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AC invoice-generator

Generate a professional invoice from natural language or structured input. USE WHEN: - User asks to create an invoice, bill, or payment request - User provides line items, client details, or says "invoice [client] for [work]" - User wants a receipt or proof of payment document DON'T USE WHEN: - User wants a quote, estimate, or proposal (different document structure and intent) - User wants to track invoices, manage accounts receivable, or build an invoicing system - User wants a contract or agreement (use a contract/legal template instead) - User wants to send an invoice via email (this generates the document; sending is a separate step) - User wants a recurring billing setup or subscription management OUTPUTS: Single self-contained .html file, print-ready. User opens in browser → Print → Save as PDF. Clean, professional layout. INPUTS: Sender info, client info, line items (description + qty + price). Optional: invoice number, dates, tax rate, notes, payment terms.

modbender/skill-library-mcp Agent Skills author: modbender MIT 2 files body ≈ 506 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Generate a professional invoice from natural language or structured input.

As a process C 55/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency

GeneratorFinancetype 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
A
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
81
Run on models
none yet
Process rating
C
55/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
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: 2. 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 55/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 5 mutating operations with no state check
    • 40Consistency. Frontmatter name (invoice-generator) differs from the folder (nova-invoice-generator)
    • 50When it triggers. No condition that starts the skill
    • 55Failures and branches. 1 branches
    • 100Tools and files. No external tools needed
    • 100Steps. 23 steps
    • 100Execution cost. Instruction body is 506 tokens

    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
    • +3Description length 983: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +4Structure: 6 headings
    • +3Step-by-step instructions: 23 items

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.