meddic-call-prep-auto
Auto-generate MEDDIC-structured call prep scripts from prospect context. Pulls HubSpot deal + contact data, Apollo enrichment, CRM activity history, and calendar context to build a complete demo/discovery brief in 60 seconds. Use when: 'call prep', 'prep me for', 'demo prep', 'discovery prep', 'meddic brief', 'prep [company]', 'get me ready for [company]'.
What this skill does
<objective>
Eliminate 15-20 minutes of manual demo prep per call. Auto-generates a MEDDIC-structured briefing by pulling context from HubSpot (deal stage, properties, notes), Apollo (contact enrichment, org data), CRM activity history (via ask_agent), and Google Calendar (meeting details + attendees). Outputs a single actionable brief with talking points, objection prep, and competitive intel.
</objective>
<quick_start>
**Before a call:**
"call prep Baylor University" → pulls all context → generates MEDDIC brief
**Before a demo:**
"demo prep for University of Michigan, meeting at 2pm" → adds demo-specific flow
**From calendar:**
"prep my next call" → reads next calendar event → auto-identifies company → runs full prep
**Trigger phrases:**
- "call prep [company]"
- "prep me for [company]"
- "demo prep [company]"
- "discovery prep [company]"
- "meddic brief [company]"
- "prep my next call"
- "get me ready for my [time] meeting"
</quick_start>
<success_criteria>
- Complete MEDDIC brief generated in under 60 seconds
- All 6 MEDDIC dimensions populated (even if some are "UNKNOWN — ask this")
- Prior conversation context included from CRM activity history
- Competitive displacement angle identified if relevant
- Attendee roles mapped to MEDDIC roles (champion, economic buyer, etc.)
- 3 personalized discovery questions generated from enrichment data
- Brief fits on one screen (scannable in 30 seconds before call)
</success_criteria>
<workflow>
## Pipeline
```
TRIGGER CONTEXT GATHER SYNTHESIZE OUTPUT
───────────────────────────────────────────────────────────────────────────────
"call prep X" → Calendar: meeting details → Map attendees to → MEDDIC Brief
→ HubSpot: deal + contact → MEDDIC roles → Talking Points
→ Apollo: enrichment → Identify gaps → Questions
→ CRM: activity history → Competitive angle → Objection Prep
→ Epiphan CRM: device check → Pain hypothesis → Next Steps
```
## Stage 1: Context Gathering
Run these MCP calls in parallel:
### 1a. Calendar Context
| Tool | Purpose |
|------|---------|
| `gcal_list_events` | Find the meeting (by company name or next upcoming) |
| `gcal_get_event` | Get attendees, description, meeting link |
Extract: attendee names + emails, meeting time, any agenda in description.
### 1b. HubSpot Context
| Tool | Purpose |
|------|---------|
| `hubspot_search_companies` | Company record — lifecycle, owner, properties |
| `hubspot_search_contacts` | Contact records for each attendee |
| `hubspot_search_deals` | Active deals — stage, amount, close date, notes |
Extract: deal stage, deal amount, lifecycle stage, prior notes, form submissions, page views.
### 1c. Apollo Enrichment
| Tool | Purpose |
|------|---------|
| `apollo_organizations_enrich` | Company firmographics, tech stack, employee count |
| `apollo_people_match` | Each attendee — title, seniority, department |
| `apollo_organizations_job_postings` | Active AV/IT hiring = buying signal |
### 1d. Clari Conversation History
| Tool | Purpose |
|------|---------|
| `clari_search_calls` | Find prior calls with this prospect (by attendeeEmail, last 90 days) |
| `clari_get_call_summary` | Get AI summary, action items, key moments for each call |
For each attendee email:
1. `clari_search_calls(attendeeEmail=attendee_email, daysBack=90)`
2. For up to 3 most recent calls: `clari_get_call_summary(callId=call_id)`
3. Extract:
- **Prior conversation topics**: What was discussed last time?
- **Unresolved action items**: What did we promise to follow up on?
- **Competitor mentions**: Were Extron, Blackmagic, Crestron, vMix, Teradek mentioned?
- **Objections raised**: Price, features, integration concerns?
- **MEDDIC gaps from prior call**: Which dimensions were NOT covered?
