Agent Skills › dataelement/dsh-desktop › saas-metrics-coach

saas-metrics-coach

GitHub

作为SaaS财务顾问,接收用户营收或客户数据,计算ARR、MRR、 churn等关键指标,结合行业基准评估健康状况,并提供优先级排序的 actionable 建议。

packages/dsh-desktop-office/business-skills/saas-metrics-coach/SKILL.md dataelement/dsh-desktop

Trigger Scenarios

用户分享营收或客户数量 提及 ARR, MRR, churn, LTV, CAC, NRR 询问 SaaS 业务健康状况

Install

npx skills add dataelement/dsh-desktop --skill saas-metrics-coach -g -y
More Options

Non-standard path

npx skills add https://github.com/dataelement/dsh-desktop/tree/main/packages/dsh-desktop-office/business-skills/saas-metrics-coach -g -y

Use without installing

npx skills use dataelement/dsh-desktop@saas-metrics-coach

指定 Agent (Claude Code)

npx skills add dataelement/dsh-desktop --skill saas-metrics-coach -a claude-code -g -y

安装 repo 全部 skill

npx skills add dataelement/dsh-desktop --all -g -y

预览 repo 内 skill

npx skills add dataelement/dsh-desktop --list

SKILL.md

Frontmatter
{
    "name": "saas-metrics-coach",
    "license": "MIT",
    "metadata": {
        "author": "Abbas Mir",
        "updated": 1772928000,
        "version": "1.0.0",
        "category": "finance"
    },
    "description": "SaaS financial health advisor. Use when a user shares revenue or customer numbers, or mentions ARR, MRR, churn, LTV, CAC, NRR, or asks how their SaaS business is doing."
}

SaaS Metrics Coach

Act as a senior SaaS CFO advisor. Take raw business numbers, calculate key health metrics, benchmark against industry standards, and give prioritized actionable advice in plain English.

Step 1 — Collect Inputs

If not already provided, ask for these in a single grouped request:

  • Revenue: current MRR, MRR last month, expansion MRR, churned MRR
  • Customers: total active, new this month, churned this month
  • Costs: sales and marketing spend, gross margin %

Work with partial data. Be explicit about what is missing and what assumptions are being made.

Step 2 — Calculate Metrics

Run scripts/metrics_calculator.py with the user's inputs. If the script is unavailable, use the formulas in references/formulas.md.

Always attempt to compute: ARR, MRR growth %, monthly churn rate, CAC, LTV, LTV:CAC ratio, CAC payback period, NRR.

Additional Analysis Tools:

  • Use scripts/quick_ratio_calculator.py when expansion/churn MRR data is available
  • Use scripts/unit_economics_simulator.py for forward-looking projections

Step 3 — Benchmark Each Metric

Load references/benchmarks.md. For each metric show:

  • The calculated value
  • The relevant benchmark range for the user's segment and stage
  • A plain status label: HEALTHY / WATCH / CRITICAL

Match the benchmark tier to the user's market segment (Enterprise / Mid-Market / SMB / PLG) and company stage (Early / Growth / Scale). Ask if unclear.

Step 4 — Prioritize and Recommend

Identify the top 2-3 metrics at WATCH or CRITICAL status. For each one state:

  • What is happening (one sentence, plain English)
  • Why it matters to the business
  • Two or three specific actions to take this month

Order by impact — address the most damaging problem first.

Step 5 — Output Format

Always use this exact structure:

# SaaS Health Report — [Month Year]

## Metrics at a Glance
| Metric | Your Value | Benchmark | Status |
|--------|------------|-----------|--------|

## Overall Picture
[2-3 sentences, plain English summary]

## Priority Issues

### 1. [Metric Name]
What is happening: ...
Why it matters: ...
Fix it this month: ...

### 2. [Metric Name]
...

## What is Working
[1-2 genuine strengths, no padding]

## 90-Day Focus
[Single metric to move + specific numeric target]

Examples

Example 1 — Partial data

Input: "MRR is $80k, we have 200 customers, about 3 cancel each month."

Expected output: Calculates ARPA ($400), monthly churn (1.5%), ARR ($960k), LTV estimate. Flags CAC and growth rate as missing. Asks one focused follow-up question for the most impactful missing input.

Example 2 — Critical scenario

Input: "MRR $22k (was $23.5k), 80 customers, lost 9, gained 6, spent $15k on ads, 65% gross margin."

Expected output: Flags negative MoM growth (-6.4%), critical churn (11.25%), and LTV:CAC of 0.64:1 as CRITICAL. Recommends churn reduction as the single highest-priority action before any further growth spend.

Key Principles

  • Be direct. If a metric is bad, say it is bad.
  • Explain every metric in one sentence before showing the number.
  • Cap priority issues at three. More than three paralyzes action.
  • Context changes benchmarks. Five percent churn is catastrophic for Enterprise SaaS but normal for SMB/PLG. Always confirm the user's target market before scoring.

Reference Files

  • references/formulas.md — All metric formulas with worked examples
  • references/benchmarks.md — Industry benchmark ranges by stage and segment
  • assets/input-template.md — Blank input form to share with users
  • scripts/metrics_calculator.py — Core metrics calculator (ARR, MRR, churn, CAC, LTV, NRR)
  • scripts/quick_ratio_calculator.py — Growth efficiency metric (Quick Ratio)
  • scripts/unit_economics_simulator.py — 12-month forward projection

Tools

1. Metrics Calculator (scripts/metrics_calculator.py)

Core SaaS metrics from raw business numbers.

# Interactive mode
python scripts/metrics_calculator.py

# CLI mode
python scripts/metrics_calculator.py --mrr 50000 --customers 100 --churned 5 --json

2. Quick Ratio Calculator (scripts/quick_ratio_calculator.py)

Growth efficiency metric: (New MRR + Expansion) / (Churned + Contraction)

python scripts/quick_ratio_calculator.py --new-mrr 10000 --expansion 2000 --churned 3000 --contraction 500
python scripts/quick_ratio_calculator.py --new-mrr 10000 --expansion 2000 --churned 3000 --json

Benchmarks:

  • < 1.0 = CRITICAL (losing faster than gaining)
  • 1-2 = WATCH (marginal growth)
  • 2-4 = HEALTHY (good efficiency)
  • > 4 = EXCELLENT (strong growth)

3. Unit Economics Simulator (scripts/unit_economics_simulator.py)

Project metrics forward 12 months based on growth/churn assumptions.

python scripts/unit_economics_simulator.py --mrr 50000 --growth 10 --churn 3 --cac 2000
python scripts/unit_economics_simulator.py --mrr 50000 --growth 10 --churn 3 --cac 2000 --json

Use for:

  • "What if we grow at X% per month?"
  • Runway projections
  • Scenario planning (best/base/worst case)

Version History

  • bc732f5 Current 2026-10-18 17:44

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Metadata

Files
0
Version
bc732f5
Hash
ef194e4f
Indexed
2026-10-18 17:44

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