Entity Research

Deep background research on a company or a person → a cited dossier for a human to read and act on. General-purpose research — vetting a vendor, a counterparty before a contract, a prospective hire, a partner, or verifying a media claim.

Research & compilation — NOT a determination. This skill does not screen, clear, rate, or block anyone. A sanctions-list / PEP / watchlist signal is a SIGNAL to escalate to a qualified compliance / AML function, not a finding; negative press is an allegation with a source and date, not a proven fact. Everything is cited; nothing is auto-acted on.

Scope and routing

Use this skill for general background research and fast public signals. Do not use it as a substitute for professional CDD/AML screening, sanctions clearance, PEP screening, or a compliance decision, and do not build an intrusive profile of a private individual.

When to use it

• Vet a vendor / supplier / counterparty before engaging or signing a contract.
• Background on a prospective hire, a partner, or a co-investor's principal.
• Check for negative / adverse media or litigation on a name.
• A quick public sanctions-list signal check (to escalate, not to clear).
• A quick PEP indication check from public research (to escalate, not to clear).
• "Who actually owns / runs X?" — ownership & key-management background.

The research lenses

1. Identity & background — confirm you have the right entity (registration no., jurisdiction, incorporation date, website, aliases / former names; for a person: role, employer, location, DOB if public). Disambiguate same-name entities early.
2. Ownership & key management — shareholders / UBO signals, directors, senior managers; group/parent structure. Use the people-enrichment skill / PDL for people & firmographics where appropriate.
3. Adverse / negative media — allegations, investigations, scandals, insolvency, fraud, environmental/labour issues — each with source, date, and allegation-vs-outcome.
4. Sanctions / PEP / watchlist signals — screen_lists(name) checks public sanctions lists only (OFAC SDN + Consolidated, UK OFSI, UN). PEP indications are manual/open-web research signals, such as public office, senior state-owned enterprise role, close-associate indications, or official biographies. Neither is a clearance.
5. Litigation & regulatory — material lawsuits, regulator actions, fines, debarments.
6. Summary & flags — a short read with escalation flags for a compliance reviewer.

Data sources (self-sufficient core + optional depth)

• Open web — web_search + web_extract for press, litigation, registry mentions, ownership clues, and public PEP indications.
• Deep-research engine — for a thorough pass, hand the entity + lenses to a deep-research engine/skill if available; otherwise run fan-out searches directly.
• PDL (people / firmographics) — run the people-enrichment skill for the owner / key-management / company layer where appropriate (needs PDL_API_KEY).
• Public sanctions lists — scripts/entity_research.py → screen_lists(name) fetches + token-name-matches the official government consolidated lists: US (OFAC SDN + Consolidated), UK (OFSI), UN. It returns potential-match signals only. It is not fuzzy/phonetic screening and does not cover all local autonomous lists. For a country's local autonomous measures, do a manual official-portal check. A match is a signal to verify with a compliance function; no match is NOT a clearance.

Workflow

1. Pin the subject — name + identifiers (jurisdiction, registration no., website, role/employer for a person). Resolve same-name ambiguity before researching.
2. Plan the research — break the entity into 3-6 research sub-questions across the lenses (e.g. "Who owns X?", "Any litigation against X?", "Is X on any sanctions list?"). Define success criteria: what would a complete dossier cover? This plan guides which lenses to prioritize and prevents skipping lenses.
3. Run the lenses — fan-out web search per lens; PDL for people/firmographics; screen_lists() for public sanctions-list signals; manual official/public checks for PEP indications and local sanctions lists. Capture the source URL + date for every claim.
   • Date grounding (mandatory). Before searching, ground in the real current date: "Today's date is {current date}. Use {current year} in queries — never a year inferred from training data."
   • Quality filter. Discard thin/irrelevant results before extraction: landing pages, aggregator stubs (<100 words of substantive content), pages with keyword overlap but no actual relevance (word-boundary match entity name, not substring), and duplicate URLs.
4. Gap analysis — after the first pass, review findings against the research plan from step 2. Which sub-questions are unanswered? Which lenses have thin coverage? Generate targeted follow-up queries for the gaps and run a second search pass. Repeat once more if significant gaps remain (max 3 passes).
5. Weigh — primary/official sources > reputable press > blogs/forums; allegation vs outcome; recency; corroboration (≥2 independent sources for a serious claim). Flag low-confidence items as such; don't launder rumour into fact.
6. Assemble — dossier(...) builds the cited markdown dossier (the six lenses + a "not a determination" header + escalation flags). Deliver to chat or save as .md.
7. Flag escalations — any sanctions-list signal, PEP indication, serious adverse finding, or integrity concern → call out "escalate to a compliance / AML reviewer" explicitly.

