osint-investigation skill

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:

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

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

clients winning awards

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

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

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

narrative + Wayback Machine to recover dead URLs

• 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

Do NOT use this skill for:

(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.

• general web research → websearch / webextract
• 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

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

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

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

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

# OFAC SDN sanctions list (full snapshot)
python 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/).
python SKILL_DIR/scripts/fetch_icij_offshore.py --entity "EXAMPLE CORP" \
    --out data/icij.csv

Identity / property / litigation / archives / news

# NYC property records (deeds, mortgages, liens) — ACRIS via Socrata
python SKILL_DIR/scripts/fetch_nyc_acris.py --name "SMITH, JOHN" \
    --out data/acris.csv
python 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)
python SKILL_DIR/scripts/fetch_opencorporates.py --query "Example Corp" \
    --jurisdiction us_ny --out data/opencorporates.csv

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

# Wayback Machine — historical web captures
python 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
python SKILL_DIR/scripts/fetch_wikipedia.py --query "Bill Gates" \
    --out data/wp.csv

# GDELT — global news in 100+ languages, ~2015→present
python SKILL_DIR/scripts/fetch_gdelt.py --query '"Examp

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 (SECUSERAGENT, SENATELDATOKEN, OPENCORPORATESAPITOKEN, 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:

# Match lobbying clients (Senate LDA) against contract recipients (USAspending)
python 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:

Output crosslinks.csv columns: matchtype, confidence, leftname, rightname, leftnormalized, rightnormalized, leftrow, rightrow.

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:

python 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)

python 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:

not "confirmed."

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

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

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

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

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

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

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

Adding a new data source

Use the template:

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

Fill in all 9 sections. Write a fetch_.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