Client Dossier — Intelligence Brief for High-Stakes Meetings

TL;DR: Transform public signals about a prospect into a battle-ready dossier — company DNA, key contacts, pain hypotheses, and a personalized approach strategy. Output: branded HTML dossier (JM Labs dark style) + optional markdown summary.

Principio Rector: Prepare like a detective, enter like a consultant. Every fact verified, every hypothesis labeled. A dossier is not an encyclopedia — it is a decision support tool.

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When to Activate

Activate when:

• User says "build dossier on [company/person]", "research [company X]", "client profile for [name]"
• User says "quién es [empresa]", "investiga a [empresa]", "perfil de prospecto para [nombre]"
• User says "dossier for sales call", "prep for meeting with [company]", "who is [person] at [company]"
• A sales or BD context is active and a company or executive name is the primary input [INFERRED]
• User provides a company name and asks for entry strategy, pain points, or stakeholder map [INFERRED]
• User has a discovery call in <48h and needs preparation material [INFERRED]

Do NOT activate when:

• User is asking for general market research without a named target (use market-intelligence instead)
• The request is about internal team members or employees (use stakeholder-mapping instead)
• The request is about a private individual in a non-business context (privacy concern — decline)
• User wants a competitive landscape across multiple players (use competitive-intelligence instead)
• Input is too vague: "research my industry" — no named target present, clarify first
• User needs legal/financial due diligence for M&A (requires specialized legal tools, not this skill)

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S1: Dossier Types

Three dossier modes, each with a distinct research agenda. Infer the mode from phrasing. If ambiguous, ask: "Is this a company dossier, an executive profile, or a partnership evaluation?"

Company Dossier [EXPLICIT] Full organizational profile. Used before a first meeting, RFP response, or partnership pitch. Covers founding story, ownership, revenue signals, headcount trajectory, tech stack, recent strategic moves, and cultural tone. Most common mode. Output: all 6 sections (S2–S5 + HTML).

Executive / Public Figure Dossier [EXPLICIT] Profile of a named decision-maker. Used to personalize outreach, prepare for a call, or understand a gatekeeper. Covers career arc, public positions, speaking topics, known priorities, management style signals, and mutual connections. Strict scope: public professional information only. No personal life, no private data inference. Do not include home addresses, personal social media, family data, or non-professional history.

Partnership Target Dossier [INFERRED] Hybrid of company + lead contact. Used when evaluating a channel partner, reseller, or co-builder. Emphasizes business model alignment, revenue complementarity, integration potential, and historical partnership behavior: who they partner with, on what terms, and how long those partnerships last.

Minimum viable input per mode:

Mode	Required	Useful extras
Company	Company name	Website, LinkedIn URL, industry
Executive	Full name + company	LinkedIn URL, role title
Partnership	Company name + relationship type	Customer segment, known tech stack

Dossier depth options:

Mode	Time	Scope	Use case
Flash (30 min)	Fast	Company DNA + 1 contact	Inbound call in <1h
Standard (2h)	Medium	Full S2–S5, 2–3 contacts	Tomorrow's discovery call
Deep (5h+)	Slow	Full + org chart + competitive context	Strategic account, large deal

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S2: Company DNA Framework

Systematic extraction of organizational identity signals. Work through each dimension. Tag every claim with [EXPLICIT], [INFERRED], or [OPEN]. [EXPLICIT]

Founding & Ownership

• Year founded, founding team background (operators vs. investors vs. technologists) [EXPLICIT]
• Current ownership: bootstrapped, VC-backed (series + lead investor), PE-owned, public [EXPLICIT]
• Acquisition history: have they been acquired or acquired others? Signals strategic ambition [EXPLICIT]
• Ownership concentration: founder-led vs. institutional vs. family-controlled [INFERRED]

Revenue & Growth Signals

• Public revenue if available; otherwise triangulate from funding rounds + headcount trend [INFERRED]
• Growth stage: pre-PMF, scaling, mature/optimizing, declining/pivoting [INFERRED]
• Geographic footprint: single market, regional, global — matters for solution scope [EXPLICIT]
• Hiring velocity: net new roles in last 90 days signals investment in capability [INFERRED]

