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-teamPrepares for sales calls with account context, attendee research, and suggested agenda. Works standalone with user input and web research, and can pull from CRM, email, chat, or transcripts when connected.
Prepares call briefs using Common Room signals: account research, contact research, signal synthesis, and recency web search.
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).