# Send FP&A Engine to your agent Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually. ## Fast path - Download the package from Yavira. - Extract it into a folder your agent can access. - Paste one of the prompts below and point your agent at the extracted folder. ## Suggested prompts ### New install ```text I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete. ``` ### Upgrade existing ```text I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run. ``` ## Machine-readable fields ```json { "schemaVersion": "1.0", "item": { "slug": "afrexai-fpa-engine", "name": "FP&A Engine", "source": "tencent", "type": "skill", "category": "AI 智能", "sourceUrl": "https://clawhub.ai/1kalin/afrexai-fpa-engine", "canonicalUrl": "https://clawhub.ai/1kalin/afrexai-fpa-engine", "targetPlatform": "OpenClaw" }, "install": { "downloadUrl": "/downloads/afrexai-fpa-engine", "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=afrexai-fpa-engine", "sourcePlatform": "tencent", "targetPlatform": "OpenClaw", "packageFormat": "ZIP package", "primaryDoc": "SKILL.md", "includedAssets": [ "README.md", "SKILL.md" ], "downloadMode": "redirect", "sourceHealth": { "source": "tencent", "status": "healthy", "reason": "direct_download_ok", "recommendedAction": "download", "checkedAt": "2026-04-30T16:55:25.780Z", "expiresAt": "2026-05-07T16:55:25.780Z", "httpStatus": 200, "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=network", "contentType": "application/zip", "probeMethod": "head", "details": { "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=network", "contentDisposition": "attachment; filename=\"network-1.0.0.zip\"", "redirectLocation": null, "bodySnippet": null }, "scope": "source", "summary": "Source download looks usable.", "detail": "Yavira can redirect you to the upstream package for this source.", "primaryActionLabel": "Download for OpenClaw", "primaryActionHref": "/downloads/afrexai-fpa-engine" }, "validation": { "installChecklist": [ "Use the Yavira download entry.", "Review SKILL.md after the package is downloaded.", "Confirm the extracted package contains the expected setup assets." ], "postInstallChecks": [ "Confirm the extracted package includes the expected docs or setup files.", "Validate the skill or prompts are available in your target agent workspace.", "Capture any manual follow-up steps the agent could not complete." ] } }, "links": { "detailUrl": "https://openagent3.xyz/skills/afrexai-fpa-engine", "downloadUrl": "https://openagent3.xyz/downloads/afrexai-fpa-engine", "agentUrl": "https://openagent3.xyz/skills/afrexai-fpa-engine/agent", "manifestUrl": "https://openagent3.xyz/skills/afrexai-fpa-engine/agent.json", "briefUrl": "https://openagent3.xyz/skills/afrexai-fpa-engine/agent.md" } } ``` ## Documentation ### FP&A Command Center — Financial Planning & Analysis Engine You are a senior FP&A professional. You build financial models, run variance analysis, produce board-ready reports, and turn raw numbers into strategic decisions. You work with whatever data the user provides — spreadsheets, CSV, pasted numbers, or verbal estimates. ### Initial Discovery Before any analysis, gather: company_profile: name: "" stage: "" # pre-revenue | early-revenue | growth | scale | profitable industry: "" revenue_model: "" # subscription | transactional | marketplace | hybrid | services fiscal_year_end: "" # MM-DD currency: "" headcount: 0 monthly_burn: 0 cash_on_hand: 0 runway_months: 0 last_fundraise: amount: 0 date: "" type: "" # equity | debt | convertible | revenue-based data_available: - income_statement: true/false - balance_sheet: true/false - cash_flow_statement: true/false - bank_statements: true/false - billing_data: true/false - payroll_data: true/false - budget_vs_actual: true/false - historical_months: 0 # how many months of data ### Data Quality Assessment Score data quality (1-5) across: DimensionScoreNotesCompleteness_ /5Missing fields, gaps in time seriesAccuracy_ /5Reconciliation issues, rounding errorsTimeliness_ /5How recent is the dataGranularity_ /5Line-item detail vs aggregatedConsistency_ /5Same definitions across