Decision-grade entity research skill — produces a hypothesis-tested dossier on a specific company, person, nonprofit, or government org, not a generic profile. Forcing intake makes the user state their hypothesis upfront (what they already believe and want to verify or disprove) so the dossier tests it rather than confirms it. Output is an editable Word document (.docx) with verdict on the hypothesis, identity facts, 12-month activity timeline, network and reputation signals, red flags, conversation hooks tied to specific findings, and source-provenance audit log. Uses WebSearch + WebFetch + free APIs (SEC EDGAR, GitHub, ProPublica) as workhorses; optional BYOK MCPs enhance coverage. Use when the user asks for background research, diligence, or meeting prep on a specific entity (e.g., 'prep me for a meeting with [person/company]', 'due diligence on [company]'). Honors sensitivity exclusions for journalism + personal-vetting contexts.
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Une commande directe contourne le prompt de vérification. Examinez la source avant de l'exécuter.
Decision-grade entity research skill — produces a hypothesis-tested dossier on a specific company, person, nonprofit, or government org, not a generic profile. Forcing intake makes the user state their hypothesis upfront (what they already believe and want to verify or disprove) so the dossier tests it rather than confirms it. Output is an editable Word document (.docx) with verdict on the hypothesis, identity facts, 12-month activity timeline, network and reputation signals, red flags, conversation hooks tied to specific findings, and source-provenance audit log. Uses WebSearch + WebFetch + free APIs (SEC EDGAR, GitHub, ProPublica) as workhorses; optional BYOK MCPs enhance coverage. Use when the user asks for background research, diligence, or meeting prep on a specific entity (e.g., 'prep me for a meeting with [person/company]', 'due diligence on [company]'). Honors sensitivity exclusions for journalism + personal-vetting contexts.
license
MIT
metadata
{"source_spec":"megaprompts/12-dossier-megaprompt.md","build_pattern":"Path B (direct conversion)","research_pack_convention":"Agent Integrity Rules verbatim per PR #657 audit; hypothesis-testing variant","version":"1.0.0"}
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.
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:
Sales meeting / partnership pitch
Investment diligence
Acquisition diligence
Journalism / due diligence
Job interview prep
Competitive intelligence
Personal vetting (date, hire, business partner)
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.
"Mention their recent acquisition of [X] — it signals they're investing in vertical Y. Suggested framing: 'Saw the [X] announcement — how does that change your roadmap on Y?'"
"Ask about hiring"
"Their VP Engineering left 3 weeks ago (LinkedIn). Suggested framing: 'I noticed [name] moved on — what's the eng leadership plan?'"
"Talk about their values"
"They updated their pricing page last week (their official site). Suggested framing: 'Saw the pricing refresh — what drove that?'"
Each hook:
The hook (one sentence)
The finding it's tied to (with hyperlink + tier)
Suggested framing (verbatim phrasing user can adapt)
Phase 9: DOCX Generation (9 Sections)
Via Node.js + docx library.
Executive Summary — one paragraph: who they are + why they matter + verdict on the hypothesis (SUPPORTED / PARTIALLY SUPPORTED / DISPROVEN / INCONCLUSIVE) + 3 things-you-should-know bullets.
Identity Facts Table — founded/born, location, size/stage, current role, key affiliations. All cells sourced; hover-text tier.
Hypothesis Test — user's hypothesis stated verbatim. Supporting evidence (3-5 bullets with hyperlinked citations). Disconfirming evidence (3-5 bullets with hyperlinked citations). Verdict paragraph (2-3 sentences explaining the weight).
Chat summary: file path + verdict on hypothesis + audit counts + tier breakdown + BYOK MCPs used (if any)
Validate: check zip integrity with python3 -c "import zipfile,sys; zipfile.ZipFile(sys.argv[1]).testzip()" <docx> (no output = intact), then confirm the required sections are present
Tooling
Script
Role
scripts/citation_tracker.py
Three-count audit + supporting/disconfirming classification + source-tier tagging at ~/.dossier_sessions/<session>.json
scripts/disconfirming_evidence_balance.py
Verifies ≥30% of search budget allocated to disconfirming queries; warns if biased
