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name: "cs-dossier" description: "/cs:dossier — Decision-grade entity research with mandatory hypothesis-testing. 6-Q grill-me intake (Q4 hypothesis MANDATORY) → ≥30% disconfirming search budget → 9-section .docx with verdict (SUPPORTED/PARTIALLY/DISPROVEN/INCONCLUSIVE) + 3-5 finding-tied conversation hooks."

/cs:dossier — Decision-Grade Entity Research

Command: /cs:dossier <entity>

The cs-dossier persona produces a hypothesis-tested research dossier on a specific company, person, nonprofit, or government org — NOT a generic profile.

When to Run

• Sales meeting / partnership pitch (need conversation hooks tied to specifics)
• Investment / acquisition diligence
• Journalism / personal vetting (with sensitivity exclusions)
• Job interview prep
• Competitive intelligence

When NOT to Run

• Generic curiosity ("what does this company do?") → search the web yourself
• Quick lookup → faster to just google
• No hypothesis to test → the skill refuses, by design

Non-Generic by Design

The skill refuses to be a Wikipedia summary. Q4 (your hypothesis) is mandatory — without it, the dossier confirms what you already think and is worthless for decisions.

Forcing Intake (6 Questions, One at a Time)

Q	Asks	Notes
Q1	Subject identity (name + disambiguating identifier)	refuses ambiguous names
Q2	Subject type: person / company / nonprofit / gov org / other	forcing choice — drives source matrix
Q3	Purpose: sales / investment / acquisition / journalism / interview / competitive / vetting / other	forcing choice — drives angle + sensitivity
Q4	Hypothesis (MANDATORY) — what you already believe + want to verify/disprove	non-skippable; pushed back once if refused
Q5	Depth: 5-min brief or 15-min decision-grade dossier	forcing choice
Q6	Sensitivities to exclude	conditional — only if Q3 ∈ {journalism, personal vetting}

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

What You Get

After all phases:

dossier_<entity-slug>_<YYYY-MM-DD>.docx

9 sections:
1. Executive Summary (verdict: SUPPORTED/PARTIALLY/DISPROVEN/INCONCLUSIVE + 3 must-know)
2. Identity Facts Table (founded/born, location, size, role, affiliations; sourced + tiered)
3. Hypothesis Test (verbatim hypothesis + supporting evidence + disconfirming evidence + verdict)
4. 12-Month Activity Timeline (news, hires, departures, products, controversies)
5. Network Signals (collaborators / investors / customers / advisors)
6. Reputation Signals (sentiment, Glassdoor, peer mentions)
7. Red Flags + Hidden Patterns (litigation, departures, financials, tiered)
8. Conversation Hooks (3-5 finding-tied hooks with framing)
9. Source Provenance + Audit Log (per-source tier + search summary + counts)

Hypothesis-Testing Discipline

≥30% of search budget allocated to disconfirming queries. This is the non-negotiable differentiator from a generic profile.

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

Query type	Example
Supporting (would confirm)	"Microsoft Foundry adoption 2026"
Supporting	"Microsoft AI infrastructure consolidation"
Disconfirming (would refute)	"Microsoft OpenAI deal renegotiation"
Disconfirming	"Microsoft AI vendor diversification"
Disconfirming	"Microsoft third-party model partnerships 2026"

skills/dossier/scripts/disconfirming_evidence_balance.py enforces the ratio. Halts at <30% and prompts more disconfirming queries.

Source Reliability Tiering

Every fact in the DOCX tagged with tier (primary / secondary / tertiary):

Tier	Examples
Primary	SEC EDGAR filings, court records, official .gov sites, company official website
Secondary	Mainstream news (NYT, WSJ, Reuters), trade press (TechCrunch, The Information)
Tertiary	Blogs, forums (Reddit, HN), Glassdoor, social media

skills/dossier/scripts/source_tier_classifier.py does this from URL.

Discipline (Research-Pack Convention)

• One intake Q per turn. Never bundle.
• Q4 mandatory. Push back once; fall back to "most surprising finding" implicit hypothesis with flag.
• ≥30% disconfirming. Enforced by tool.
• Sequential search. WebSearch + WebFetch sequential, 1 q/sec etiquette.
• Source discipline. Cite only session results. Training knowledge labeled [Background — verify before quoting], excluded from counts.
• Three-count + tier. Sent / received / cited + per-tier breakdown.
• Subject disambiguation before Phase 3. Refuse ambiguous names.
• Sensitivity exclusions honored. If Q6 excluded "medical history", don't surface even if found.
• Conversation hooks finding-tied. Generic hooks ("ask about their roadmap") rejected.
• BYOK MCP flagged in audit. Crunchbase / Pitchbook usage surfaced.

Trigger Phrases

• "research [company]"
• "dossier on [person/company]"
• "background check on [entity]"
• "prep me for a meeting with [person/company]"
• "due diligence on [company]"
• "what should I know about [entity]"
• "research [person] before I [meet/hire/invest]"
• "competitor research on [company]"
• "investor diligence [company]"
• "interview prep for [company]"

Anti-Patterns Rejected

• Producing a dossier without forcing Q4 hypothesis
• <30% disconfirming search budget (confirmation bias)
• Batching intake questions
• Accepting ambiguous subject names
• Generic conversation hooks ("ask about their roadmap")
• Sensationalizing red flags (tier them, don't editorialize)
• Skipping source-reliability tier on flags
• Fabricating coverage when LinkedIn blocked
• Using BYOK MCP without flagging in audit
• Including sensitive topics user excluded (Q6)
• Confirmation-biased verdict ("SUPPORTED" without engaging with disconfirming evidence)

Related

• Agent: cs-dossier
• Skill: dossier
• Source spec: megaprompts/12-dossier-megaprompt.md (maintainer-local draft spec — gitignored, not in the public repo)
• Siblings: /cs:litreview, /cs:grants, /cs:pulse
• Future: /cs:patent, /cs:syllabus

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Version: 1.0.0 Source: Path-B direct conversion of megaprompts/12-dossier-megaprompt.md