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SCF Deep Analysis

Controller-level Statement of Cash Flows deep analysis for QBO-connected clients. Computes CF Quality Ratio, Free Cash Flow, working capital movement drivers...
针对QBO连接客户的现金流量表深度分析。计算现金流质量比率、自由现金流及营运资本变动驱动因素。
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概述

SCF Deep Analysis — SKILL.md

What This Skill Does

Runs a controller-level Statement of Cash Flows deep analysis for a QBO-connected client. Extends the SCF Quick Compare with:

  • CF Quality Ratio: Operating CF ÷ Net Income (>1.0 = cash-backed quality earnings)
  • Free Cash Flow: Operating CF − CapEx (pulled from investing section)
  • Working Capital Movement: AR, AP, Inventory, Prepaid — which WC components drove operating CF changes
  • 3-Month Rolling Averages: Per-account and per-section CF trend baseline
  • GL Drill-Down: Vendor-level transaction detail for every flagged CF account
  • Controller Findings: Plain-English narratives with urgency — "Operating CF decreased 15% because AR increased $8K (collections lagging) while AP decreased $4K (paying faster)"
  • Action Proposals: Specific, actionable recommendations (HIGH / MEDIUM / LOW urgency)
  • CDC: Tracks what changed in CF since last run

Output: Excel workbook with 7 tabs:

Summary | Detail | ⚠ Flags | GL Drill-Down | Working Capital Movement | Controller Findings | CDC Log

When To Use

  • Monthly close: controller-level CF review beyond the Quick Compare
  • Client with CF concerns (declining operating CF, negative FCF, low CF quality)
  • Board prep: need to explain why cash changed this period
  • Internal review: identify AR collection problems, AP timing, capex overruns

When NOT To Use

  • Quick period-over-period CF snapshot → use scf-quick-compare.py instead
  • P&L variance analysis → use pl-deep-analysis.py
  • Budget vs. actual → use budget-vs-actual skill
  • Non-QBO clients (no integration) → use bank-reconciliation skill

Prerequisites

  • QBO client connected for the slug
  • QBO auth token configured
  • openpyxl installed: pip install openpyxl
  • Node.js available on PATH

Script Location

scripts/pipelines/scf-deep-analysis.py

Usage

# Basic: current month vs. prior month (auto-calculated)
python3 scripts/pipelines/scf-deep-analysis.py \
  --slug my-client \
  --current-start 2026-03-01 --current-end 2026-03-31

# Explicit prior period
python3 scripts/pipelines/scf-deep-analysis.py \
  --slug my-client \
  --current-start 2026-02-01 --current-end 2026-02-28 \
  --prior-start 2026-01-01 --prior-end 2026-01-31

# YTD vs prior YTD
python3 scripts/pipelines/scf-deep-analysis.py \
  --slug my-client --ytd --year 2026

# Skip GL drill-down (faster — use when GL is unavailable or not needed)
python3 scripts/pipelines/scf-deep-analysis.py \
  --slug my-client \
  --current-start 2026-03-01 --current-end 2026-03-31 --skip-gl

# Custom output directory
python3 scripts/pipelines/scf-deep-analysis.py \
  --slug glowlabs \
  --current-start 2026-03-01 --current-end 2026-03-31 \
  --out ~/Desktop/reports

# Sandbox mode (QBO sandbox environment)
python3 scripts/pipelines/scf-deep-analysis.py \
  --slug glowlabs \
  --current-start 2026-03-01 --current-end 2026-03-31 --sandbox

Arguments

ArgumentRequiredDescription
---------
--slugCompany slug (must match qbo-client connection)
--current-start✅*Current period start YYYY-MM-DD
--current-end✅*Current period end YYYY-MM-DD
--prior-startPrior period start — auto-calculated if omitted
--prior-endPrior period end — auto-calculated if omitted
--ytdYTD mode: Jan 1 → end of last completed month
--yearYear for --ytd (default: current year)
--skip-glSkip GL drill-down (faster run)
--outOutput directory (default: ~/Desktop)
--sandboxUse QBO sandbox environment

*Required unless --ytd is used.

