| name | sales-playbook |
| description | "Build value-based pricing frameworks, generate pre-call briefings, analyze sales calls for patterns, and create tiered pricing packages. Use when asked to "create pricing tiers", "pre-call briefing", "analyze this sales call", "value-based pricing", "upsell strategy", or "deal packaging"." |
AI Sales Playbook — Value-Based Pricing & Deal Upselling
Preamble (runs on skill start)
python3 telemetry/version_check.py 2>/dev/null || true
python3 telemetry/telemetry_init.py 2>/dev/null || true
Privacy: This skill logs usage locally to ~/.ai-marketing-skills/analytics/. Remote telemetry is opt-in only. No code, file paths, or repo content is ever collected. See telemetry/README.md.
Framework for value-based pricing that moves deals from $10K/mo → $40-100K/mo. Pre-call briefings, tiered package generation, post-call analysis, and a pattern library for training sales teams on proven pricing techniques.
When to Use
Use this skill when:
- Preparing for a sales call and need competitive data to anchor on value
- Building tiered pricing proposals for prospects at different deal sizes
- Analyzing sales call transcripts to score against the value-based pricing framework
- Training sales reps on proven pricing patterns and objection handling
- Upselling existing deals by identifying missed value levers
Tools
Pre-Call Preparation
| Script | Purpose | Key Command |
|---|
value_pricing_briefing.py | Generate pre-call briefing with competitive data, value calcs, and conversation hooks | python3 value_pricing_briefing.py --domain acme.com --competitors "comp1.com,comp2.com" |
value_pricing_packager.py | Generate tiered S/M/L + performance pricing packages | python3 value_pricing_packager.py --target-monthly 80000 --services "seo,cro,content,paid" |
Post-Call Analysis
| Script | Purpose | Key Command |
|---|
call_analyzer.py | Score a call transcript against the value-based pricing framework | python3 call_analyzer.py --transcript call.txt |
pricing_pattern_library.py | Reference library of 10 proven pricing patterns + training mode | python3 pricing_pattern_library.py --list |
Configuration
All scripts use environment variables for API keys:
Optional Environment Variables
AHREFS_API_KEY — Ahrefs API key (Briefing Generator, optional — uses stubs without it)SEMRUSH_API_KEY — SEMrush API key (Briefing Generator, optional — uses stubs without it)ANTHROPIC_API_KEY — Anthropic API key (Call Analyzer, Pattern Library scenario mode)OPENAI_API_KEY — OpenAI API key (alternative to Anthropic for LLM features)
Scripts work without API keys using built-in stubs and sample data for testing.
Key Concepts
The Value-Based Pricing Framework
- Lead with data, not your pitch — Show the prospect their competitive gaps before discussing services
- Anchor high — Present the premium tier first so the target tier feels reasonable
- Tie price to value — Every dollar of investment maps to projected ROI
- Use competitive triggers — Competitor rankings activate urgency without being pushy
- Present tiered options — 3-4 tiers with clear tradeoffs, always including a performance option
Pricing Framework Score (0-100)
The call analyzer scores calls against these criteria:
- Showed data before pitching (20 pts)
- Presented tiered options (20 pts)
- Anchored high first (15 pts)
- Tied price to value/ROI (15 pts)
- Used competitive triggers (15 pts)
- Got prospect to state their own pain (15 pts)
Dependencies
- Python 3.9+
requests (for API integrations)- No other external dependencies
