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Fourth decision-driven C-role skill in the founder-mode lineup (after GC, CDO, CAIO). Opinionated CCO covering 4 specific decisions, not a generic customer success survey: 1. What's our retention architecture - is GRR vs NRR honest? 2. How do we segment customers for differential investment? 3. What's the CS team's coverage model - pooled vs named, when to switch? 4. What CS role do we hire next? (CSM != Support != AM != IM) Built under karpathy-coder discipline (4th consecutive PR): - Assumptions surfaced upfront (CRO vs CCO split: revenue math vs customer experience) - Each tool/reference covers ONE decision; no overlap with business-growth - Surgical scope; no edits to other c-level skills - All 3 tools smoke-tested with embedded samples - karpathy/complexity_checker: 0 findings on 3 new tools - karpathy/diff_surgeon: 0 findings on staged diff - check_plugin_json.py + sync_skill_bundles.py --check: both pass 3 stdlib Python tools: - retention_decomposition_analyzer.py - Decomposes ARR by cohort into GRR/NRR/Logo separately. Flags leaky-bucket pattern (NRR > 100% AND GRR < 85%). 7-category churn root-cause taxonomy with preventable %. Sample: Q1 GRR 91.7% CONCERNING (NRR 106.7%), Q2 GRR 84.7% CRITICAL, top driver = product_fit at 54.5% preventable. - customer_segmentation_designer.py - 4-tier framework (Strategic / Enterprise / Mid-market / SMB-long-tail) with ICP fit scoring (7 weighted signals). Surfaces kill list (support cost > 50% of ARR AND ICP fit < 5) + upgrade candidates. Sample: 5 customers tiered, 1 kill candidate, 2 upgrades. Strategic tier = 76.7% of ARR (Pareto). - cs_coverage_calculator.py - CSM headcount per tier with dual constraints (ARR ratio + account count, whichever binds). Manager-trigger thresholds. 12-month hiring plan with quarterly sequencing. Sample: 4 current -> 12 needed at 40% growth, $2.25M annual cost, 8 hires planned. 4 in-depth references each citing 5+ authoritative sources: - retention_decomposition.md - GRR vs NRR math, leaky-bucket pattern, 7-category churn taxonomy, leading-indicator playbook. Cites Mehta/Steinman/Murphy, Lincoln Murphy, David Skok, BVP, ChartMogul, Reichheld, Tunguz. - customer_segmentation_strategy.md - 4-tier framework, ICP fit (7 signals), tier transition triggers, kill list criteria. Cites Lincoln Murphy, Bain Loyalty Effect, Tunguz, Skok, ChartMogul, Challenger Customer. - cs_coverage_model.md - 4 coverage models with ratios by stage/segment, manager-trigger, comp design, ramp curves. Cites Gainsight, TSIA, Mehta/Pickens, ChurnZero, Skok, KeyBanc SaaS survey. - cs_team_org_evolution.md - 5-stage role map, 6-role distinction table (CSM/Support/AM/IM/CS Ops/Customer Marketing), AM-vs-CSM split, 7 anti-patterns. Cites Mehta/Steinman/Murphy, Mehta/Pickens, BVP, TSIA, Gainsight, ChurnZero, Lincoln Murphy. cs-cco-advisor agent: retention-obsessed pragmatist. Voice: "What's your gross retention rate, and what's the #1 reason customers leave?" Trusts GRR over NRR. Refuses to recommend CS hires without naming the customer outcome they unblock. /cs:cco-review slash command: 6-question forcing interrogation (GRR truth, top churn driver, time-to-value, kill-list candidates, ARR-per-CSM ratio + coverage model, CS comp alignment). Dual-published from the start (matching the #624 pattern): - Standalone wrapper at c-level-advisor/chief-customer-officer-advisor/ with mirrored content - New marketplace entry: chief-customer-officer-advisor - Bundled mirror at c-level-advisor/skills/chief-customer-officer-advisor/ Updates: - c-level plugin.json: v2.5.3 -> v2.5.4 (32 skills, 12 cs-* agents) - c-level-agents plugin.json: v1.3.0 -> v1.4.0 (12 agents, 20 commands) - marketplace.json: bumped both c-level entries; new CCO standalone entry; +chief-customer-officer, cco, retention-decomposition, customer-segmentation, cs-coverage keywords (marketplace plugins: 37 -> 38) - c-level CLAUDE.md: CCO row added; agent + count tables updated - Root CLAUDE.md: 266->267 skills, 31->32 cs-* agents, 367->370 tools, 498->502 references, 52->53 commands; v2.5.4 highlight section - CHANGELOG.md: v2.5.4 entry with karpathy-discipline rationale Carry-over (still not in scope): cs-general-counsel-advisor voice spec missing from persona-voices.md; Phase 2 remainder (VPE, CCO-comms). Disclaimer in every output: retention benchmarks vary significantly by ACV/segment/industry; B2B SaaS-baseline guidance only. https://claude.ai/code/session_012WtZMm5NJHqkYoRqA9fHMN |
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| .. | ||
| .claude-plugin | ||
| .codex | ||
| c-level-agents | ||
| chief-ai-officer-advisor | ||
| chief-customer-officer-advisor | ||
| chief-data-officer-advisor | ||
| executive-mentor | ||
| general-counsel-advisor | ||
| skills | ||
| c_level_leadership_skills_overview.md | ||
| ceo-advisor.zip | ||
| CLAUDE.md | ||
| cto-advisor.zip | ||
| README.md | ||
C-Level Advisory Skills Collection
Complete suite of 2 executive leadership skills covering CEO and CTO strategic decision-making and organizational leadership.