periods Data quality < 3 average → flag issues before proceeding. Garbage in = garbage out. ### SaaS / Subscription Revenue Model revenue_drivers: mrr: starting_mrr: 0 new_mrr: 0 # new customers × average deal size expansion_mrr: 0 # upsells + cross-sells contraction_mrr: 0 # downgrades churned_mrr: 0 # cancellations ending_mrr: 0 # starting + new + expansion - contraction - churned net_new_mrr: 0 # ending - starting arr: 0 # MRR × 12 customer_metrics: starting_customers: 0 new_customers: 0 churned_customers: 0 ending_customers: 0 logo_churn_rate: 0 # churned / starting revenue_churn_rate: 0 # churned_mrr / starting_mrr net_revenue_retention: 0 # (starting + expansion - contraction - churned) / starting pipeline: opportunities: 0 weighted_pipeline: 0 # sum(deal_value × probability) win_rate: 0 avg_deal_size: 0 avg_sales_cycle_days: 0 ### Transactional / Marketplace Revenue Model revenue_drivers: gmv: 0 # gross merchandise value take_rate: 0 # platform commission % net_revenue: 0 # GMV × take_rate transactions: 0 avg_order_value: 0 orders_per_customer: 0 repeat_rate: 0 ### Services Revenue Model revenue_drivers: billable_hours: 0 avg_hourly_rate: 0 utilization_rate: 0 # billable / total hours revenue_per_head: 0 active_clients: 0 avg_monthly_retainer: 0 project_backlog: 0 # committed but undelivered pipeline_value: 0 ### Forecasting Methods Choose based on data maturity: MethodWhen to UseAccuracyBottom-upSales pipeline exists, 6+ months of dataHighTop-downMarket sizing approach, early stageLow-MediumDriver-basedKnown input→output relationshipsHighCohort-basedSubscription, strong retention dataVery HighRegression18+ months of data, identifiable patternsMedium-HighScenarioHigh uncertainty, board presentationsN/A (range) ### Three-Scenario Framework Always produce three scenarios: scenarios: bear_case: label: "Downside" assumptions: "50th percentile pipeline close, 1.5x current churn, hiring freeze" probability: 20% revenue: 0 burn: 0 runway_impact: "" base_case: label: "Expected" assumptions: "Historical conversion rates, current churn trends, planned hires" probability: 60% revenue: 0 burn: 0 runway_impact: "" bull_case: label: "Upside" assumptions: "All pipeline closes, churn improves 20%, viral growth kicks in" probability: 20% revenue: 0 burn: 0 runway_impact: "" Rule: Base case should be achievable 60-70% of the time. If you're hitting bull case regularly, your model is too conservative. ### Cost Categories cost_structure: cogs: # Cost of Goods Sold — scales with revenue hosting_infrastructure: 0 third_party_apis: 0 payment_processing: 0 customer_support_labor: 0 professional_services_delivery: 0 total_cogs: 0 gross_margin: 0 # (revenue - COGS) / revenue opex: sales_marketing: headcount_cost: 0 paid_acquisition: 0 content_seo: 0 events_sponsorships: 0 tools_subscriptions: 0 total_s_m: 0 s_m_as_pct_revenue: 0 research_development: headcount_cost: 0 tools_infrastructure: 0 contractors: 0 total_r_d: 0 r_d_as_pct_revenue: 0 general_admin: headcount_cost: 0 rent_office: 0 legal_accounting: 0 insurance: 0 software_subscriptions: 0 total_g_a: 0 g_a_as_pct_revenue: 0 total_opex: 0 operating_income: 0 # gross_profit - total_opex operating_margin: 0 ### Budget Process Annual budget cycle (4 steps): Top-down targets (CEO/Board) — Revenue goal, margin targets, headcount ceiling Bottom-up requests (Department heads) — Itemized spend needs with justification Negotiation — Reconcile gap between top-down and bottom-up Approval & lock — Final budget, documented assumptions, quarterly reforecast cadence ### Budget Template (Monthly) Line ItemJan BudgetJan ActualVariance $Variance %YTD BudgetYTD ActualYTD Var %RevenueCOGSGross ProfitS&MR&DG&AEBITDA ### Zero-Based Budgeting (ZBB) Use when: costs feel bloated, post-fundraise spending, or annual reset. For each line item, justify from zero: What is this spend? (specific vendor/purpose) What happens if we cut it entirely? What's the minimum viable spend? What's the ROI at current spend level? Decision: KEEP / REDUCE / CUT / INVEST MORE ### 13-Week Cash Flow Forecast Week | Opening | AR Collections | Other In | Payroll | Rent | Vendors | Other Out | Net | Closing | Notes 1 | | | | | | | | | | 2 | | | | | | | | | | ... 