本文へ移動
cccskills
無料GitHub で公開

cro-advisor

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.

インストール方法を見る

含まれるファイル(6)

  • SKILL.md8.7 KB
  • references/nrr_playbook.md15.1 KB
  • references/pricing_strategy.md14.9 KB
  • references/sales_playbook.md16.5 KB
  • scripts/churn_analyzer.py29.2 KB
  • scripts/revenue_forecast_model.py22.0 KB

SKILL.md(原文)

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。

CRO Advisor

Revenue frameworks for building predictable, scalable revenue engines — from $1M ARR to $100M and beyond.

Keywords

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

Quick Start

Revenue Forecasting

python scripts/revenue_forecast_model.py

Weighted pipeline model with historical win rate adjustment and conservative/base/upside scenarios.

Churn & Retention Analysis

python scripts/churn_analyzer.py

NRR, GRR, cohort retention curves, at-risk account identification, expansion opportunity segmentation.

Diagnostic Questions

Ask these before any framework:

Revenue Health

  • What's your NRR? If below 100%, everything else is a leaky bucket.
  • What percentage of ARR comes from expansion vs. new logo?
  • What's your GRR (retention floor without expansion)?

Pipeline & Forecasting

  • What's your pipeline coverage ratio (pipeline ÷ quota)? Under 3x is a problem.
  • Walk me through your top 10 deals by ARR — who closed them, how long, what drove them?
  • What's your stage-by-stage conversion rate? Where do deals die?

Sales Team

  • What % of your sales team hit quota last quarter?
  • What's average ramp time before a new AE is quota-attaining?
  • What's the sales cycle variance by segment? High variance = unpredictable forecasts.

Pricing

  • How do customers articulate the value they get? What outcome do you deliver?
  • When did you last raise prices? What happened to win rate?
  • If fewer than 20% of prospects push back on price, you're underpriced.

Core Responsibilities (Overview)

AreaWhat the CRO OwnsReference
Revenue ForecastingBottoms-up pipeline model, scenario planning, board forecastrevenue_forecast_model.py
Sales ModelPLG vs. sales-led vs. hybrid, team structure, stage definitionsreferences/sales_playbook.md
Pricing StrategyValue-based pricing, packaging, competitive positioning, price increasesreferences/pricing_strategy.md
NRR & RetentionExpansion revenue, churn prevention, health scoring, cohort analysisreferences/nrr_playbook.md
Sales Team ScalingQuota setting, ramp planning, capacity modeling, territory designreferences/sales_playbook.md
ICP & SegmentationIdeal customer profiling from won deals, segment routingreferences/nrr_playbook.md
Board ReportingARR waterfall, NRR trend, pipeline coverage, forecast vs. actualrevenue_forecast_model.py

Revenue Metrics

Board-Level (monthly/quarterly)

MetricTargetRed Flag
ARR Growth YoY2x+ at early stageDecelerating 2+ quarters
NRR> 110%< 100%
GRR (gross retention)> 85% annual< 80%
Pipeline Coverage3x+ 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 %)

Revenue Waterfall

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 Benchmarks

NRRSignal
> 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.

Red Flags

  • NRR declining two quarters in a row — customer value story is broken
  • Pipeline coverage below 3x entering the quarter — already forecasting a miss
  • Win rate dropping while sales cycle extends — competitive pressure or ICP drift
  • < 50% of sales team quota-attaining — comp plan, ramp, or quota calibration issue
  • Average deal size declining — moving downmarket under pressure (dangerous)
  • Magic Number below 0.5 — sales spend not converting to revenue
  • Forecast accuracy below 80% — reps sandbagging or pipeline quality is poor
  • Single customer > 15% of ARR — concentration risk, board will flag this
  • "Too expensive" appearing in > 40% of loss notes — value demonstration broken, not pricing
  • Expansion ARR < 20% of total ARR — upsell motion isn't working

Integration with Other C-Suite Roles

When...CRO works with...To...
Pricing changesCPO + CFOAlign value positioning, model margin impact
Product roadmapCPOEnsure features support ICP and close pipeline
Headcount planCFO + CHROJustify sales hiring with capacity model and ROI
NRR decliningCPO + COORoot cause: product gaps or CS process failures
Enterprise expansionCEOExecutive sponsorship, board-level relationships
Revenue targetsCFOBottoms-up model to validate top-down board targets
Pipeline SLACMOMQL → SQL conversion, CAC by channel, attribution
Security reviewsCISOUnblock enterprise deals with security artifacts
Sales ops scalingCOORevOps staffing, commission infrastructure, tooling

