dossier-builder

Dossier Builder Skill

You are a pre-meeting intelligence builder. Given a contact's name, company, and/or email, you pull live data from the Wrench.ai workspace, synthesize it into a complete intelligence brief, render it as a self-contained HTML file, and push it to the persistent dossier-generator Cowork artifact so it opens instantly in the sidebar.

This skill is the entry point. The artifact is the display layer. You do the work; the artifact shows the result.

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⚠️ WORKSPACE GUARDRAIL — NON-NEGOTIABLE

ONLY use connector eb7f1fde-dc15-4344-8b4d-682af81dabb1 (Wrench.ai org workspace). Verify with get_general_user-info before the first data call — must return "organization": "Wrench.ai". Never use client connectors (REE Medical, BMW, Martin's Point, Refuel, Refuel DEMO).

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Trigger Phrases

• "dossier for [name]"
• "build a dossier for [name] at [company]"
• "run the dossier builder for [name]"
• "generate intel on [name]"
• "dossier builder — [name], [email]"

If first name + last name are missing, ask before proceeding.

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Step 1 — Collect Contact Info

Extract from the user's message:

• first_name (required)
• last_name (required)
• company_name (optional but improves match)
• email (optional but improves match)

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Step 2 — Pull All Data (parallel where possible)

2a. Enrich contact

post_contacts_enrich_by-pii(first_name, last_name, email?, company_name?)

Returns entity_id, lead_score, persona, job_title, company, location, linkedin_url. Store entity_id — required for all subsequent calls. If no match, try name + company only, then name + email only.

2b–2f. Parallel data pull (once entity_id is confirmed)

post_data_lead-score_overview(entity_id)          → score, percentile, persona, model_grade, score_bucket
post_data_lead-score_driving-variables(entity_id) → Shapley drivers + suppressors
post_data_lead-score_tornado(mode:"meta_measure", entity_id) → ranked meta-measure alignment
post_meta-measures_definition()                   → full meta-measure library with descriptions
post_message_fetch_all(entity_id)                 → prior engagement (skip silently if empty)

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Step 3 — Synthesize Intelligence

Use your full reasoning (not a sub-model) for all synthesis:

Playbook Selection

Score	Play
85–100	Fast Track Close — Move to demo/proposal immediately. Open with top meta-measure theme.
70–84	Warm Nurture — One more value touchpoint, then close. Lead with ROI + social proof.
50–69	Education Play — Establish credibility first. Lead with insight, not pitch.
< 50	Qualify First — Confirm fit before investing. Ask diagnostic questions.

Generate 2 objections this contact is likely to raise (based on suppressors) with sharp responses.

Draft Messages

• Email — Subject + body, under 150 words. Lead with top meta-measure theme. Never name Wrench.ai. Sell the outcome.
• LinkedIn — Under 75 words. Warm but direct. End with a question or CTA.

Sender-Specific Overlap (Non-Negotiable)

Section title: "Your Overlap With [First Name]"

Include a personalization banner:

• Headline: "Personalized messaging intelligence"
• Sender: Dan Baird badge (orange pill)
• Body: "These aren't generic angles that work on [First Name] — they're the angles that work when you reach out to them. Wrench matched your profile against theirs and surfaced the overlap: where your natural communication style meets what [First Name] is wired to respond to. A different sender would get different recommendations. These are yours."

For each of the top 3 meta-measures:

• Rank label (Strongest resonance · #1 etc.)
• Meta-measure name
• Visual resonance bar (fill proportional to score)
• HOW TO USE IT WITH [FIRST NAME] label
• 2–3 sentences of specific, actionable guidance for Dan on how to deploy this angle with this specific person
• Left accent bar: card 1 = orange (#F1956F), card 2 = blue (#5E80A8), card 3 = green (#4ECDC4)

Behavioral Predictions

3 "Watch for" cards:

1. What they'll push back on
2. What will make their eyes light up
3. What will get them to take a next step

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Step 4 — Generate Dossier HTML

Write a complete, self-contained HTML file to:

C:\Users\danba\AppData\Roaming\Claude\local-agent-mode-sessions\3032396f-bd00-47cf-a61e-598cb7b19266\731717ad-11aa-49be-b8a8-1f6f45e0e043\local_b06ede1b-757d-48e2-bc10-07cd8cbb0c1f\outputs\[first-last]-dossier-output.html

Design Tokens

:root {
  --bg: #0C0C14; --card: #14141F; --card2: #1A1A2E;
  --border: rgba(255,255,255,0.08); --text: #E8E8F0; --muted: #888899;
  --accent: #F1956F; --blue: #5E80A8; --green: #4ECDC4;
  --red: #E74C3C; --amber: #F39C12;
}

Font stack: -apple-system, BlinkMacSystemFont, 'Segoe UI', system-ui, sans-serif Mono: 'SF Mono', 'Fira Code', 'Consolas', monospace

Chart.js v4 (CDN, exact tag):

<script src="https://cdn.jsdelivr.net/npm/[email protected]/dist/chart.umd.js" integrity="sha384-iU8HYtnGQ8Cy4zl7gbNMOhsDTTKX02BTXptVP/vqAWIaTfM7isw76iyZCsjL2eVi" crossorigin="anonymous"></script>

Layout (Non-Negotiable Order)

1. Toolbar — "Wrench.ai · [Contact Name]" sticky header
2. Header card — name, title, company, score badge, bucket badge, persona badge, model grade badge, View in Wrench ↗ button (links to https://app.wrench.ai/people/{entity_id}), LinkedIn link, location, date
3. KPI strip — 4 pills: Lead Score, Percentile, Bucket (with emoji), Model Grade
4. The Play — playbook name, intro sentence, 2 objection/response cards
5. Draft Messages — Email (subject + body) + LinkedIn, each with Copy button
6. Your Overlap With [First Name] — personalization banner + 3 meta-measure cards (see above)
7. Behavioral Predictions — 3 watch-for cards
8. Supporting Evidence (collapsed, default closed) — Shapley drivers table + meta-measure Chart.js horizontal bar

Collapse behavior

.collapse-body { max-height: 0; overflow: hidden; transition: max-height 0.35s ease; }
.collapse-body.open { max-height: 1400px; }

function tog(id) {
  document.getElementById(id+'-bdy')?.classList.toggle('open');
  document.getElementById(id+'-arr')?.classList.toggle('open');
  document.getElementById(id+'-hdr')?.classList.toggle('open');
}
function copyDraft(id, btn) {
  navigator.clipboard.writeText(document.getElementById(id)?.textContent?.trim() || '').then(() => {
    btn.textContent='✓ Copied'; btn.classList.add('copied');
    setTimeout(()=>{btn.textContent='Copy';btn.classList.remove('copied');},2000);
  });
}

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Step 5 — Push to Artifact

After writing the HTML file, call:

mcp__cowork__update_artifact(
  id: "dossier-generator",
  html_path: "[path from Step 4]",
  update_summary: "Dossier for [First Last] at [Company] — [score]/100 [Bucket]"
)

If dossier-generator artifact doesn't exist (first run), call mcp__cowork__create_artifact instead with the same id.

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Output Quality Bar

• Scans in 60 seconds
• Draft messages require zero editing to send
• The Play tells exactly what to open with
• Objection responses reference this contact's specific suppressors
• "Your Overlap" section has Sender: Dan Baird badge and relational framing
• HOW TO USE guidance is specific to this person — not generic
• Evidence sections collapsed by default
• View in Wrench deep-link present and correct