Pipeline Steps (8 Steps)

  1. Pull CF — current + prior period CF from QBO via report {slug} cf
  2. Pull P&L — current + prior period P&L for Net Income extraction (CF quality ratio)
  3. Rolling Averages — pull 3 prior months of CF; compute per-account and per-section averages
  4. CDC — compare current flat map vs. .cache/scf-deep-analysis/{slug}.json
  5. CF Quality + FCF — compute ratio + extract CapEx from investing section
  6. Working Capital Movement — classify AR / AP / Inventory / Prepaid from operating section
  7. Variance + Flags + GL — flag material variances (≥10% or ≥$2,500); pull GL for flagged accounts
  8. Controller Findings — generate narrative findings with urgency and action proposals

Output: Excel Workbook (7 Tabs)

Tab 1: Summary

  • Section totals: Operating / Investing / Financing / Net Change / Beginning / Ending Cash
  • vs. prior period + 3-month rolling average
  • CF Quality Ratio block (with color coding)
  • Free Cash Flow calculation
  • Controller Findings summary (top 8 findings, HIGH in red)

Tab 2: Detail

  • Every CF line item with prior period, current period, $ variance, % variance, rolling avg
  • Section-grouped with color bands (Operating = blue, Investing = yellow, Financing = purple)
  • F/U column (Favorable = more cash, Unfavorable = less cash)

Tab 3: ⚠ Flags

  • Material variances only: ≥10% change OR ≥$2,500 absolute delta
  • Includes rolling avg comparison
  • Sorted by absolute dollar variance

Tab 4: GL Drill-Down

  • Vendor-level transaction detail for every flagged account
  • Top 3 vendor contributors shown in sub-header per account
  • Max 50 transactions per account
  • Skipped if --skip-gl flag used

Tab 5: Working Capital Movement

  • AR, AP, Inventory, Prepaid, Other WC — current vs. prior vs. delta
  • Cash Impact column (Source / Use)
  • Plain-English analysis note per component (e.g., "AR increased $8K — collections lagging sales")
  • Total WC impact row

Tab 6: Controller Findings

  • Full narrative findings sorted HIGH → MEDIUM → LOW
  • Detail / GL attribution per finding
  • Specific recommended action per finding
  • $ Impact column

Tab 7: CDC Log

  • All accounts/line items that changed vs. last cached run
  • Prior value, current value, $ delta, % change, note (New / Changed / Removed)
  • Color coded: green = cash increased, red = cash decreased

Cache

.cache/scf-deep-analysis/{slug}.json

Stored after each run. Contains:

  • flat_map — all CF line items and amounts
  • totals — section totals (operating, investing, financing, net_change, ending_cash)
  • cf_quality — CF quality label
  • fcf — Free Cash Flow amount
  • net_income — Net Income for the period
  • saved_at — ISO date of last run

Key Metrics Explained

CF Quality Ratio

Operating CF / Net Income
RatioQualityColor
---------
≥ 1.0x✅ Quality Earnings — cash-backedGreen
0.5–1.0x⚠ Adequate — partially cash-backedYellow
< 0.5x⚠ Low Quality — accrual-heavyOrange
< 0🔴 Cash Drain — cash negative despite reported profitRed

Free Cash Flow

FCF = Operating CF + CapEx (CapEx is negative outflow, so this subtracts it)

CapEx detected by keyword matching in investing section (equipment, property, asset, capital expenditure, etc.)

Working Capital Movement (in Operating section)

  • AR change (negative = AR grew = cash used; positive = AR shrank = cash released)
  • AP change (positive = AP grew = cash deferred; negative = AP shrank = cash paid out)
  • Inventory change (negative = inventory built = cash used; positive = inventory drawn = cash released)
  • Prepaid change (negative = more prepaid = cash used; positive = prepaid expensed = cash released)

Materiality Thresholds

  • Percent threshold: ≥10% change in any CF line item
  • Absolute threshold: ≥$2,500 absolute variance
  • Both thresholds checked — either triggers a flag

Safety Rules

  • Read-only: No writes to QBO — only pulls reports
  • All Decimal math: No floating-point for financial calculations
  • Disclaimer footer: Controller Findings tab includes audit disclaimer
  • Cache separation: Uses .cache/scf-deep-analysis/ — separate from scf-quick-compare cache

Related Pipelines

PipelineUse When
------
scf-quick-compare.pyQuick CF period-over-period snapshot (4 tabs, no GL)
scf-deep-analysis.pyController-level CF with GL drill-down, CF quality, FCF, WC movement
pl-deep-analysis.pySame depth for P&L (not CF)
budget-builder.pyBuild annual CF budget / BvA

版本历史

共 1 个版本

  • v1.0.2 当前
    2026-05-02 02:21 安全 安全

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