mirror of https://github.com/NousResearch/hermes-agent.git synced 2026-05-26 06:01:49 +00:00

feat(skills): add osint-investigation optional skill (closes #355 ) (#26729 )

* feat(skills): add osint-investigation optional skill (closes #355)

Phase-1 public-records OSINT investigation framework adapted from
ShinMegamiBoson/OpenPlanter (MIT). Lives in optional-skills/research/.

Six data-source wiki entries (FEC, SEC EDGAR, USAspending, Senate LD,
OFAC SDN, ICIJ Offshore Leaks), each following the 9-section template:
summary, access, schema, coverage, cross-reference keys, data quality,
acquisition, legal, references.

Six stdlib-only acquisition scripts that emit normalized CSV, plus three
analysis scripts:

  - entity_resolution.py  — three-tier match (exact / fuzzy / token overlap)
                            with explicit confidence per row
  - timing_analysis.py    — permutation test for donation/contract timing
                            correlation, joins through cross-links
  - build_findings.py     — assembles structured findings.json with
                            evidence chains pointing back to source rows

Validation: full pipeline runs end-to-end on synthetic fixtures. Entity
resolution found 24 cross-matches with 0 false positives on a 5-row /
4-row test set. Timing analysis on 5 donations clustered near 3 awards
returned p=0.000, effect size 2.41 SD. Findings JSON correctly tags
HIGH-severity timing pattern. All 9 scripts pass --help and py_compile.

Docs site page auto-generated by website/scripts/generate-skill-docs.py;
sidebar + catalog entries updated by the same generator.

* fix(osint-investigation): live API fixes from end-to-end sweep

Live-tested the skill on a real public-citizen query and found three bugs
the synthetic E2E missed. All three are now fixed and re-verified.

1. FEC fetch hung on contributor name searches.
   The combination of two_year_transaction_period + sort=date +
   contributor_name puts the OpenFEC query plan on a slow path that the
   upstream gateway times out (25s+). Switched to min_date/max_date with no
   explicit sort. Renamed --candidate to --contributor (the original name
   was misleading: FEC searches by donor, not by candidate; --candidate is
   kept as a deprecated alias). Added --state filter for narrowing.

2. ICIJ Offshore Leaks reconcile endpoint returns 404.
   ICIJ removed the Open Refine reconciliation API. Rewrote
   fetch_icij_offshore.py to download the official bulk CSV ZIP (~70 MB,
   public, no auth) and search it locally. Cached under
   $HERMES_OSINT_CACHE/icij/ (default ~/.cache/hermes-osint/icij/) for
   30 days, --force-refresh to refetch. Verified live: 'PUTIN' query
   returns 5 Panama Papers officer matches in 0.5s after first download.

3. SEC EDGAR silently returned 0 when the company-name resolver matched
   an individual Form 3/4/5 filer (insider trading disclosures).
   Now surfaces 'Resolved company X → CIK Y (Z)' on stderr, prints a
   filing-type histogram when the type filter wipes results, and
   explicitly warns when the matched CIK appears to be an individual
   filer rather than a corporate registrant.

Bonus: _http.py was retrying 429 responses with exponential backoff plus
honoring (often-missing) Retry-After headers, which compounded into
multi-second hangs per page when the upstream key was over quota.
Changed to fail-fast on 429 with a clear, actionable error showing the
upstream's quota message. Verified: 0.3s fast-fail vs the previous 60s
hang on DEMO_KEY rate-limit exhaustion.

Updated SKILL.md, fec.md, and icij-offshore.md to match the new CLI
flags and ICIJ bulk-cache flow. Regenerated the docusaurus page via
website/scripts/generate-skill-docs.py.

Live sweep results across all 6 sources for 'Dillon Rolnick, New York':
- OFAC SDN: 0 matches ✓ (correctly not sanctioned)
- USAspending: 0 matches ✓ (correctly not a federal contractor)
- Senate LDA: 0 matches ✓ (correctly not a lobbying client)
- SEC EDGAR: warns it resolved to 'Rolnick Michael' (CIK 0001845264)
    who is an individual Form 3 filer, not a corporate registrant
- ICIJ: 0 matches ✓ (correctly not in any offshore leak)
- FEC: rate-limited (DEMO_KEY); fails fast with clear quota message

* feat(osint-investigation): expand to 12 sources covering identity, property, courts, archives, news

Phase-2 expansion per Teknium feedback that the original 6-source skill
(federal financial/regulatory only) wasn't a complete OSINT toolkit. Adds
6 more sources covering the major omissions a real investigation would
reach for first.

