Osint Investigation — Follow the money via public records and sanctions data
Osint Investigation — Follow the money via public records and sanctions data
Start with meaning, then move to detail.
This lesson explains Osint Investigation — Follow the money via public records and sanctions data as part of extending Hermes and connecting external tools. You will learn what it does, when it matters, and the smallest safe test that proves it works.
If you are new, do not memorize names. Focus on three questions: what problem does this solve, what access does it need, and how can you verify the result?
For practice, inspect the first example, identify its effects, run it on test data, and compare the result with the source claim.
For advanced readers, inspect Skill metadata, Reference: full SKILL.md, When to use this skill, then verify failure modes and version compatibility.
Complete installation and one successful task before adding new capabilities.
A clear outcome before you read.
- Understand Osint Investigation — Follow the money via public records and sanctions data without assumed prior knowledge.
- Separate the source description from what still needs testing in your environment.
- Read the first command and identify its inputs and outputs before copying it.
Short definitions before the details.
- Skill
- An instruction bundle that teaches Hermes a repeatable workflow without necessarily adding an external service.
Follow the money via public records and sanctions data
What does the source say, and in what order?
- 01Skill metadata
Start here to understand the core idea or structure.
- 02Reference: full SKILL.md
Read this after the foundation, then connect it to the previous step.
- 03When to use this skill
Read this after the foundation, then connect it to the previous step.
- 04Workflow
Read this after the foundation, then connect it to the previous step.
- 051. Identify which sources apply
Read this after the foundation, then connect it to the previous step.
- 062. Acquire data
Read this after the foundation, then connect it to the previous step.
- 073. Resolve entities across sources
Read this after the foundation, then connect it to the previous step.
- 084. Statistical timing correlation (optional)
Read this after the foundation, then connect it to the previous step.
- 095. Build the findings JSON (evidence chain)
Read this after the foundation, then connect it to the previous step.
- 10Confidence and evidence discipline
Finish here to verify the result and special cases.
Copy only after you understand the effect.
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 cor# 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 — dow# 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_DIRead the first command and identify its inputs and outputs before copying it.
Match every command to your installed Hermes version, review the files and accounts it can reach, and use non-sensitive data for the first test. If this explanation differs from the source, the official source wins.