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CB saas-revenue-growth-metrics

Calculate SaaS revenue, retention, and growth metrics. Use when diagnosing momentum, churn, expansion, or product-market-fit signals.

deanpeters/Product-Manager-Skills Claude Code author: deanpeters NOASSERTION 4 files body ≈ 5 515 tokens Open the sourcegithub.com↗ analyzed 4 d ago

Calculate SaaS revenue, retention, and growth metrics.

As a process B 66/100 · Nearly there — weak spots: result and completion, running it twice, progress reporting

AnalyzerData and analyticstype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
C
89/100
safety, quality, tests
Safety 60%
100
Quality 40%
73
Run on models
none yet
Process rating
B
66/100
Nearly there
Result and completion w 14
0
Progress reporting w 2
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 0

✓ No critical or high findings

Files scanned: 4. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 5515 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "intent"
  • note frontmatter-key unknown frontmatter key "type"
  • note frontmatter-key unknown frontmatter key "theme"
  • note frontmatter-key unknown frontmatter key "best_for"
  • note frontmatter-key unknown frontmatter key "scenarios"
  • note frontmatter-key unknown frontmatter key "estimated_time"
  • note edit-residue the text marks something as outdated (lines 501, 505, 510): check that old rules are not kept next to new ones — the full check reads the text for contradictions

Process rating: all ten parameters 66/100

  • 0Result and completion. Does not say what the result is
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 14 mutating operations with no state check
  • 50When it triggers. No condition that starts the skill
  • 55Failures and branches. 1 branches
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 70Execution cost. Instruction body is 5515 tokens
  • 100Tools and files. No external tools needed
  • 100Steps. 210 steps
  • 100Consistency. Name and required fields are in place

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +3Output format is not stated: the model decides each time
  • -223 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +4Description says when NOT to use the skill
  • +3Description length 133: enough signal without eating the budget
  • +4Structure: 45 headings
  • +3Step-by-step instructions: 210 items
  • +4Has examples (12 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 73.