Resources

  • Sales process, MEDDPICC, comp plans, hiring: references/sales_playbook.md
  • Pricing models, value-based pricing, packaging: references/pricing_strategy.md
  • NRR deep dive, churn anatomy, health scoring, expansion: references/nrr_playbook.md
  • Revenue forecast model (CLI): scripts/revenue_forecast_model.py
  • Churn & retention analyzer (CLI): scripts/churn_analyzer.py

Proactive Triggers

Surface these without being asked when you detect them in company context:

  • NRR < 100% → leaky bucket, retention must be fixed before pouring more in
  • Pipeline coverage < 3x → forecast at risk, flag to CEO immediately
  • Win rate declining → sales process or product-market alignment issue
  • Top customer concentration > 20% ARR → single-point-of-failure revenue risk
  • No pricing review in 12+ months → leaving money on the table or losing deals

Output Artifacts

RequestYou 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

Reasoning Technique: Chain of Thought

Pipeline math must be explicit: leads → MQLs → SQLs → opportunities → closed. Show conversion rates at each stage. Question any assumption above historical averages.

Communication

All output passes the Internal Quality Loop before reaching the founder (see ../agent-protocol/SKILL.md).

  • Self-verify: source attribution, assumption audit, confidence scoring
  • Peer-verify: cross-functional claims validated by the owning role
  • Critic pre-screen: high-stakes decisions reviewed by Executive Mentor
  • Output format: Bottom Line → What (with confidence) → Why → How to Act → Your Decision
  • Results only. Every finding tagged: 🟢 verified, 🟡 medium, 🔴 assumed.

Context Integration

  • Always read company-context.md before responding (if it exists)
  • During board meetings: Use only your own analysis in Phase 2 (no cross-pollination)
  • Invocation: You can request input from other roles: [INVOKE:role|question]

レビュー

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。

同じリポジトリのスキル

概要と使いどころ

Accessibility audit skill for scanning, fixing, and verifying WCAG 2.2 Level A and AA compliance across React, Next.js, Vue, Angular, Svelte, and plain HTML codebases. Use when auditing accessibility, fixing a11y violations, checking color contrast, generating compliance reports, or integrating accessibility checks into CI/CD pipelines.

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。

alirezarezvani/claude-skills2.8万2026年8月30日 更新

When the user wants to plan, design, or implement an A/B test or experiment. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "conversion experiment," "statistical significance," or "test this." For tracking implementation, see analytics-tracking.

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。

alirezarezvani/claude-skills2.8万2026年8月30日 更新

When the user needs to generate, iterate, or scale ad creative for paid advertising. Use when they say 'write ad copy,' 'generate headlines,' 'create ad variations,' 'bulk creative,' 'iterate on ads,' 'ad copy validation,' 'RSA headlines,' 'Meta ad copy,' 'LinkedIn ad,' or 'creative testing.' This is pure creative production — distinct from paid-ads (campaign strategy). Use ad-creative when you need the copy, not the campaign plan.

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。

alirezarezvani/claude-skills2.8万2026年8月30日 更新

Adversarial code review that breaks the self-review monoculture. Use when you want a genuinely critical review of recent changes, before merging a PR, or when you suspect Claude is being too agreeable about code quality. Forces perspective shifts through hostile reviewer personas that catch blind spots the author's mental model shares with the reviewer.

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。

alirezarezvani/claude-skills2.8万2026年8月30日 更新

aeo

無料

Answer Engine Optimization (AEO) skill — optimize content to be cited by AI language models (ChatGPT, Perplexity, Claude, Gemini, Mistral) as authoritative sources. Distinct from SEO — AEO optimizes for citation in LLM-generated responses, not search rankings. Use when planning content for AI-first search audiences, auditing existing content for E-E-A-T signals, tracking which pages get cited by which LLMs, or building a citation-friendly content strategy. Triggers — 'AEO audit', 'optimize for ChatGPT', 'get cited by Perplexity', 'LLM citation strategy', 'answer engine optimization', 'content for AI search', 'E-E-A-T audit'. Output is a markdown audit report (default) or JSON for pipeline integration. Stdlib-only Python tools.

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。

alirezarezvani/claude-skills2.8万2026年8月30日 更新

Mint a tamper-evident, post-quantum-signed receipt for a consequential agent action (deploy, delete, pay, grant-access, model decision) so it can be verified later from the certificate alone. Use when an autonomous agent takes a side-effecting action that may need to be proven later, or when satisfying EU AI Act Article 12 record-keeping. Three decisions: whether an action needs a receipt, minting it, verifying it. Signing is delegated to the open-source OpenAgentOntology package. Not after-the-fact log analysis; not a hosted notary; not a legal opinion.

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。

alirezarezvani/claude-skills2.8万2026年8月30日 更新

alirezarezvani のスキルをすべて見る

このスキルの問題を報告する