SKILLEMALL.ai
GitHub ↗ RU Sign in

BC stripe_analytics

Complete Stripe analytics dashboard for SaaS founders (READ-ONLY). CORE METRICS: MRR, churn (revenue/customer), NRR, LTV/CAC CUSTOMER: Top LTV, segments (plan/geo/industry), health scores REVENUE: Expansion/contraction, failed payment recovery FORECAST: MRR projection (1-12 months) INSIGHTS: Actionable alerts + opportunities Usage: "Stripe metrics", "top customers", "churn risks", "90d revenue" REQUIRES: STRIPE_READ_KEY (restricted: read:customers/subscriptions/invoices/payment_intents) TIMEOUT: 8s | PARALLEL: 5 API calls | CACHE: 1hr recommended (caller-side; not built-in)

ClawHub Agent Skills author: Tomas Mikula v1.0.2 MIT-0 4 files body ≈ 1 067 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 59/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, consistency

IntegrationStripeData and analyticstype and topics are labelled automatically from the skill text
Runs in: OpenClaw 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) Goose OpenCode
JSON
Technical rating
B
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
70
Run on models
none yet
Process rating
C
59/100
Has gaps
Inputs and preconditions w 11
0
Progress reporting w 2
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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 name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "web"
  • note frontmatter-key unknown frontmatter key "install"
  • note frontmatter-key unknown frontmatter key "env"
  • note frontmatter-key unknown frontmatter key "gating"
  • note frontmatter-key unknown frontmatter key "slash-commands"
  • note frontmatter-key unknown frontmatter key "categories"

Process rating: all ten parameters 59/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 2 mutating operations with no state check
  • 40Consistency. Frontmatter name (stripe_analytics) differs from the folder (stripe-analytics)
  • 50Failures and branches. 0 branches, has a failure section
  • 60Result and completion. Output format stated, no completion criterion
  • 100Tools and files. No external tools needed
  • 100Steps. 21 steps
  • 100Execution cost. Instruction body is 1067 tokens
  • low 12 top-level sections: this looks like several domains in one skill

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

  • +4Description does not say when NOT to use the skill (false activations)
  • -213 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 4 example trigger phrases
  • +3Description length 583: enough signal without eating the budget
  • +4Structure: 18 headings
  • +3Step-by-step instructions: 21 items
  • +3Output format is stated explicitly
  • +4Has examples (10 code blocks)

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

External checks

ClawHub: suspicious
This skill is a read-only Stripe analytics tool, but it can expose sensitive revenue and customer data through broad text and voice triggers.
LLM: suspicious (high) · VirusTotal: · 29 May 2026