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* 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).
184 lines
6.6 KiB
Python
184 lines
6.6 KiB
Python
#!/usr/bin/env python3
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"""Fetch SEC EDGAR filings index for a given CIK or company name.
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SEC requires a User-Agent header with contact info. Set SEC_USER_AGENT,
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e.g. SEC_USER_AGENT="Research example@example.com".
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Filings JSON is published at:
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https://data.sec.gov/submissions/CIK<10-digit-padded>.json
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Company lookup uses:
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https://www.sec.gov/cgi-bin/browse-edgar?action=getcompany&company=<name>&output=atom
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"""
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from __future__ import annotations
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import argparse
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import csv
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import os
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import re
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import sys
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from pathlib import Path
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sys.path.insert(0, str(Path(__file__).parent))
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from _http import get, get_json # noqa: E402
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SUBMISSIONS_URL = "https://data.sec.gov/submissions/CIK{cik}.json"
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COLUMNS = [
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"cik",
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"company_name",
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"form_type",
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"filing_date",
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"accession_number",
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"primary_document",
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"filing_url",
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"reporting_period",
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]
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def _ua() -> str:
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ua = os.environ.get("SEC_USER_AGENT", "").strip()
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if not ua:
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raise SystemExit(
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"SEC requires a User-Agent with contact info. "
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"Set SEC_USER_AGENT='Your Name your@email'."
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)
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return ua
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def _resolve_cik(company: str) -> tuple[str, str]:
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"""Resolve a company name to a CIK via EDGAR's atom feed.
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Returns (cik, resolved_company_name). The feed entries also reveal whether
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the match is an individual filer (Form 3/4/5 only) — surfaced in the
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return value so callers can warn.
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"""
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url = "https://www.sec.gov/cgi-bin/browse-edgar"
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params = {"action": "getcompany", "company": company, "output": "atom", "owner": "include"}
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body = get(url, params=params, user_agent=_ua()).decode("utf-8", errors="replace")
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m = re.search(r"CIK=(\d{10})", body)
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if not m:
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raise SystemExit(f"Could not resolve CIK for company={company!r}")
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cik = m.group(1)
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name_m = re.search(r"<title>([^<]+)\s*\((\d{10})\)</title>", body)
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resolved = name_m.group(1).strip() if name_m else ""
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return cik, resolved
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def fetch(
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cik: str | None,
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company: str | None,
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types: list[str],
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since: str | None,
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out_path: str,
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) -> int:
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resolved_name = ""
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if not cik and company:
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try:
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cik, resolved_name = _resolve_cik(company) # type: ignore[assignment]
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except SystemExit as e:
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# Write empty CSV with header so downstream tools still work,
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# and tell the user clearly.
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print(f"SEC EDGAR: {e}", file=sys.stderr)
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Path(out_path).parent.mkdir(parents=True, exist_ok=True)
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with open(out_path, "w", newline="", encoding="utf-8") as fh:
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csv.DictWriter(fh, fieldnames=COLUMNS).writeheader()
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return 0
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if resolved_name:
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print(
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f"Resolved company={company!r} → CIK {cik} ({resolved_name})",
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file=sys.stderr,
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)
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if not cik:
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raise SystemExit("must supply --cik or --company")
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cik = cik.zfill(10)
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url = SUBMISSIONS_URL.format(cik=cik)
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payload = get_json(url, user_agent=_ua())
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if not isinstance(payload, dict):
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raise SystemExit(f"Unexpected EDGAR response shape for CIK {cik}")
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name = payload.get("name", "")
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recent = (payload.get("filings", {}) or {}).get("recent", {}) or {}
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form = recent.get("form", [])
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date = recent.get("filingDate", [])
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accession = recent.get("accessionNumber", [])
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primary_doc = recent.get("primaryDocument", [])
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period = recent.get("reportDate", [])
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# Histogram of available filing types — useful for surfacing why a filter
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# returned 0 (e.g. user asked for 10-K on an individual Form 4 filer).
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type_hist: dict[str, int] = {}
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for ftype in form:
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type_hist[ftype] = type_hist.get(ftype, 0) + 1
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type_set = {t.strip().upper() for t in types} if types else None
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rows: list[dict[str, str]] = []
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for i, ftype in enumerate(form):
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if type_set and ftype.upper() not in type_set:
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continue
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fdate = date[i] if i < len(date) else ""
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if since and fdate and fdate < since:
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continue
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acc = accession[i] if i < len(accession) else ""
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pdoc = primary_doc[i] if i < len(primary_doc) else ""
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acc_nodash = acc.replace("-", "")
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filing_url = (
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f"https://www.sec.gov/Archives/edgar/data/{int(cik)}/{acc_nodash}/{pdoc}"
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if acc and pdoc
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else ""
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)
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rows.append(
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{
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"cik": cik,
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"company_name": name,
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"form_type": ftype,
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"filing_date": fdate,
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"accession_number": acc,
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"primary_document": pdoc,
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"filing_url": filing_url,
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"reporting_period": period[i] if i < len(period) else "",
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}
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)
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Path(out_path).parent.mkdir(parents=True, exist_ok=True)
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with open(out_path, "w", newline="", encoding="utf-8") as fh:
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w = csv.DictWriter(fh, fieldnames=COLUMNS)
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w.writeheader()
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w.writerows(rows)
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if not rows and type_hist:
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top = sorted(type_hist.items(), key=lambda kv: -kv[1])[:8]
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hist_str = ", ".join(f"{t}={n}" for t, n in top)
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print(
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f"Warning: SEC EDGAR CIK {cik} ({name}) has {sum(type_hist.values())} "
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f"recent filings but NONE match types={types}. "
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f"Available form types: {hist_str}.",
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file=sys.stderr,
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)
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# Insider-filer heuristic: only Form 3/4/5 → individual person, not a company.
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company_types = {"10-K", "10-Q", "8-K", "20-F", "DEF 14A", "S-1"}
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if not (set(type_hist.keys()) & company_types):
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print(
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f"Note: CIK {cik} appears to be an INDIVIDUAL filer "
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f"(insider Form 3/4/5 only), not a corporate registrant. "
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f"The resolver may have matched an officer/director named "
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f"{company!r} rather than a company.",
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file=sys.stderr,
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)
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return len(rows)
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def main() -> int:
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p = argparse.ArgumentParser(description=__doc__)
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p.add_argument("--cik", help="Central Index Key (will be 10-digit zero-padded)")
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p.add_argument("--company", help="Resolve to CIK by company name")
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p.add_argument("--types", default="", help="Comma-separated form types (e.g. 10-K,10-Q,8-K)")
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p.add_argument("--since", help="Skip filings before YYYY-MM-DD")
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p.add_argument("--out", required=True)
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a = p.parse_args()
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types = [t for t in (a.types or "").split(",") if t.strip()]
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n = fetch(cik=a.cik, company=a.company, types=types, since=a.since, out_path=a.out)
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print(f"Wrote {n} EDGAR filing rows to {a.out}")
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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