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。
Financial leadership for startups and scaling companies. Financial modeling, unit economics, fundraising strategy, cash management, and board financial packages. Use when building financial models, analyzing unit economics, planning fundraising, managing cash runway, preparing board materials, or when user mentions CFO, burn rate, runway, fundraising, unit economics, LTV, CAC, term sheets, or financial strategy.
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.
Strategic financial frameworks for startup CFOs and finance leaders. Numbers-driven, decisions-focused.
This is not a financial analyst skill. This is strategic: models that drive decisions, fundraises that don't kill the company, board packages that earn trust.
CFO, chief financial officer, burn rate, runway, unit economics, LTV, CAC, fundraising, Series A, Series B, term sheet, cap table, dilution, financial model, cash flow, board financials, FP&A, SaaS metrics, ARR, MRR, net dollar retention, gross margin, scenario planning, cash management, treasury, working capital, burn multiple, rule of 40
# Burn rate & runway scenarios (base/bull/bear)
python scripts/burn_rate_calculator.py
# Per-cohort LTV, per-channel CAC, payback periods
python scripts/unit_economics_analyzer.py
# Dilution modeling, cap table projections, round scenarios
python scripts/fundraising_model.py
| Area | What It Covers | Reference |
|---|---|---|
| Financial Modeling | Bottoms-up P&L, three-statement model, headcount cost model | references/financial_planning.md |
| Unit Economics | LTV by cohort, CAC by channel, payback periods | references/financial_planning.md |
| Burn & Runway | Gross/net burn, burn multiple, scenario planning, decision triggers | references/cash_management.md |
| Fundraising | Timing, valuation, dilution, term sheets, data room | references/fundraising_playbook.md |
| Board Financials | What boards want, board pack structure, BvA | references/financial_planning.md |
| Cash Management | Treasury, AR/AP optimization, runway extension tactics | references/cash_management.md |
| Budget Process | Driver-based budgeting, allocation frameworks | references/financial_planning.md |
| Category | Metric | Target | Frequency |
|---|---|---|---|
| Efficiency | Burn Multiple | < 1.5x | Monthly |
| Efficiency | Rule of 40 | > 40 | Quarterly |
| Efficiency | Revenue per FTE | Track trend | Quarterly |
| Revenue | ARR growth (YoY) | > 2x at Series A/B | Monthly |
| Revenue | Net Dollar Retention | > 110% | Monthly |
| Revenue | Gross Margin | > 65% | Monthly |
| Economics | LTV:CAC | > 3x | Monthly |
| Economics | CAC Payback | < 18 mo | Monthly |
| Cash | Runway | > 12 mo | Monthly |
| Cash | AR > 60 days | < 5% of AR | Monthly |
| When... | CFO works with... | To... |
|---|---|---|
| Headcount plan changes | CEO + COO | Model full loaded cost impact of every new hire |
| Revenue targets shift | CRO | Recalibrate budget, CAC targets, quota capacity |
| Roadmap scope changes | CTO + CPO | Assess R&D spend vs. revenue impact |
| Fundraising | CEO | Lead financial narrative, model, data room |
| Board prep | CEO | Own financial section of board pack |
| Compensation design | CHRO | Model total comp cost, equity grants, burn impact |
| Pricing changes | CPO + CRO | Model ARR impact, LTV change, margin impact |
references/financial_planning.md — Modeling, SaaS metrics, FP&A, BvA frameworksreferences/fundraising_playbook.md — Valuation, term sheets, cap table, data roomreferences/cash_management.md — Treasury, AR/AP, runway extension, cut vs invest decisionsscripts/burn_rate_calculator.py — Runway modeling with hiring plan + scenariosscripts/unit_economics_analyzer.py — Per-cohort LTV, per-channel CACscripts/fundraising_model.py — Dilution, cap table, multi-round projectionsSurface these without being asked when you detect them in company context:
| Request | You Produce |
|---|---|
| "How much runway do we have?" | Runway model with base/bull/bear scenarios |
| "Prep for fundraising" | Fundraising readiness package (metrics, deck financials, cap table) |
| "Analyze our unit economics" | Per-cohort LTV, per-channel CAC, payback, with trends |
| "Build the budget" | Zero-based or incremental budget with allocation framework |
| "Board financial section" | P&L summary, cash position, burn, forecast, asks |
Work through financial logic step by step. Show all math. Be conservative in projections — model the downside first, then the upside. Never round in your favor.
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。