See references/research-lenses.md (what to look for + query patterns) and references/boundaries-and-sanctions.md (the signal-not-determination rule, false positives, and privacy guardrails).

Output pattern

For adverse / litigation / regulatory findings, prefer a compact table:

Item	Source	Date	Allegation / charge / outcome	Confidence	Escalate?

For sanctions / PEP / watchlist signals, never write "clear" or "blocked". Use language like:

• Public sanctions-list check found no potential matches in OFAC SDN/Consolidated, UK OFSI and UN via screen_lists() as of [date]. This is not a clearance; local lists and professional screening remain outside this helper.
• Potential sanctions-list name match on "[matched name]" ([list], score [x]) — escalate to compliance / AML reviewer for verification.
• Public PEP indication: [public office / SOE role / close associate indication] from [source, date] — escalate for compliance review. This is not a PEP-screening determination.

Boundaries & safety

• Research, not a determination / clearance. Sanctions-list or PEP signal = escalate, never "clear" or "block". No match ≠ clean.
• PEP clarity. screen_lists() is not a PEP screener. PEP indications come from public-source research and must be reviewed by a qualified compliance function.
• Allegations vs facts. Attribute and date every negative item; distinguish allegation, charge, and outcome. Avoid defamatory framing; report what sources say, with the source.
• Persons — legitimate purpose, public info only. Research a person only for a legitimate purpose (vetting), and only publicly-available information; don't compile sensitive personal data (health, beliefs, sexuality, etc.) or build an intrusive profile.
• A dossier is a draft for a human — never the basis for an automated action.

Principles

• Drafts, not advice — a dossier is a research aid for a person to read and act on.
• Never invent — cite every claim with a source + date; mark thin/uncorroborated items as such.
• Signal, not determination — a sanctions-list / PEP / watchlist signal escalates; it never clears or blocks.
• Honesty and calibration — distinguish allegation from outcome; present conflicts, note confidence.
• Workspace hygiene — keep the dossier local; it's internal and may name individuals.

Data handling — search the name, not the relationship

A bare name with no relationship attached is fine to research on the open web. But:

• Never leak the context. Search "[entity]", not "we're investing in [entity]" or "[entity] our client" — keep your deal/client relationship out of external queries.
• If the entity is tied to a live deal or a client, the relationship stays confidential (omit it from queries); the public research on the name still proceeds.
• Keep the dossier on the local machine; it may name individuals.

Files

• scripts/entity_research.py — screen_lists (public sanctions-list name-match signal, cached, graceful), dossier (assemble the cited markdown dossier + escalation flags), name-normalisation/citation helpers; --self-test (offline; matcher + dossier).
• references/research-lenses.md — per-lens checklist + good query patterns + source weighting.
• references/boundaries-and-sanctions.md — the lists, signal-not-determination, false positives, escalation, and person-privacy guardrails.

Verification checklist

• Subject pinned; same-name ambiguity resolved (or both presented).
• Research plan created (3-6 sub-questions across lenses, success criteria defined).
• Date grounding applied — queries use current year, not training-cutoff year.
• Quality filter applied — thin/irrelevant/duplicate results discarded before extraction.
• Gap analysis run — at least 2 search passes; remaining gaps documented.
• Every claim carries a source URL + date; serious claims corroborated (≥2 sources).
• Allegations attributed and distinguished from outcomes; thin items flagged as thin.
• screen_lists() presented as public sanctions-list signal only, never PEP screening or clearance.
• PEP indications, if any, came from public sources and are presented as escalation signals only.
• Person research limited to a legitimate purpose and public info only.
• Escalation flags listed explicitly; dossier kept local.

Requirements

• Python 3.8+ (stdlib only for screen_lists/dossier).
• Session web search + fetch tools for the research lenses (not bundled).
• Network for screen_lists (public lists) — --self-test runs offline.
• Optional: PDL_API_KEY + the people-enrichment skill for the people/firmographics layer; a deep-research engine/skill for a deeper pass.