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BB finance-analytics

Use this skill when the user wants to analyze financial performance, review P&L health, monitor MRR/ARR, investigate margin or cost trends, build a finance report, check cash flow or runway, or answer questions about their accounting/billing data. Triggers include 'how is revenue trending', 'gross margin', 'MRR breakdown', 'churn impact on ARR', 'why did costs go up', 'operating expenses', 'cash runway', 'P&L review', 'EBITDA', 'cost per acquisition trend', 'pull my QuickBooks numbers', 'finance report'.

coupler-io/skills Agent Skills author: coupler-io 1 file body ≈ 3 351 tokens Open the sourcegithub.com↗ analyzed 7 d ago

Use this skill when the user wants to analyze financial performance, review P&L health, monitor MRR/ARR, investigate margin or cost trends, build a finance…

As a process B 70/100 · Nearly there — weak spots: result and completion, inputs and preconditions

AnalyzerStripeData and analyticsFinancetype 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
B
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
84
Run on models
none yet
Process rating
B
70/100
Nearly there
Result and completion w 14
0
Inputs and preconditions w 11
30
Failures and branches w 10
50
the three weakest of ten parameters · all ten

How to improve

    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: 1. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 70/100

    • 0Result and completion. Does not say what the result is
    • 30Inputs and preconditions. Does not say what the process needs to start
    • 50Failures and branches. 0 branches, has a failure section
    • 70When it triggers. States when to use, but not when not to
    • 100Tools and files. No external tools needed
    • 100Steps. 38 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3351 tokens
    • 100Running it twice. Mutating operations check current state
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 509: enough signal without eating the budget
    • +4Structure: 21 headings
    • +3Step-by-step instructions: 38 items
    • +4Has examples (1 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.