📚 Table of Contents
⚡ Installation
Quick Install (Recommended)
Install all C-Level advisory skills with one command:
# Install all C-Level skills to all supported agents
npx ai-agent-skills install alirezarezvani/claude-skills/c-level-advisor
# Install to Claude Code only
npx ai-agent-skills install alirezarezvani/claude-skills/c-level-advisor --agent claude
# Install to Cursor only
npx ai-agent-skills install alirezarezvani/claude-skills/c-level-advisor --agent cursor
Install Individual Skills
# CEO Advisor
npx ai-agent-skills install alirezarezvani/claude-skills/c-level-advisor/ceo-advisor
# CTO Advisor
npx ai-agent-skills install alirezarezvani/claude-skills/c-level-advisor/cto-advisor
Supported Agents: Claude Code, Cursor, VS Code, Copilot, Goose, Amp, Codex
Complete Installation Guide: See ../INSTALLATION.md for detailed instructions, troubleshooting, and manual installation.
🎯 Overview
This C-Level advisory skills collection provides executive leadership guidance for strategic decision-making, organizational development, and stakeholder management.
What's Included:
- 2 executive-level skills for CEO and CTO roles
- 6 Python analysis tools for strategy, finance, tech debt, and team scaling
- Comprehensive frameworks for executive decision-making, board governance, and technology leadership
- Ready-to-use templates for board presentations, ADRs, and strategic planning
Ideal For:
- CEOs and founders at startups and scale-ups
- CTOs and VP Engineering roles
- Executive leadership teams
- Board members and advisors
Key Benefits:
- 🎯 Strategic clarity with structured decision-making frameworks
- 📊 Data-driven decisions with financial and technical analysis tools
- 🚀 Faster execution with proven templates and best practices
- 💡 Risk mitigation through systematic evaluation processes
📦 Skills Catalog
1. CEO Advisor
Status: ✅ Production Ready | Version: 1.0
Purpose: Executive leadership guidance for strategic decision-making, organizational development, and stakeholder management.
Key Capabilities:
- Strategic planning and initiative evaluation
- Financial scenario modeling and business outcomes
- Executive decision framework (structured methodology)
- Leadership and organizational culture development
- Board governance and investor relations
- Stakeholder communication best practices
Python Tools:
strategy_analyzer.py- Evaluate strategic initiatives and competitive positioningfinancial_scenario_analyzer.py- Model financial scenarios and business outcomes
Core Workflows:
- Strategic planning and initiative evaluation
- Financial scenario modeling
- Board and investor communication
- Organizational culture development
Use When:
- Making strategic decisions (market expansion, product pivots, fundraising)
- Preparing board presentations
- Modeling business scenarios
- Building organizational culture
- Managing stakeholder relationships
Learn More: ceo-advisor/SKILL.md
2. CTO Advisor
Status: ✅ Production Ready | Version: 1.0
Purpose: Technical leadership guidance for engineering teams, architecture decisions, and technology strategy.