Headcount & Team Composition

• Total headcount from LinkedIn (snapshot, not exact); engineering % vs. sales % vs. ops % [INFERRED]
• Departures / executive churn: LinkedIn tenure data, Glassdoor reviews as qualitative signal [INFERRED]
• Key department gaps: 5 open data engineer roles → they have an infrastructure problem [INFERRED]
• Note: headcount from LinkedIn can be ±15-20% of actual; label as [est.] [EXPLICIT]

Tech Stack Signals

• Job posting language: "must know Kubernetes", "experience with Salesforce", "dbt + Snowflake"
• BuiltWith / Wappalyzer (if web-facing product) for frontend/marketing stack [INFERRED]
• GitHub org activity: open-source repos, language distribution, commit frequency [EXPLICIT]
• Integration partners mentioned on their website or in press releases [EXPLICIT]

Recent News & Strategic Moves (last 90 days)

• Funding announcements, acquisitions, product launches [EXPLICIT]
• Leadership changes (new CTO/CPO = new agenda, new vendor evaluation cycle) [INFERRED]
• Customer wins / case studies published = proof of strategic direction [EXPLICIT]
• Conference appearances and speaking slots = what narrative they are pushing publicly [INFERRED]
• Regulatory or legal events (SEC filings if public, lawsuit mentions) [EXPLICIT]

Cultural & Operational Tone

• Mission language on careers page: "move fast", "customer obsessed", "enterprise-grade" [INFERRED]
• Glassdoor themes: execution culture vs. churn risk; note review recency [INFERRED]
• Social media tone: thought leadership vs. pure broadcast vs. community-engaged [INFERRED]

Financial Signals Table:

Signal	What It Indicates
Recent funding round	Available budget, growth phase, investor expectations
High job posting volume	Investment areas, team priorities, active pain points
Contract announcements	Revenue tiers, deal size norms
Downsizing / layoffs	Budget pressure, cost-saving motive, churn risk
Awards / rankings	Brand health, employer strength, strategic priorities
Re-posted same role 3×	Retention problem or unrealistic hiring bar

[OPEN] Revenue figures for private companies are estimates. Flag all inferred revenue with [est. — not verified] in every output. Never present as confirmed facts.

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S3: Key Contacts Map

Build a stakeholder map of the target organization focused on the deal or engagement type. Produce a Contact Card (see template below) for each named person. [EXPLICIT]

Decision Maker Identification by Deal Type

Deal Type	Decision Maker	Influencer	Gatekeeper
Technology / SaaS	CTO, VP Engineering	Engineering Manager	EA, Procurement
Consulting / services	CEO, COO	CFO	EA, Legal
Training / enablement	CHRO, VP L&D	Department Head	Training Coordinator
Data / analytics	CDO, VP Data	Data Lead	Procurement
Marketing tech	CMO, VP Marketing	Demand Gen Lead	Marketing Ops
Digital transformation	CIO / CDO + C-suite sponsor	COO	PMO, Legal

Contact Research Protocol (execute in this order):

1. LinkedIn search: "[Company] [Title]" — note tenure, previous companies, education [EXPLICIT]
2. Company website team/about page — often lists leadership with bios [EXPLICIT]
3. Search "[Name] [Company] interview" or "[Name] [Company] podcast" for public positions [EXPLICIT]
4. Twitter/X profile for thought leadership signals and engagement patterns [EXPLICIT]
5. Note mutual connections — warm intro is always the highest-value outreach path [INFERRED]

Email Pattern Inference (validate before use — never assume confirmed):

• firstname@company.com — common at startups and SMB
• firstname.lastname@company.com — most common at mid-market and enterprise
• flastname@company.com — common in finance and professional services
• f.lastname@company.com — frequent in European companies Use Hunter.io pattern matching (if user has access) or validate via email verification tool. Label all email patterns as [INFERRED — not verified] in output. [EXPLICIT]

Contact Card Template (produce one per person):