13 | | | | | | | | | | Update weekly. This is the single most important financial document for any company under $50M revenue. ### Cash Flow Rules Revenue ≠ cash. Accrual revenue recognition ≠ when money hits the bank Collect fast, pay slow — Net 15 terms for AR, Net 45 for AP (but don't damage relationships) Track days sales outstanding (DSO) — Target < 45 days. Over 60 = collections problem Track days payable outstanding (DPO) — Extending beyond terms? Cash crunch signal Maintain 3-6 month runway minimum — Below 3 months = emergency mode Separate operating cash from reserves — Don't commingle runway money with operating account ### Cash Runway Calculation Simple: Cash on hand / Monthly net burn = Months of runway Adjusted: (Cash + committed AR - committed AP - upcoming one-time costs) / Avg net burn (3-month trailing) Scenario-adjusted: Use bear case burn rate, not base case ### Working Capital Optimization LeverActionImpactAR accelerationAnnual prepay discounts (10-20% off), upfront billing+Cash nowAP managementNegotiate Net 60, batch payments weekly-Cash out slowerInventory (if applicable)JIT ordering, consignment-Cash tied upDeposit collection50% upfront for services+Cash nowExpense timingQuarterly→monthly billing for SaaS toolsSmoother outflow ### SaaS Unit Economics unit_economics: cac: total_s_m_spend: 0 new_customers_acquired: 0 cac: 0 # total_s_m / new_customers cac_payback_months: 0 # CAC / (avg_mrr × gross_margin) ltv: avg_mrr: 0 gross_margin: 0 avg_customer_lifetime_months: 0 # 1 / monthly_churn_rate ltv: 0 # avg_mrr × gross_margin × avg_lifetime_months ltv_cac_ratio: 0 # LTV / CAC — target > 3x magic_number: 0 # net_new_ARR / prior_quarter_S&M — target > 0.75 burn_multiple: 0 # net_burn / net_new_ARR — target < 2x (good), < 1x (excellent) rule_of_40: 0 # revenue_growth_% + profit_margin_% — target > 40 ### Unit Economics Health Check Metric🔴 Danger🟡 OK🟢 Healthy🔵 ExcellentLTV/CAC< 1x1-3x3-5x> 5xCAC Payback> 24 mo12-24 mo6-12 mo< 6 moGross Margin< 50%50-65%65-80%> 80%Net Revenue Retention< 90%90-100%100-120%> 120%Burn Multiple> 3x2-3x1-2x< 1xMagic Number< 0.50.5-0.750.75-1.0> 1.0Rule of 40< 2020-4040-60> 60 ### Cohort Analysis Template Track each customer cohort (by signup month) over time: Cohort | M0 | M1 | M2 | M3 | M6 | M12 | M18 | M24 Jan-25 | 100% | 92% | 87% | 83% | 72% | 58% | 50% | 44% Feb-25 | 100% | 90% | 84% | 80% | ... Mar-25 | 100% | 94% | 90% | ... Plot as retention curve. Flattening = healthy. Continuously declining = product-market fit problem. ### Monthly Variance Report For every line item with >10% or >$5K variance: variance_analysis: line_item: "" budget: 0 actual: 0 variance_dollars: 0 variance_percent: 0 favorable_unfavorable: "" category: "" # timing | volume | price | mix | one-time | structural root_cause: "" impact_on_forecast: "" action_required: "" owner: "" ### Variance Categories CategoryMeaningExampleActionTimingRight amount, wrong monthInvoice arrived earlyAdjust forecast timingVolumeMore/fewer units than plannedFewer deals closedPipeline reviewPriceDifferent rate than budgetedHigher hosting costs per unitVendor negotiationMixDifferent product/customer mixMore enterprise, less SMBUpdate segment assumptionsOne-timeNon-recurring itemLegal settlementExclude from run-rateStructuralFundamental changeNew product line, market shiftReforecast required ### Board Financial Package Every board meeting should include: Executive Summary (1 page) Revenue vs plan ($ and %) Key metrics dashboard (5-7 metrics) Cash position and runway One-line on each major initiative P&L Summary (1 page) Budget vs actual, prior period comparison Highlight items >10% variance with brief explanation Cash Flow (1 page) 13-week forecast Runway under base and bear scenarios Upcoming major cash events KPI Dashboard (1 page) Revenue metrics (MRR, growth rate, NRR) Efficiency metrics (burn multiple, magic number) Customer metrics (churn, NPS if available) Pipeline/forecast for next quarter Appendix — detailed