references/sources/ofac-sdn.md        # sanctions
cat SKILL_DIR/references/sources/icij-offshore.md   # offshore leaks

# Identity / property / litigation / archives / news
cat SKILL_DIR/references/sources/nyc-acris.md       # NYC property records
cat SKILL_DIR/references/sources/opencorporates.md  # global corporate registry
cat SKILL_DIR/references/sources/courtlistener.md   # court records (federal + state)
cat SKILL_DIR/references/sources/wayback.md         # Wayback Machine archives
cat SKILL_DIR/references/sources/wikipedia.md       # Wikipedia + Wikidata
cat SKILL_DIR/references/sources/gdelt.md           # global news monitoring

Each entry follows a 9-section template: summary, access, schema, coverage, cross-reference keys, data quality, acquisition, legal, references.

The cross-reference potential section maps join keys between sources — read those first to pick the right pair.

2. Acquire data

Each source has a stdlib-only fetch script in SKILL_DIR/scripts/:

Federal financial / regulatory

bash
# SEC EDGAR filings (corporate disclosures)
python3 SKILL_DIR/scripts/fetch_sec_edgar.py --cik 0000320193 \
    --types 10-K,10-Q --out data/edgar_filings.csv

# USAspending federal contracts
python3 SKILL_DIR/scripts/fetch_usaspending.py --recipient "EXAMPLE CORP" \
    --fy 2024 --out data/contracts.csv

# Senate LD-1 / LD-2 lobbying disclosures
python3 SKILL_DIR/scripts/fetch_senate_ld.py --client "EXAMPLE CORP" \
    --year 2024 --out data/lobbying.csv

# OFAC SDN sanctions list (full snapshot)
python3 SKILL_DIR/scripts/fetch_ofac_sdn.py --out data/ofac_sdn.csv

# ICIJ Offshore Leaks — downloads ~70 MB bulk CSV on first use,
# then searches it locally. Cached for 30 days under
# $HERMES_OSINT_CACHE/icij/ (default: ~/.cache/hermes-osint/icij/).
python3 SKILL_DIR/scripts/fetch_icij_offshore.py --entity "EXAMPLE CORP" \
    --out data/icij.csv

Identity / property / litigation / archives / news

bash
# NYC property records (deeds, mortgages, liens) — ACRIS via Socrata
python3 SKILL_DIR/scripts/fetch_nyc_acris.py --name "SMITH, JOHN" \
    --out data/acris.csv
python3 SKILL_DIR/scripts/fetch_nyc_acris.py --address "571 HUDSON" \
    --out data/acris_addr.csv

# OpenCorporates — 130+ jurisdiction corporate registry
# (free token required; set OPENCORPORATES_API_TOKEN or pass --token)
python3 SKILL_DIR/scripts/fetch_opencorporates.py --query "Example Corp" \
    --jurisdiction us_ny --out data/opencorporates.csv

# CourtListener — federal + state court opinions, PACER dockets
python3 SKILL_DIR/scripts/fetch_courtlistener.py --query "Smith v. Example Corp" \
    --type opinions --out data/courts.csv

# Wayback Machine — historical web captures
python3 SKILL_DIR/scripts/fetch_wayback.py --url "example.com" \
    --match host --collapse digest --out data/wayback.csv

# Wikipedia + Wikidata — narrative bio + structured facts
# Set HERMES_OSINT_UA=your-app/1.0 (your@email) to identify yourself
python3 SKILL_DIR/scripts/fetch_wikipedia.py --query "Bill Gates" \
    --out data/wp.csv

# GDELT — global news in 100+ languages, ~2015→present
python3 SKILL_DIR/scripts/fetch_gdelt.py --query '"Example Corp"' \
    --timespan 1y --out data/gdelt.csv

All outputs are normalized CSV with a header row. Re-run scripts idempotently.

When a private individual won't be in a source (e.g. SEC EDGAR for a non-public- company person, USAspending for someone who isn't a federal contractor, Senate LDA for someone who isn't a lobbying client), the script returns 0 rows with a clear warning rather than silently writing an empty CSV. EDGAR specifically flags when the company-name resolver matched an individual Form 3/4/5 filer rather than a corporate registrant.

