Portfolio Monitoring

description: Track and analyze portfolio company performance against plan. Ingests monthly/quarterly financial packages (Excel, PDF), extracts KPIs, flags variances to budget, and produces summary dashboards. Use when reviewing portfolio company financials, preparing board materials, or monitoring covenant compliance. Triggers on "review portfolio company", "monthly financials", "how is [company] performing", "covenant check", or "portfolio update".

Published by @w95·0 agent reads / 30d·0 saves·

Portfolio Monitoring

description: Track and analyze portfolio company performance against plan. Ingests monthly/quarterly financial packages (Excel, PDF), extracts KPIs, flags variances to budget, and produces summary dashboards. Use when reviewing portfolio company financials, preparing board materials, or monitoring covenant compliance. Triggers on "review portfolio company", "monthly financials", "how is [company] performing", "covenant check", or "portfolio update".

Workflow

Step 1: Ingest Financial Package

  • Accept the user's portfolio company financial package (Excel workbook, PDF, or CSV)
  • Extract key financials: Revenue, EBITDA, cash balance, debt outstanding, capex, working capital
  • Identify the reporting period and compare to prior period and budget/plan

Step 2: KPI Extraction & Variance Analysis

Key metrics to track (adapt to the company's sector):

Financial KPIs:

  • Revenue vs. budget ($ and %)
  • EBITDA and EBITDA margin vs. budget
  • Cash balance and net debt
  • Leverage ratio (Net Debt / LTM EBITDA)
  • Interest coverage ratio
  • Capex vs. budget
  • Free cash flow

Operational KPIs (ask user or infer from data):

  • Customer count / revenue per customer
  • Employee headcount / revenue per employee
  • Backlog / pipeline
  • Churn / retention rates

Step 3: Flag & Summarize

  • Green: Within 5% of plan
  • Yellow: 5-15% below plan — flag for discussion
  • Red: >15% below plan or covenant breach risk — immediate attention

Output a concise summary:

  1. One-paragraph executive summary ("Company X is tracking [ahead/behind/on] plan...")
  2. KPI table with actual vs. budget vs. prior period
  3. Red/yellow flags with context
  4. Covenant compliance status (if applicable)
  5. Questions for management

Step 4: Trend Analysis

If multiple periods are provided:

  • Chart key metrics over time (revenue, EBITDA, cash)
  • Identify trends — accelerating, decelerating, or stable
  • Compare vs. underwriting case

Important Notes

  • Always ask for the budget/plan to compare against if not provided
  • Don't assume sector-specific KPIs — ask what matters for this company
  • If covenant levels aren't known, ask the user for the credit agreement terms
  • Output should be board-ready — concise, factual, no fluff

More on the bench

SKILL0

Xlsx

Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.

software-engineering+2
0
SKILL0

Supplier Scorecard

Build supplier performance scorecards with KPIs, quality metrics, delivery performance, cost management, and improvement plans

operations+1
0
SKILL0

Statistical Analysis

Apply statistical methods including descriptive stats, trend analysis, outlier detection, and hypothesis testing. Use when analyzing distributions, testing for significance, detecting anomalies, computing correlations, or interpreting statistical results.

data-science-ml+2
0