Use when long-lived agents, project memories, system prompts, or playbooks are repeatedly summarized, rewritten, or optimized and risk losing domain detail, provenance, or execution learnings.
日本語の概要は準備中です。原文の説明を表示しています。
Revenue leadership for B2B SaaS companies. Revenue forecasting, sales model design, pricing strategy, net revenue retention, and sales team scaling. Use when designing the revenue engine, setting quotas, modeling NRR, evaluating pricing, building board forecasts, or when user mentions CRO, chief revenue officer, revenue strategy, sales model, ARR growth, NRR, expansion revenue, churn, pricing strategy, or sales capacity.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Attribution: Sourced from alirezarezvani/claude-skills by Alireza Rezvani.
Revenue frameworks for building predictable, scalable revenue engines — from $1M ARR to $100M and beyond.
CRO, chief revenue officer, revenue strategy, ARR, MRR, sales model, pipeline, revenue forecasting, pricing strategy, net revenue retention, NRR, gross revenue retention, GRR, expansion revenue, upsell, cross-sell, churn, customer success, sales capacity, quota, ramp, territory design, MEDDPICC, PLG, product-led growth, sales-led growth, enterprise sales, SMB, self-serve, value-based pricing, usage-based pricing, ICP, ideal customer profile, revenue board reporting, sales cycle, CAC payback, magic number
python scripts/revenue_forecast_model.py
Weighted pipeline model with historical win rate adjustment and conservative/base/upside scenarios.
python scripts/churn_analyzer.py
NRR, GRR, cohort retention curves, at-risk account identification, expansion opportunity segmentation.
Ask these before any framework:
Revenue Health
Pipeline & Forecasting
Sales Team
Pricing
| Area | What the CRO Owns | Reference |
|---|---|---|
| Revenue Forecasting | Bottoms-up pipeline model, scenario planning, board forecast | revenue_forecast_model.py |
| Sales Model | PLG vs. sales-led vs. hybrid, team structure, stage definitions | references/sales_playbook.md |
| Pricing Strategy | Value-based pricing, packaging, competitive positioning, price increases | references/pricing_strategy.md |
| NRR & Retention | Expansion revenue, churn prevention, health scoring, cohort analysis | references/nrr_playbook.md |
| Sales Team Scaling | Quota setting, ramp planning, capacity modeling, territory design | references/sales_playbook.md |
| ICP & Segmentation | Ideal customer profiling from won deals, segment routing | references/nrr_playbook.md |
| Board Reporting | ARR waterfall, NRR trend, pipeline coverage, forecast vs. actual | revenue_forecast_model.py |
| Metric | Target | Red Flag |
|---|---|---|
| ARR Growth YoY | 2x+ at early stage | Decelerating 2+ quarters |
| NRR | > 110% | < 100% |
| GRR (gross retention) | > 85% annual | < 80% |
| Pipeline Coverage | 3x+ quota | < 2x entering quarter |
| Magic Number | > 0.75 | < 0.5 (fix unit economics before spending more) |
| CAC Payback | < 18 months | > 24 months |
| Quota Attainment % | 60-70% of reps | < 50% (calibration problem) |
Magic Number: Net New ARR × 4 ÷ Prior Quarter S&M Spend
CAC Payback: S&M Spend ÷ New Logo ARR × (1 / Gross Margin %)
Opening ARR
+ New Logo ARR
+ Expansion ARR (upsell, cross-sell, seat adds)
- Contraction ARR (downgrades)
- Churned ARR
= Closing ARR
NRR = (Opening + Expansion - Contraction - Churn) / Opening
| NRR | Signal |
|---|---|
| > 120% | World-class. Grow even with zero new logos. |
| 100-120% | Healthy. Existing base is growing. |
| 90-100% | Concerning. Churn eating growth. |
| < 90% | Crisis. Fix before scaling sales. |
| When... | CRO works with... | To... |
|---|---|---|
| Pricing changes | CPO + CFO | Align value positioning, model margin impact |
| Product roadmap | CPO | Ensure features support ICP and close pipeline |
| Headcount plan | CFO + CHRO | Justify sales hiring with capacity model and ROI |
| NRR declining | CPO + COO | Root cause: product gaps or CS process failures |
| Enterprise expansion | CEO | Executive sponsorship, board-level relationships |
| Revenue targets | CFO | Bottoms-up model to validate top-down board targets |
| Pipeline SLA | CMO | MQL → SQL conversion, CAC by channel, attribution |
| Security reviews | CISO | Unblock enterprise deals with security artifacts |
| Sales ops scaling | COO | RevOps staffing, commission infrastructure, tooling |
references/sales_playbook.mdreferences/pricing_strategy.mdreferences/nrr_playbook.mdscripts/revenue_forecast_model.pyscripts/churn_analyzer.pySurface these without being asked when you detect them in company context:
| Request | You Produce |
|---|---|
| "Forecast next quarter" | Pipeline-based forecast with confidence intervals |
| "Analyze our churn" | Cohort churn analysis with at-risk accounts and intervention plan |
| "Review our pricing" | Pricing analysis with competitive benchmarks and recommendations |
| "Scale the sales team" | Capacity model with quota, ramp, territories, comp plan |
| "Revenue board section" | ARR waterfall, NRR, pipeline, forecast, risks |
Pipeline math must be explicit: leads → MQLs → SQLs → opportunities → closed. Show conversion rates at each stage. Question any assumption above historical averages.
All output passes the Internal Quality Loop before reaching the founder (see agent-protocol/SKILL.md).
company-context.md before responding (if it exists)[INVOKE:role|question]まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Use when long-lived agents, project memories, system prompts, or playbooks are repeatedly summarized, rewritten, or optimized and risk losing domain detail, provenance, or execution learnings.
日本語の概要は準備中です。原文の説明を表示しています。
Use this skill when the user explicitly asks to map, document, or onboard into an existing codebase. Trigger for prompts like "map this codebase", "document this architecture", "onboard me to this repo", or "create codebase docs". Do not trigger for routine feature implementation, bug fixes, or narrow code edits unless the user asks for repository-level discovery.
日本語の概要は準備中です。原文の説明を表示しています。
Adds educational comments to a code file so it becomes a learning resource, asking for the file (with a list of close matches) if none is given. Use when preparing code for learners, onboarding, or teaching.
日本語の概要は準備中です。原文の説明を表示しています。
Write, debug, and optimize Adobe Illustrator automation scripts using ExtendScript (JavaScript/JSX). Use when creating or modifying scripts that manipulate documents, layers, paths, text frames, colors, symbols, artboards, or any Illustrator DOM objects. Covers the complete JavaScript object model, coordinate system, measurement units, export workflows, and scripting best practices.
日本語の概要は準備中です。原文の説明を表示しています。
Patterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when: - Building AI agents that call external tools (APIs, databases, file systems) - Implementing policy-based access controls for agent tool usage - Adding semantic intent classification to detect dangerous prompts - Creating trust scoring systems for multi-agent workflows - Building audit trails for agent actions and decisions - Enforcing rate limits, content filters, or tool restrictions on agents - Working with any agent framework (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)
日本語の概要は準備中です。原文の説明を表示しています。
Check any AI agent codebase against the OWASP Agentic Security Initiative (ASI) Top 10 risks. Use this skill when: - Evaluating an agent system's security posture before production deployment - Running a compliance check against OWASP ASI 2026 standards - Mapping existing security controls to the 10 agentic risks - Generating a compliance report for security review or audit - Comparing agent framework security features against the standard - Any request like "is my agent OWASP compliant?", "check ASI compliance", or "agentic security audit"
日本語の概要は準備中です。原文の説明を表示しています。