Name:              [Full Name]                          [EXPLICIT]
Title:             [Current Title]                      [EXPLICIT]
Tenure:            [X years at company]                 [EXPLICIT]
Prior companies:   [2–3 most relevant past employers]   [EXPLICIT]
Background:        [2-sentence career arc]              [EXPLICIT]
Public positions:  [Key theme they discuss publicly]    [EXPLICIT]
Best channel:      [LinkedIn / email / warm intro via X][INFERRED]
Likely priority:   [What they care about professionally][INFERRED]
Potential concern: [What might make them say no]        [INFERRED]
Email pattern:     [pattern@company.com — confidence: H/M/L] [INFERRED]
Mutual connection: [Name + relationship if applicable]  [EXPLICIT / OPEN]

Org Chart Reconstruction [INFERRED]

• Map reporting lines from LinkedIn titles and tenure signals
• Identify recently promoted people (likely championing new initiatives)
• Flag who joined from a competitor (may bring vendor preferences with them)
• Note who is actively hiring under them (signals budget ownership)

[EXPLICIT] Only use publicly available professional information. Do not infer personal details, home addresses, or non-professional social media content.

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S4: Pain Point Hypothesis

Construct a hypothesis of what is hurting the target organization right now. This drives message-market fit for the outreach or pitch. Produce 3–5 hypotheses. [EXPLICIT]

Signal-to-Pain Mapping:

Signal Observed	Derived Pain Hypothesis
15+ open engineering roles	Scaling pains, team capacity issues, delivery bottleneck
3 CTOs in 24 months	Tech strategy instability, culture/direction tension
No data/analytics roles	Low data maturity, spreadsheet-heavy operations
Series B raised 12 months ago	Board pressure for growth metrics and unit economics
Recent compliance-related press	Regulatory risk, need for audit/governance tooling
Legacy tech stack in job postings	Migration need, tech debt, developer experience friction
Rapid expansion to new markets	Localization needs, operational complexity, compliance gaps
Re-posting the same role 3×	Retention problem or misaligned expectations
Competitor launched a feature they lack	Product gap, pressure to close the gap fast
Customer forum / G2 complaints visible	Support overload, churn risk, UX debt

Pain Point Hypothesis Format (produce per hypothesis):

Hypothesis:    [Company X] likely struggles with [SPECIFIC PAIN]         [INFERRED]
Evidence:      [Signal 1: source] + [Signal 2: source]
Urgency:       [Why this pain is acute NOW vs. chronic background noise]  [INFERRED]
Our hook:      [How our offering maps to this specific pain]              [INFERRED]
Confidence:    High / Medium / Low
Validation Q:  [The one question to ask on the call to confirm or refute]

Pain Categories Checklist:

• Revenue growth blocked (can't acquire, expand, or retain customers)
• Operational efficiency (manual work, slow processes, high headcount for low output)
• Technical debt (legacy stack slowing product velocity)
• Talent (can't hire, retain, or upskill fast enough)
• Compliance / risk (regulatory pressure, security gaps, audit failures)
• Data visibility (can't measure what matters, decisions made on gut)
• Customer experience (churn, NPS decline, support volume growing)

[EXPLICIT] Pain hypotheses are hypotheses. They must be validated in the discovery call. Do not present them as confirmed facts in the dossier or in outreach messages.

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S5: Approach Strategy

Synthesize dossier findings into a concrete outreach and engagement plan. [INFERRED]

Best Entry Angle Selection:

Context	Recommended Angle
Recent funding round	Congrats + growth/scale challenge framing, budget window open
New executive hire	Fresh start, new vendor evaluation cycle, open to change
Recent product launch	Integration opportunity, complementary capability
Cost pressure signal	ROI-first framing, fast time-to-value, efficiency story
Competitor win in their space	Urgency angle, competitive pressure, fear of falling behind
Award / milestone	Validation: "how do you maintain this standard at scale?"
Pain signal from job postings	Precision hook: "we saw your [X] postings, we solve that"

Personalization Layers (stack all four for highest conversion):