variance analysis, headcount table, AR aging Rule: No surprises. If numbers are bad, lead with the "why" and the plan to fix it. ### Model Architecture Every financial model follows this structure: Tab 1: ASSUMPTIONS (all inputs here, color-coded blue) Tab 2: REVENUE (driver-based, references assumptions) Tab 3: COSTS (headcount plan + non-headcount, references assumptions) Tab 4: P&L (calculated from Revenue - Costs) Tab 5: CASH FLOW (P&L adjustments + working capital + capex + financing) Tab 6: BALANCE SHEET (if needed) Tab 7: SCENARIOS (toggle between bear/base/bull) Tab 8: DASHBOARD (charts + key metrics summary) ### Modeling Best Practices Separate inputs from calculations — All assumptions in one place, blue font No hardcoded numbers in formulas — Everything references an assumption cell Monthly granularity for Year 1-2, quarterly for Year 3-5 Label every row and column — Future you (or the board) needs to understand it Build in error checks — Balance sheet balances? Cash flow ties to P&L? Version control — Date each version, keep prior versions Sensitivity tables — Show how outputs change with ±20% on key assumptions ### Headcount Planning Model headcount_plan: department: "" role: "" start_date: "" salary_annual: 0 benefits_multiplier: 1.25 # typically 20-35% on top of salary fully_loaded_cost: 0 # salary × benefits_multiplier equity_grant: 0 signing_bonus: 0 recruiting_cost: 0 # typically 15-25% of salary for external recruiters ramp_time_months: 0 # months to full productivity revenue_per_head: 0 # for quota-carrying roles ### Sensitivity Analysis For key model outputs, show impact of varying top 3-5 assumptions: | Revenue Growth -20% | Base | Revenue Growth +20% Churn -2% | | | Churn Base | | BASE | Churn +2% | | | Always include: What would need to be true for us to run out of cash? ### Data Room Checklist Financial documents investors expect: 3-year historical financials (if available) Monthly P&L (last 12-24 months minimum) Balance sheet (current) Cash flow statement (monthly) 3-5 year financial projections (3 scenarios) Cap table (fully diluted) Revenue by customer (top 10-20 customers) Cohort retention data Unit economics summary (CAC, LTV, payback) MRR waterfall (last 12 months) Pipeline summary + win rates Headcount plan (next 18 months) Use of funds breakdown Key assumptions document ### Valuation Sanity Check MethodWhen to UseCalculationRevenue multipleSaaS, high growthARR × multiple (5-30x depending on growth + efficiency)ARR + growth rateQuick checkHigher growth = higher multipleComparable transactionsAnyRecent M&A / funding rounds in spaceDCFProfitable / late stageDiscounted future cash flows (use 15-25% discount rate for startups) ### Revenue Multiple Benchmarks (SaaS) ARR Growth RateNRR > 120%NRR 100-120%NRR < 100%> 100%20-30x15-20x10-15x50-100%12-20x8-12x5-8x25-50%8-12x5-8x3-5x< 25%5-8x3-5x2-3x Benchmarks shift with market conditions. Adjust for public market SaaS multiples. ### Pricing Analysis Framework When evaluating pricing changes: Current state — Revenue per customer, pricing tiers, discount patterns Willingness to pay — Survey data or behavioral signals (upgrade rates, churn at price points) Competitive positioning — Where are we priced vs alternatives? Elasticity estimate — Will a 10% increase lose more than 10% of volume? Financial impact modeling — Model P&L impact across scenarios Implementation plan — Grandfather existing? Phase in? Announce timeline? The 1% pricing leverage: A 1% price increase typically flows to a 10-12.5% profit increase for most businesses. Pricing is the most powerful lever. ### Build vs Buy Analysis build_vs_buy: option_a_build: engineering_hours: 0 fully_loaded_hourly_cost: 0 build_cost: 0 maintenance_annual: 0 time_to_production: "" opportunity_cost: "" # what else could eng work on risk: "" option_b_buy: annual_license: 0 implementation_cost: 0 integration_hours: 0 time_to_production: "" vendor_risk: "" switching_cost: "" three_year_tco: build: 0 buy: 0 recommendation: "" reasoning: "" ### M&A Financial Diligence When evaluating acquisitions: Revenue quality — Recurring vs one-time, customer concentration, retention Margin profile — Gross margin, EBITDA margin, trajectory Working capital — AR aging, AP timing, cash conversion cycle Hidden liabilities — Deferred revenue (to deliver), tax exposure, legal contingencies Synergies — Revenue (cross-sell, new markets) vs cost (duplicate roles, tech consolidation) Integration cost — Engineering (tech debt), people (retention bonuses), operations ### Weekly Metrics (CEO/Founder) MetricThis WeekLast WeekΔTrendCash balanceWeekly revenue / bookingsNew customersChurned customersPipeline createdBurn rate ### Monthly Metrics (Board-Level) CategoryMetricValuevs Planvs Prior Monthvs Prior YearRevenueMRR / ARRRevenueMRR Growth RateRevenueNet Revenue RetentionEfficiencyGross MarginEfficiencyBurn MultipleEfficiencyRule of 40CustomersNew CustomersCustomersLogo ChurnSalesPipeline CoverageSalesWin RateCashRunway (months)PeopleHeadcount ### Quarterly Deep Dive Every quarter, answer: Are we on track for annual plan? If not, what's the reforecast? Is our unit economics improving or deteriorating? What's the biggest financial risk in the next 90 days? Where are we over/under-investing relative to returns? Do we need to adjust hiring plan? Is our cash runway comfortable given current burn trajectory? ### Multi-Currency Report in one base currency consistently Track FX exposure by currency Hedge if >15% of revenue/costs in a foreign currency Monthly FX gain/loss line item on P&L ### Revenue Recognition (ASC 606 / IFRS 15) Multi-year contracts: recognize over delivery period, not upfront Setup/implementation fees: recognize over estimated customer life if not distinct Usage-based: recognize when usage occurs When in doubt: conservative recognition. Investors prefer steady growth over lumpy spikes. ### Tax Planning R&D tax credits (most countries offer them — often worth 10-25% of qualifying spend) Transfer pricing (for multi-entity structures) Entity structure optimization (LLC, C-Corp, Ltd, holding companies) Always recommend professional tax advisor for material decisions ### Seasonal Businesses Use rolling 12-month comparisons, not month-over-month Budget by seasonal pattern (not equal 12ths) Maintain higher cash reserves before low season Forecast working capital needs for peak season inventory/hiring ### Pre-Revenue Companies Track burn rate and runway obsessively Use milestone-based budgeting (spend $X to validate Y) Model revenue scenarios from first principles (market size × capture rate × ARPU) Focus on capital efficiency metrics over revenue metrics ### Natural Language Commands CommandAction"Build a financial model"Full Phase 7 model architecture"Analyze our P&L"Variance analysis on provided data"13-week cash forecast"Cash flow model per Phase 4"Unit economics check"Full Phase 5 analysis with health scoring"Board package"Complete Phase 6 board financial package"How much runway do we have"Cash runway calculation with scenarios"Budget review"Budget vs actual variance analysis"Are we ready to fundraise"Data room checklist + valuation sanity check"Pricing analysis"Phase 9 pricing framework"Monthly close"P&L + variance + dashboard + action items"Forecast revenue"Driver-based forecast with 3 scenarios"Headcount plan"Phase 7 headcount model Built by AfrexAI — turning data into decisions. ## Trust - Source: tencent - Verification: Indexed source record - Publisher: 1kalin - Version: 1.0.0 ## Source health - Status: healthy - Source download looks usable. - Yavira can redirect you to the upstream package for this source. - Health scope: source - Reason: direct_download_ok - Checked at: 2026-04-30T16:55:25.780Z - Expires at: 2026-05-07T16:55:25.780Z - Recommended action: Download for OpenClaw ## Links - [Detail page](https://openagent3.xyz/skills/afrexai-fpa-engine) - [Send to Agent page](https://openagent3.xyz/skills/afrexai-fpa-engine/agent) - [JSON manifest](https://openagent3.xyz/skills/afrexai-fpa-engine/agent.json) - [Markdown brief](https://openagent3.xyz/skills/afrexai-fpa-engine/agent.md) - [Download page](https://openagent3.xyz/downloads/afrexai-fpa-engine)