New sources (6 fetch scripts + 6 wiki entries):

1. NYC ACRIS — Real property records (deeds, mortgages, liens) via the
   city's Socrata API. Search by party name or property address. Joins
   Parties to Master to populate doc_type, dates, borough, and amount.
   Coverage: 5 NYC boroughs, ~70M party records, 1966-present.

2. OpenCorporates — Global corporate registry covering 130+ jurisdictions
   (~200M companies). Free API token at
   https://opencorporates.com/api_accounts/new raises the rate limit;
   HTML fallback works without one (limited fields).

3. CourtListener (Free Law Project) — federal + state court opinions
   (~10M back to colonial era) + PACER dockets via RECAP. Anonymous v4
   search works; COURTLISTENER_TOKEN raises rate limits.

4. Wayback Machine CDX — historical web captures (~900B+). Used both for
   surveillance-of-record (when did this site change?) and as a
   content-recovery layer when other sources point to dead URLs.

5. Wikipedia + Wikidata — narrative bio + structured facts. Wikipedia
   OpenSearch for article matching, REST summary for extracts, Wikidata
   Action API (wbgetentities) for claims. Avoids the SPARQL Query
   Service which is aggressively rate-limited.

6. GDELT 2.0 DOC API — global news monitoring in 100+ languages,
   ~2015-present. Auto-retries with 6s backoff on the standard
   1-req-per-5-sec throttle.

Other changes in this commit:

- SEC EDGAR no longer raises SystemExit when the company-name resolver
  finds no CIK; writes an empty CSV with header so the rest of a
  pipeline can keep moving and the warning is just on stderr.

- _http.py User-Agent updated per Wikimedia policy: includes app name,
  version, and a 'set HERMES_OSINT_UA to identify yourself' instruction.

- SKILL.md workflow now groups sources into two clusters (federal
  financial vs identity/property/courts/archives/news) with bash
  examples for each. 'When to use this skill' lists the broader set of
  investigation patterns the expanded sources unlock.

Live sweep results on 'Dillon Rolnick, New York' across all 12 sources:

  ofac           ✓ 0 (correctly clean)
  icij           ✓ 0 (correctly not in any leak)
  usaspending    ✓ 0 (correctly not a federal contractor)
  senate_lda     ✓ 0 (correctly not a lobbying client)
  sec_edgar      ✓ 0, warns: resolved to 'Rolnick Michael' (CIK 0001845264),
                   individual Form 3 filer, NOT a corporate registrant
  fec            — rate-limited (DEMO_KEY exhausted), fails fast with
                   clear quota message
  nyc_acris      ✓ 200 records named Rolnick across NYC; 48 records at
                   571 Hudson (the property the web identifies as his)
  opencorporates ✓ 0 (no API token configured; HTML fallback)
  courtlistener  ✓ 0 for 'Dillon Rolnick'; 20 for 'Rolnick' generally;
                   5 for 'Microsoft' sanity check
  wayback        ✓ 30 captures of nousresearch.com from 2011-present
  wikipedia      ✓ 0 (correctly not notable enough); Bill Gates sanity
                   returns full structured facts (occupation, employer,
                   DOB, place of birth, country)
  gdelt          ✓ 0 for 'Dillon Rolnick'; 5 for 'Nous Research'

All 17 scripts compile clean and pass --help. Synthetic analysis pipeline
regression still passes (entity_resolution 30 matches, timing p=0.000,
findings 2).

* feat(osint-investigation): remove FEC; DEMO_KEY rate-limits make it unreliable

The FEC fetcher consistently failed the live sweep because the OpenFEC
DEMO_KEY tier (40 calls/hour) exhausts on a single investigation, and
the upstream returns slow-path query plans for unindexed contributor-name
searches that the gateway times out. Without a real API key it's not
usable; with one the user has to sign up at api.data.gov first. That's
too much setup friction for a skill that should work out of the box.