1. Company layer — Reference specific company news, milestone, or challenge [EXPLICIT]
2. Role layer — Speak to their specific responsibilities and KPIs [INFERRED]
3. Person layer — Reference their public content, career moves, or interests [EXPLICIT]
4. Timing layer — Connect to a trigger event (funding, launch, Q1 planning cycle) [INFERRED]

Channel Sequence Recommendation:

1. LinkedIn connection request (no note, or short note referencing shared context)
2. LinkedIn message after connection (1 paragraph, specific hook, one clear ask)
3. Email follow-up 5 days later (reference the LinkedIn outreach, add new value element)
4. Warm intro via mutual connection if one exists (highest conversion, always preferred)

Objection Pre-empts:

Common Objection	Pre-empt in the Message
"We already have a solution"	Acknowledge + differentiate on one specific dimension
"Not the right time / budget frozen"	Ask for a 15-min discovery, not a commitment
"Send me more info"	Offer a 1-pager tailored to their use case, not a generic deck
"We handle this internally"	Validate their approach, offer a benchmark/audit angle
"We're in evaluation mode already"	Ask to be included, offer a specific POV or framework

Opening Message Draft Formula:

[Trigger event / personalization hook] + [specific pain we solve] +
[1 reference to a peer / analogous company] + [metric or outcome] + [single soft ask]

Example:

"Saw your team posted 6 data engineer roles this quarter — teams scaling data infra at your stage usually hit the orchestration wall 60 days after the dbt rollout. We helped [Peer Company] cut pipeline failures by 70% in that window. Worth a 20-minute call to see if the pattern fits?"

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S6: HTML Dossier Output Template

Generate a branded, self-contained HTML dossier. Load full template from references/html-template.md. [EXPLICIT]

Brand tokens: --navy: #122562 · --gold: #FFD700 · --blue: #137DC5 · dark bg #0d0d0d · Poppins headings + Inter body.

Sections in the HTML output: Header (company name + date + confidential badge) → TL;DR executive brief (3 bullets) → Company DNA (10-field data grid) → Key Contacts (2 contact cards) → Pain Hypotheses (3 items with confidence badges) → Approach Strategy (4-step numbered list) → Footer (JM Labs attribution).

Delivery rules [EXPLICIT]:

• Replace every [placeholder] with researched data
• Label inferred fields with [est.] inline
• Do not deliver with unfilled placeholders
• Mark [INFERRED] on estimated revenue / headcount / tech stack when source is indirect

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Trade-off Matrix

Decision	Option A	Option B	Recommendation
Depth vs. speed	Full 6-section dossier (2-3h)	Quick 2-section brief (30min)	Full for strategic accounts; brief for same-day inbound
Public vs. inferred data	Only confirmed facts	Include reasoned inferences	Include inferences, tag clearly with [INFERRED]
HTML vs. markdown output	Branded HTML dossier	Plain markdown summary	HTML for client-facing; markdown for internal notes
Company vs. person focus	Company-first, then people	Person-first, then company	Company-first unless user specifies a named target
One contact vs. full map	Single decision-maker	Full stakeholder map	Full map for deals >$50K; single for fast outreach
Hypothesis depth	1 primary pain	3–5 ranked hypotheses	3–5 always; more hypotheses = better discovery call prep

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Assumptions & Limits

• [EXPLICIT] All research is based on publicly available information only. No paid databases, no data brokers, no private data inference beyond professional context.
• [INFERRED] Revenue and headcount figures for private companies are estimates. Every such figure is labeled [est. — not verified] in the output.
• [EXPLICIT] Email patterns are hypotheses, not confirmed addresses. User must validate before use.
• [OPEN] Tech stack inferences from job postings may lag reality by 6–12 months. Directional only.
• [EXPLICIT] This skill does not profile private individuals outside a professional or public-figure context. If a request concerns a private person, decline and explain the privacy concern.
• [INFERRED] Pain hypotheses are probabilistic. The validation question in each hypothesis is mandatory — do not treat as confirmed until the discovery call verifies it.
• [EXPLICIT] Dossier freshness degrades fast — refresh if >30 days old before any meeting.

