From agi-super-team
Prepares for client/lead calls by gathering CRM data, conducting web research, updating CRM records, and generating a conversation plan as a PDF.
How this skill is triggered — by the user, by Claude, or both
Slash command
/agi-super-team:call-prepThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
> Preparation for a call with a client/lead: research, CRM update, conversation plan, PDF
Preparation for a call with a client/lead: research, CRM update, conversation plan, PDF
query-leadsweasyprint (Python)| What | Path |
|---|---|
| CRM Companies | $CRM_PATH/contacts/companies.csv |
| CRM People | $CRM_PATH/contacts/people.csv |
| CRM Leads | $CRM_PATH/relationships/leads.csv |
| CRM Activities | $CRM_PATH/activities.csv |
| PM Tasks | $PM_PATH/pm_tasks_master.csv |
| Output PDF | $PROJECT_ROOT/docs/{slug}-call-prep.pdf |
Read everything from CRM about this person/company:
1. companies.csv -- company record
2. people.csv -- person record + notes
3. leads.csv -- lead stage, priority, next_action, notes
4. activities.csv -- communication history (emails, calls, messages)
5. pm_tasks_master.csv -- related tasks
Important: gather ALL interaction history -- not just the latest entry.
Run in parallel:
1. WebSearch: "{name} {company}" -- general info
2. WebSearch: "{name} linkedin founder" -- career, track record
3. WebFetch: company website -- products, positioning, pricing
4. WebSearch: "{company} 2025 2026" -- latest news
5. WebFetch: LinkedIn profile (if URL exists in CRM)
What to look for:
Based on research, update:
companies.csv -- description, industry, sizepeople.csv -- role, notes from researchleads.csv -- notesUse skill update-lead.
If a client workspace exists in Drive (Clients/{CompanyName}/), check:
DM="$GOOGLE_TOOLS_PATH/.venv/bin/python3 $GOOGLE_TOOLS_PATH/drive_manager.py"
# Search for client folder
$DM search "CompanyName" --folder <YOUR_CLIENTS_FOLDER_ID>
# List docs in client folder
$DM list CLIENT_FOLDER_ID
# For each shared doc — check if client opened/edited it
$DM info DOC_ID
# → Look at lastModifiedBy: if it's the client, they filled it in
# → Look at modifiedTime: when was it last touched
If questionnaire exists and client filled it in: read their answers and incorporate into conversation plan.
If questionnaire exists but client didn't fill it: mention on the call, go through questions verbally.
If no workspace exists: consider creating one with client-workspace skill.
See skill: client-workspace
Standard discovery call structure:
Phase 1: Small talk + context (~3 min)
- How you got in touch
- What you know about them (but not everything -- let them tell)
Phase 2: Business discovery (~10 min)
- What does the company do?
- What products/services?
- Who are the customers?
- What stage? (pre-launch, growth, scaling)
- How many people on the team?
Phase 3: Pain point discovery (~10 min)
- What specifically hurts? What problem do they want to solve?
- What have they already tried?
- What didn't work and why?
- What is the budget/expectations?
- What are the deadlines?
Phase 4: Show relevance (~5 min)
- Specific example of how we solved a similar task
- Don't sell -- show that you understand the problem
- Adapt to the level of the conversation partner
Phase 5: Propose a format (~5 min)
- Option A: Audit (5-10h, understand scope)
- Option B: Pilot (fixed task, 2 weeks)
- Option C: Partnership (after pilot)
- DO NOT name a price without scope
Phase 6: Next steps (~2 min)
- Specific next step
- Deadline
- What is needed from them
Typical risks:
| Risk | How to respond |
|---|---|
| Wants to "look" for free | An audit is work. Minimum paid. |
| Scope too large | Narrow down to one project/channel |
| Wants equity deal right away | Paid pilot first |
| Already has a solution, comparing | Ask who they're comparing with |
| Not the decision maker | Ask who makes the decision |
| No budget | Propose a minimal pilot |
Create HTML with all info, convert to PDF via weasyprint:
import weasyprint
html = "..." # structured HTML with sections 1-5
weasyprint.HTML(string=html).write_pdf('/path/to/output.pdf')
PDF structure:
Open PDF:
open /path/to/output.pdf
User: prepare for a call with Alisa from shftd.ai
Claude:
1. Reads CRM → comp-shftd, p-shftd-001, lead-shftd-001
2. WebSearch "Alisa Chumachenko shftd.ai" → Game Insight, GOSU.ai, Forbes
3. WebFetch shftd.ai → pre-launch, venture studio
4. Updates CRM with research
5. Builds plan: discovery call, 6 phases, specific questions
6. Generates PDF → docs/alisa-shftd-call-prep.pdf
User: prepare for a follow-up with Client F Tech
Claude:
1. Reads CRM → comp-clientf, lead, activities (history)
2. Checks what was discussed earlier
3. Builds plan: review progress, discuss blockers, next steps
4. Generates PDF
pip install weasyprint)query-leads -- reading CRM dataupdate-lead -- updating CRM with researchdaily-briefing -- may contain info about scheduled callsgit-workflow -- commit CRM changes after updatenpx claudepluginhub aaaaqwq/agi-super-team --plugin agi-super-teamGenerates sales call prep briefs with company/attendee research, agendas, and questions via web search and user input or CRM/email connectors.
Generates scannable call prep briefs from Common Room signals, account/contact research, Gong history, talking points, objections, and recent news for customer/prospect meetings.
Activate for: pre-call brief, before the call, prepare for call, meeting prep, call prep, discovery prep, demo prep, before meeting, call preparation, what should I know before, deal health, deal review, deal brief, opportunity review, account review, QBR prep, renewal prep. NOT for: prospect research (use prospect-research), outreach drafting (use outreach), post-call follow-up (use follow-up), pipeline reporting (use pipeline).