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Use Go2.gg API for URL shortening, link analytics, QR code generation, webhooks, and link-in-bio pages. Use when the user needs to create short links, track clicks, generate QR codes, set up link-in-bio pages, or manage branded URLs. Free tier includes short links, QR codes, and analytics. Requires GO2GG_API_KEY env var. QR code generation is free without auth.

modbender/skill-library-mcp Agent Skills author: modbender MIT 1 file body ≈ 3 007 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Use Go2.gg API for URL shortening, link analytics, QR code generation, webhooks, and link-in-bio pages. Use when the user needs to create short links, track…

As a process D 48/100 · Unfinished process — weak spots: steps, inputs and preconditions, running it twice

IntegrationYouTubeSoftware developmenttype 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
93/100
safety, quality, tests
Safety 60%
99
Quality 40%
84
Run on models
none yet
Process rating
D
48/100
Unfinished process
Steps w 15
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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Secrets in code secret-password-literal SKILL.md:310
      Hard-coded password / key literal (may be an example) (placeholder value)
      API_KEY = "go2_…ere"  # or os.environ["GO2G…KEY"]
      placeholder

    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 48/100

    • 0Steps. Prose only: no discrete steps
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 13 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3007 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
    • +4Description does not say when NOT to use the skill (false activations)
    • +3No numbered steps or checklist
    • +1No license
    • +2Single-language instructions
    • +3Description length 363: enough signal without eating the budget
    • +4Structure: 21 headings
    • +3Output format is stated explicitly
    • +4Has examples (13 code blocks)

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