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Edge Cases

EC-1: Company with no digital footprint Very small or recently founded company has minimal LinkedIn presence, no press, minimal web. Action: Build a lightweight dossier from whatever is available. Flag the confidence score as low. Use industry + company size + founding year to construct pain hypotheses from first principles (e.g., "at 20 employees in SaaS, the most common pain at 18 months is [X]").

EC-2: Executive with a common name / ambiguous identity "John Smith at Acme" — multiple LinkedIn matches, ambiguous results. Action: Ask user for LinkedIn URL or full title before proceeding. Do not produce a dossier on the wrong person. Verify company + current title before writing any Contact Card.

EC-3: Company in a language other than English Target is a Latin American or European company with minimal English-language presence. Action: Research in the native language. Produce the dossier in the language the user requests. Use [data-l="es"] / [data-l="en"] HTML markup throughout the output.

EC-4: User wants a dossier on a competitor (not a prospect) Intent is competitive intelligence, not a sales dossier. Action: Redirect to the competitive-intelligence skill. The dossier framework is optimized for outreach and entry strategy, not competitive positioning — wrong tool for this use case.

EC-5: Publicly traded company with overwhelming data volume 10-K, earnings calls, IR page, analyst coverage — information overload. Action: Prioritize recency (last 12 months) and strategic relevance (what affects this deal). Summarize, do not dump. The dossier is a decision tool, not an archive. Cite specific sections for the user to read if they want more depth.

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Good vs. Bad Example

BAD — Generic, no action value:

"Acme Corp is a technology company with around 500 employees. They seem to be growing and might be interested in our services. Their CEO is John Doe."

GOOD — Specific, hypothesis-driven, actionable:

"Acme Corp (Series B, $42M raised Jan 2024 [EXPLICIT], 340 → 480 employees in 12 months [INFERRED via LinkedIn]) is scaling engineering faster than their data infrastructure can support. Evidence: 6 open data engineer roles in Q1 [EXPLICIT], all requiring dbt + Snowflake, none requiring Airflow — the orchestration gap [INFERRED]. Decision-maker: Maria Gómez, VP Engineering (joined 8 months ago from Stripe [EXPLICIT], previously built a data platform team from scratch [EXPLICIT via LinkedIn bio]). Hook: 'We saw your data engineer postings — teams at your stage typically hit the orchestration wall 60 days after the dbt rollout. We solved this for [Peer Company] and cut pipeline failures by 70%. Worth a 20-min call?' [INFERRED pain + EXPLICIT peer reference]."

The good version has: named signals with evidence tags, a specific gap, a named contact with career context, a peer reference, a metric, and a specific one-ask close. Every element traces to a real signal source.

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Validation Gate

Before delivering any dossier output, verify all of the following:

• Company name correctly spelled and verified — not a homonym or alias
• All revenue / headcount figures labeled [est.] if not from a public source
• At least 2 specific signals support the primary pain hypothesis
• Every named contact drawn from public professional sources only
• Email patterns labeled as [INFERRED — not verified], not as confirmed addresses
• Tech stack claims trace to at least one concrete signal (job posting, website, press)
• Approach strategy includes a specific personalization hook, not a generic opener
• HTML output uses JM Labs brand tokens (navy #122562, gold #FFD700, blue #137DC5)
• Bilingual [data-l] attributes present on all user-facing strings in HTML output
• All [placeholder] tokens in HTML have been replaced with real researched data

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Reference Files

File	Purpose
knowledge/body-of-knowledge.md	Sales intelligence and OSINT methodology
knowledge/knowledge-graph.md	Entity relationships for prospect research
evals/	Scored dossier examples for quality calibration

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Related Skills

• market-intelligence — Broader market/sector context before the dossier
• b2b-outreach — Convert dossier findings into outreach sequences
• client-prospecting — Identify who to build dossiers for
• competitive-intelligence — Competitor profiles (different framework from prospect dossiers)
• executive-pitch — Use dossier findings to prepare a pitch narrative
• stakeholder-mapping — Internal org mapping when target is an existing client