Dossier — Decision-Grade Entity Research

Portability: Requires WebSearch + WebFetch, Node.js with docx package, and optionally bash_tool + curl for free APIs (SEC EDGAR, GitHub, ProPublica). BYOK MCPs (LinkedIn, Crunchbase, Apollo, Pitchbook, SimilarWeb) are optional enhancements. Works in Claude Code CLI natively.

Non-Generic Framing — The Differentiator

This skill is decision-grade entity research with hypothesis-testing. It refuses to be "tell me about Microsoft". Every invocation forces the user to expose their hypothesis upfront (Q4) so the dossier tests it rather than confirms it.

The use case shape:

"I'm pitching Microsoft Tuesday. My hypothesis is they're consolidating AI spend on their first-party Foundry platform. Validate or disprove, and give me three conversation hooks tied to what you find."

NOT:

"Tell me about Microsoft."

The forcing Q4 — the hypothesis question — is the non-generic anchor. Skip it and the skill produces a Wikipedia summary.

See references/hypothesis_testing_discipline.md for the canon.

Agent Integrity Rules (Research-Pack Convention)

Locked verbatim per PR #657 audit.

• Execution discipline. Sequential search calls. WebSearch + WebFetch have looser rate limits than Consensus but still apply 1 q/sec etiquette. Confirm response received before next call.
• Source discipline. Cite only sources returned by this session's tool calls. Wikipedia / training knowledge labeled [Background — verify before quoting] and excluded from primary findings count.
• Three-count tracking. Queries sent / sources received / sources cited. Plus per-tier breakdown (primary / secondary / tertiary) unique to dossier. Surfaced in audit log.
• Retry policy. On failure → wait 3s → retry once → log. After 3 consecutive failures: stop, alert user.
• Source reliability tier. Each citation tagged primary (official, SEC, court records) / secondary (mainstream news, trade press) / tertiary (blogs, forums). DOCX surfaces tier on every flag.

Phase 1: Grill-Me Intake (6 forcing questions, one at a time)

Q1 (root) — Subject identity

Who is the subject? Give me the exact name and, if a company, the website or LinkedIn URL. If a person, their LinkedIn URL or a unique identifier (company affiliation + role).

Why I'm asking: Disambiguation. There are 47 John Smiths. There are three companies called "Atlas". I need a specific entity to research.

If user gives only a name, push for a second identifier. Refuse to proceed on ambiguous names.

Q2 (depends on Q1) — Subject type

What kind of subject is this? Pick one: person / company / nonprofit / government org / other.

Why I'm asking: Different source matrices apply. For people I check LinkedIn, GitHub, Scholar, news; for companies I check SEC EDGAR (if public), Crunchbase, news, GitHub for tech orgs; for nonprofits I check Form 990s on ProPublica.

Forcing choice. "Other" requires a one-line description.

Q3 (depends on Q2) — Purpose

What are you preparing for? Pick one:

1. Sales meeting / partnership pitch
2. Investment diligence
3. Acquisition diligence
4. Journalism / due diligence
5. Job interview prep
6. Competitive intelligence
7. Personal vetting (date, hire, business partner)
8. Other (specify)

Why I'm asking: The purpose dictates the angle, the depth, and the red-flag sensitivity. Sales prep needs conversation hooks. Investment diligence needs traction signals. Personal vetting needs careful sensitivity boundaries.

Q4 (depends on Q3) — Hypothesis — MANDATORY

What's your hypothesis going in? What do you already believe about this subject, and what do you want to verify or disprove?

Why I'm asking: This is the critical question. A dossier that just confirms what you already think is worthless. By stating your hypothesis upfront, I can search for evidence that would disprove it as well as evidence that supports it — and give you a verdict you can actually use.

Examples:

• "I believe Microsoft is consolidating AI spend on first-party Foundry. Verify or disprove."
• "I think the CEO is over their head — too much TAM talk, no traction. Test that."
• "I believe this nonprofit's overhead ratio is sketchy. Check the 990s."
• "I think this person is technical enough to handle a CTO role. Verify."

MANDATORY. If user says "I don't have one", push back once: "Then guess. Commit to a position you can update later. The dossier needs a hypothesis to test, otherwise it's a generic profile and won't help you make a decision."

If still refused: fall back to implicit hypothesis "what's the most surprising thing I could find?" and flag the fallback in audit log.

This question is the non-generic anchor. Skip it and the skill becomes a Wikipedia summary.

Q5 (depends on Q3) — Depth

Time horizon: 5-minute brief or 15-minute decision-grade dossier?

Why I'm asking: Brief mode caps at ~10 searches and skips the network + reputation passes. Decision-grade goes deeper on every section. Pick based on how much skin you have in this decision.

Forcing choice.

Q6 (asked only if Q3 ∈ {journalism, personal vetting}) — Sensitivities

Anything sensitive to exclude? E.g., personal medical, family details, political history, or specific topics off-limits?

Why I'm asking: Some research contexts have ethical constraints. I'd rather know upfront than surface something you'd never share.

Skip for sales/investment/acquisition/competitive intel (low sensitivity); ask for journalism/personal vetting (high sensitivity).

Stop condition: After Q6 (or earlier with dependency skips), commit and start Phase 2. Never re-open intake after Phase 2 begins.

Phase 2: Subject Disambiguation

Before Phase 3, resolve the subject to a specific entity:

• For people: confirm LinkedIn URL OR (employer + role + city)
• For companies: confirm domain OR (legal name + incorporation jurisdiction)
• For nonprofits: confirm EIN OR (legal name + state)
• For government orgs: confirm official .gov URL

If still ambiguous after Q1 push-back: halt and re-ask Q1 with disambiguating identifiers. Refuse to proceed.

Phase 3: Source Matrix Selection

Routed by Q2 subject type. See references/subject_type_source_matrix.md for the full canon.

Person

• LinkedIn (manual fetch or LinkedIn MCP if BYOK)
• Personal website
• Twitter/X (rate-limited; degrade gracefully)
• GitHub (if technical subject)
• Google Scholar (if academic)
• News (WebSearch + WebFetch)
• Conference talk transcripts, podcasts (WebSearch)

Company

• Official website (about, leadership, news, careers)
• SEC EDGAR (free API; 10-Ks, 10-Qs, 8-Ks for public co's)
• Crunchbase free tier (or Crunchbase MCP if BYOK)
• News (WebSearch + WebFetch)
• GitHub (for tech orgs)
• Glassdoor + Comparably (sentiment; degrade gracefully if scraping blocked)
• LinkedIn company page

Nonprofit

• ProPublica Nonprofit Explorer (free; Form 990s)
• Official website
• News
• GuideStar (if accessible)

Government org

• Official .gov sites
• News
• ProPublica (for federal agencies)

If a paid MCP is connected (Apollo, Pitchbook, SimilarWeb), use it but mark findings as BYOK-sourced in the audit log.

Phase 4: Hypothesis-Driven Search

Every Phase 4 search MUST be classified as either:

• Supporting evidence (confirms hypothesis), OR
• Disconfirming evidence (would refute hypothesis)

≥30% of search budget allocated to disconfirming queries. Enforced via scripts/disconfirming_evidence_balance.py.

Example for hypothesis "Microsoft is consolidating AI spend on Foundry":

• Supporting: "Microsoft Foundry adoption 2026", "Microsoft AI infrastructure consolidation"
• Disconfirming: "Microsoft OpenAI deal renegotiation", "Microsoft AI vendor divers