Removed:
  - scripts/fetch_fec.py
  - references/sources/fec.md

Updated:
  - SKILL.md frontmatter description + tags
  - 'When NOT to use' now points users at https://www.fec.gov/data/ for
    federal donations
  - entity_resolution example switched from donor↔contractor to
    lobbying-client↔contractor (Senate LDA + USAspending pair)
  - timing_analysis example switched to lobbying-filings vs awards
  - 8 wiki entries had their 'FEC ↔ ...' cross-reference bullets removed

11 sources remain (5 federal financial + 6 identity/property/courts/
archives/news). All scripts compile, pass --help, and the synthetic
analysis pipeline still passes on the new lobbying-shaped regression
fixture (30 matches, p=0.000 on tight clustering, 2 findings).

2026-05-16 01:55:06 -07:00

3.8 KiB

Raw Blame History

USAspending — Federal Government Contracts and Grants

1. Summary

USAspending.gov is the official source of federal spending data. Coverage: contracts, grants, loans, direct payments, sub-awards. Required by the DATA Act of 2014 — all federal agencies must report to a single schema.

2. Access Methods

API v2: https://api.usaspending.gov/api/v2/ (no auth, no key)
Bulk: https://files.usaspending.gov/ (CSV / Parquet by award type)
Auth: None
Rate limit: Not strictly enforced, but be polite — keep to <10 req/s

3. Data Schema

Key fields emitted by fetch_usaspending.py (prime awards):

Column	Type	Description
`award_id`	str	Federal award ID (PIID for contracts, FAIN for grants)
`recipient_name`	str	Awardee legal name
`recipient_uei`	str	Unique Entity Identifier (replaced DUNS in 2022)
`recipient_duns`	str	Legacy DUNS number (historical only)
`recipient_parent_name`	str	Ultimate parent organization
`recipient_state`	str	Recipient state
`awarding_agency`	str	Department / agency name
`awarding_sub_agency`	str	Sub-tier (e.g. DoD → Army)
`award_type`	str	Contract / Grant / Loan / Direct Payment
`award_amount`	float	Current total obligation in USD
`award_date`	str	Action / signed date YYYY-MM-DD
`period_of_performance_start`	str	YYYY-MM-DD
`period_of_performance_end`	str	YYYY-MM-DD
`naics_code`	str	Industry classification
`psc_code`	str	Product / Service Code
`competition_extent`	str	Full / limited / sole-source
`description`	str	Award description (free-text)

4. Coverage

US federal awards only (state/local not included)
FY 2008 → present (full coverage from FY 2017)
Updated bi-weekly from agency reporting
~100M+ transaction records cumulative

5. Cross-Reference Potential

SEC EDGAR ↔ recipient_name (public companies as contractors)
Senate LD ↔ recipient_name (lobbying clients winning contracts)
OFAC SDN ↔ recipient_name (sanctions screening of contractors — must be filtered out by SAM.gov but verify)
ICIJ Offshore ↔ recipient_name (offshore-linked contractors)

Join key: normalized recipient name. UEI is canonical when present.

6. Data Quality

DUNS → UEI transition (April 2022) — old records have DUNS, new records have UEI
Some sub-awards aren't reported (FFATA threshold is $30k)
Award amount changes over time (mod actions) — fetch script reports current total
competition_extent field is free-text in older records — fetch_usaspending.py normalizes to canonical values
Recipient name variations are extensive — "ACME LLC", "Acme L.L.C.", "ACME, INC" all appear. Use entity_resolution.py.

7. Acquisition Script

Path: scripts/fetch_usaspending.py

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

# By awarding agency
python3 SKILL_DIR/scripts/fetch_usaspending.py --agency "Department of Defense" \
    --fy 2024 --out data/contracts.csv

# Filter to sole-source only
python3 SKILL_DIR/scripts/fetch_usaspending.py --recipient "EXAMPLE CORP" \
    --fy 2024 --sole-source-only --out data/contracts.csv

8. Legal & Licensing

Public record under the Federal Funding Accountability and Transparency Act (FFATA, 2006) and DATA Act (2014)
No commercial use restrictions on the data
Personal information of award recipients (e.g. small business owners' addresses in some grants) should be handled per the source agency's privacy notice

9. References

API docs: https://api.usaspending.gov/
Data dictionary: https://www.usaspending.gov/data-dictionary
Award schema: https://files.usaspending.gov/docs/Data_Dictionary_Crosswalk.xlsx

3.8 KiB Raw Blame History