Rate-limit notes are in each source's wiki entry. Default fetchers sleep politely between paginated requests. API keys raise rate limits for sources that support them (SEC_USER_AGENT, SENATE_LDA_TOKEN, OPENCORPORATES_API_TOKEN, COURTLISTENER_TOKEN). All scripts surface 429 responses immediately with the upstream's quota message so the user knows to slow down or supply a key.

3. Resolve entities across sources

Normalize names and find matches between two CSV files:

bash
# Match lobbying clients (Senate LDA) against contract recipients (USAspending)
python3 SKILL_DIR/scripts/entity_resolution.py \
    --left  data/lobbying.csv   --left-name-col  client_name \
    --right data/contracts.csv  --right-name-col recipient_name \
    --out data/cross_links.csv

Three matching tiers with explicit confidence:

| Tier | Method | Confidence | |------|--------|------------| | exact | Normalized strings equal after suffix/punctuation strip | high | | fuzzy | Sorted-token equality (word-bag match) | medium | | token_overlap | ≥60% token overlap, ≥2 shared tokens, tokens ≥4 chars | low |

Output cross_links.csv columns: match_type, confidence, left_name, right_name, left_normalized, right_normalized, left_row, right_row.

4. Statistical timing correlation (optional)

Test whether two time series cluster suspiciously close together — e.g. lobbying filings near contract awards — using a permutation test:

bash
python3 SKILL_DIR/scripts/timing_analysis.py \
    --donations data/lobbying.csv --donation-date-col filing_date \
        --donation-amount-col income --donation-donor-col client_name \
        --donation-recipient-col registrant_name \
    --contracts data/contracts.csv --contract-date-col award_date \
        --contract-vendor-col recipient_name \
    --cross-links data/cross_links.csv \
    --permutations 1000 \
    --out data/timing.json

The script's column flags are intentionally generic — the original tool was written for donations vs awards, but it works for any (event, payee) time series joined through cross-links. Null hypothesis: event timing is independent of award dates. One-tailed p-value = fraction of permutations with mean nearest-award distance ≤ observed. Minimum 3 events per (payer, vendor) pair to run the test.

5. Build the findings JSON (evidence chain)

bash
python3 SKILL_DIR/scripts/build_findings.py \
    --cross-links data/cross_links.csv \
    --timing data/timing.json \
    --out data/findings.json

Every finding has id, title, severity, confidence, summary, evidence[], sources[]. Each evidence item points back to a specific row in a source CSV. The user (or a follow-up agent) can verify every claim against its source.

Confidence and evidence discipline

This is the load-bearing rule of the skill. Tell the user:

• Every claim must trace to a record. No naked assertions.
• Confidence tier travels with the claim. match_type=fuzzy is "probable",

not "confirmed."

• Entity resolution produces candidates, NOT conclusions. A fuzzy match

between "ACME LLC" and "Acme Holdings Group" is a lead, not a fact.

• Statistical significance ≠ wrongdoing. p < 0.05 means the timing pattern

is unlikely under the null. It does not establish corruption.

• All data sources here are public records. They may still contain

inaccuracies, stale info, or redactions (GDPR, sealed records).

Adding a new data source

Use the template:

bash
cp SKILL_DIR/templates/source-template.md \
    SKILL_DIR/references/sources/<your-source>.md

Fill in all 9 sections. Write a fetch_<source>.py script in scripts/ that uses stdlib only and writes a normalized CSV. Update the source list in the "When to use" section above.

Tools and their limits

• entity_resolution.py does NOT use external fuzzy libraries (no rapidfuzz,

no jellyfish). Token-bag matching is the upper bound here. If you need Levenshtein, transliteration, or phonetic matching, pip-install separately.

• timing_analysis.py uses Python's random for permutations. For

reproducibility, pass --seed N.

• fetch_*.py scripts use urllib.request and respect Retry-After. Heavy

bulk usage may still violate ToS — read each source's legal section first.

Legal note

All Phase-1 sources are public records. Bulk acquisition is permitted under their respective access terms (FOIA, public records law, ICIJ explicit publication, OFAC public data). However:

• Some sources rate-limit aggressively. Respect their headers.
• Some redact registrant info (GDPR on WHOIS, sealed filings).
• Cross-referencing public records to identify private individuals can have

ethical implications. The skill produces evidence chains, not accusations.