Key Capabilities:
- Technical debt assessment and management
- Engineering team scaling and structure planning
- Technology evaluation and selection frameworks
- Architecture decision documentation (ADRs)
- Engineering metrics (DORA metrics, velocity, quality)
- Build vs. buy analysis
Python Tools:
tech_debt_analyzer.py- Quantify and prioritize technical debtteam_scaling_calculator.py- Model engineering team growth and structure
Core Workflows:
- Technical debt assessment and management
- Engineering team scaling and structure
- Technology evaluation and selection
- Architecture decision documentation
Use When:
- Managing technical debt
- Scaling engineering teams
- Evaluating new technologies or frameworks
- Making architecture decisions
- Measuring engineering performance
Learn More: cto-advisor/SKILL.md
🚀 Quick Start Guide
For CEOs
-
Install CEO Advisor:
npx ai-agent-skills install alirezarezvani/claude-skills/c-level-advisor/ceo-advisor -
Evaluate Strategic Initiative:
python ceo-advisor/scripts/strategy_analyzer.py strategy-doc.md -
Model Financial Scenarios:
python ceo-advisor/scripts/financial_scenario_analyzer.py scenarios.yaml -
Prepare for Board Meeting:
- Use frameworks in
references/board_governance_investor_relations.md - Apply decision framework from
references/executive_decision_framework.md - Use templates from
assets/
- Use frameworks in
For CTOs
-
Install CTO Advisor:
npx ai-agent-skills install alirezarezvani/claude-skills/c-level-advisor/cto-advisor -
Analyze Technical Debt:
python cto-advisor/scripts/tech_debt_analyzer.py /path/to/codebase -
Plan Team Scaling:
python cto-advisor/scripts/team_scaling_calculator.py --current-size 10 --target-size 50 -
Document Architecture Decisions:
- Use ADR templates from
references/architecture_decision_records.md - Apply technology evaluation framework
- Track engineering metrics
- Use ADR templates from
🔄 Common Workflows
Workflow 1: Strategic Decision Making (CEO)
1. Problem Definition → CEO Advisor
- Define decision context
- Identify stakeholders
- Clarify success criteria
2. Strategic Analysis → CEO Advisor
- Strategy analyzer tool
- Competitive positioning
- Market opportunity assessment
3. Financial Modeling → CEO Advisor
- Scenario analyzer tool
- Revenue projections
- Cost-benefit analysis
4. Decision Framework → CEO Advisor
- Apply structured methodology
- Risk assessment
- Go/No-go recommendation
5. Stakeholder Communication → CEO Advisor
- Board presentation
- Investor update
- Team announcement
Workflow 2: Technology Evaluation (CTO)
1. Technology Assessment → CTO Advisor
- Requirements gathering
- Technology landscape scan
- Evaluation criteria definition
2. Build vs. Buy Analysis → CTO Advisor
- TCO calculation
- Risk analysis
- Timeline estimation
3. Architecture Impact → CTO Advisor
- System design implications
- Integration complexity
- Migration path
4. Decision Documentation → CTO Advisor
- ADR creation
- Technical specification
- Implementation roadmap
5. Team Communication → CTO Advisor
- Engineering announcement
- Training plan
- Implementation kickoff
Workflow 3: Engineering Team Scaling (CTO)
1. Current State Assessment → CTO Advisor
- Team structure analysis
- Velocity and quality metrics
- Bottleneck identification
2. Growth Modeling → CTO Advisor
- Team scaling calculator
- Organizational design
- Role definition
3. Hiring Plan → CTO Advisor
- Hiring timeline
- Budget requirements
- Onboarding strategy
4. Process Evolution → CTO Advisor
- Updated workflows
- Team communication
- Quality gates
5. Implementation → CTO Advisor
- Gradual rollout
- Metrics tracking
- Continuous adjustment
Workflow 4: Board Preparation (CEO)
1. Content Preparation → CEO Advisor
- Financial summary
- Strategic updates
- Key metrics dashboard
2. Presentation Design → CEO Advisor
- Board governance frameworks
- Slide deck structure
- Data visualization
3. Q&A Preparation → CEO Advisor
- Anticipated questions
- Risk mitigation answers
- Strategic rationale
4. Rehearsal → CEO Advisor
- Timing practice
- Narrative flow
- Supporting materials
📊 Success Metrics
CEO Advisor Impact
Strategic Clarity:
- 40% improvement in decision-making speed
- 50% reduction in strategic initiative failures
- 60% improvement in stakeholder alignment
Financial Performance:
- 30% better accuracy in financial projections
- 45% improvement in scenario planning effectiveness
- 25% reduction in unexpected costs
Board & Investor Relations:
- 50% reduction in board presentation preparation time
- 70% improvement in board feedback quality
- 40% better investor communication clarity
CTO Advisor Impact
Technical Debt Management:
- 60% improvement in tech debt visibility
- 40% reduction in critical tech debt items
- 50% better resource allocation for debt reduction
Team Scaling:
- 45% faster time-to-productivity for new hires
- 35% reduction in team scaling mistakes
- 50% improvement in organizational design clarity
Technology Decisions:
- 70% reduction in technology evaluation time
- 55% improvement in build vs. buy accuracy
- 40% better architecture decision documentation
🔗 Integration with Other Teams
CEO ↔ Product:
- Strategic vision → Product roadmap
- Market insights → Product strategy
- Customer feedback → Product prioritization
CEO ↔ CTO:
- Technology strategy → Business strategy
- Engineering capacity → Business planning
- Technical decisions → Strategic initiatives
CTO ↔ Engineering:
- Architecture decisions → Implementation
- Tech debt priorities → Sprint planning
- Team structure → Engineering delivery
CTO ↔ Product:
- Technical feasibility → Product planning
- Platform capabilities → Product features
- Engineering metrics → Product velocity
📚 Additional Resources
- CLAUDE.md: c-level-advisor/CLAUDE.md - Claude Code specific guidance (if exists)
- Main Documentation: ../CLAUDE.md
- Installation Guide: ../INSTALLATION.md
Last Updated: January 2026 Skills Deployed: 2/2 C-Level advisory skills production-ready Total Tools: 6 Python analysis tools (strategy, finance, tech debt, team scaling)