### 1e. Prior Conversation Context
| Tool | Purpose |
|------|---------|
| `ask_agent` | Query activity history, deal notes, and engagement timeline from CRM data warehouse |
| `hubspot_get_deal` | Pull deal notes and activity log for associated deals |
Query `ask_agent` with: "Show recent activity, notes, and engagement history for [company name] in the last 90 days"
Extract: what was discussed, commitments made, objections raised, next steps promised.
### 1f. Competitive Intelligence Context
Check for stored competitive intel from the bdr-v3-competitive-intel daily task:
1. Search Gmail for recent competitive intel reports: `gmail_search_messages(q="subject:competitive-intel from:me newer_than:7d")`
2. If company or competitor was mentioned in recent Clari extractions, pull the relevant battlecard context
3. Integrate into MEDDIC brief:
- **If competitor mentioned in Clari calls**: Include displacement talking points
- **If no competitor mentioned**: Include proactive competitive positioning for top 2 competitors in prospect's vertical
- **Specific objection rebuttals**: Map Clari objections to prepared responses
**Competitive Quick Reference (by vertical):**
| Vertical | Primary Competitor | Displacement Angle |
|----------|-------------------|-------------------|
| Higher Ed | Extron SMP (discontinued) | Extron EOL → Pearl migration path |
| Corporate AV | Crestron | Pearl = simpler, no programmer needed |
| Houses of Worship | vMix | Pearl = hardware reliability, no PC crashes |
| Healthcare | Blackmagic | Pearl = HIPAA-friendly, Connect cloud mgmt |
| Courts/Legal | Teradek | Pearl = all-in-one, no separate encoder needed |
### 1g. Epiphan CRM Check
| Tool | Purpose |
|------|---------|
| `crm_search_customers` | Existing customer match — devices, orders, channel relationship |
| `analytics_search_by_email` | Device registration lookup by contact email |
**Golden Rules check:** If ANY of these are true, STOP and skip this prep:
- `lifecyclestage = 'customer'` → Route to AE/account manager, not Tim's call
- `device_count >= 1` → Existing customer, route to account manager
- `is_channel = true` → Channel partner, route to channel manager
- `hubspot_owner_id` IN ('82625923', '423155215', '190030668') → AE-owned deal (Lex Evans, Ron Epstein, Phillip Sandler), not Tim's to prep
**Exception:** If Tim explicitly says 'prep me for [company]' and the company is flagged, generate the brief but add a ⚠️ WARNING banner at the top: 'This is an EXISTING customer/channel partner/AE deal. Coordinate with the owner before reaching out.'
## Stage 2: MEDDIC Synthesis
Map gathered context into MEDDIC framework:
### M — Metrics
- **Known:** Any quantifiable goals mentioned in prior calls or deal notes
- **Hypothesis:** Based on vertical benchmarks (e.g., "Universities your size typically manage 50-200 rooms")
- **Ask if unknown:** "How are you measuring success for this project?"
### E — Economic Buyer
- **Known:** Map attendees by seniority → VP/Director/Dean = likely EB
- **ATL/BTL Validation (MANDATORY — see CLAUDE.md § ATL/BTL Classification v1.0):**
- **Confirmed EB (ATL tier):** Title matches Chief (CIO, CTO, CFO, COO), VP (AVP, SVP, EVP), President, Provost, Vice Provost, Superintendent, Director (of IT, Technology, Facilities, Academic Technology, Procurement, Materials Mgmt, Medical Education, Court Administration), Dean, Court Administrator, Clerk of Court (Federal), City Manager, County Manager, Senior Pastor, Executive Pastor → Mark EB as ✅ IDENTIFIED
- **Gray Zone — confirm budget authority:** Manager (AV/Facilities/IT) — only ATL if reports to Director+ AND has delegated budget >$25K. Ask: "Does [name] control the budget for this purchase, or do they need approval from someone above them?"
- **NOT an EB (BTL tier):** Technician, Specialist, Coordinator, Support, Administrator (Systems/Network/Database), Engineer (AV/Network/Systems), Operator, Instructor/Professor/Faculty, Designer (Learning/Instructional/Graphic), Assistant, Clerk (non-Court Admin), Volunteer, Intern, Student, Resident, Help DRelated in Productivity
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