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AA postnitro-carousel

Generate professional social media carousel posts using the PostNitro.ai Embed API. Supports AI-powered content generation and manual content import for LinkedIn, Instagram, TikTok, and X (Twitter) carousels. Use this skill whenever the user wants to create a carousel, social media post, slide deck for social media, multi-slide content, or mentions PostNitro. Also trigger when the user asks to turn text, articles, blog posts, or topics into carousel posts, or wants to automate social media content creation. Outputs PNG images or PDF files. Requires a PostNitro API key.

modbender/skill-library-mcp Agent Skills author: modbender MIT 1 file body ≈ 4 120 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Generate professional social media carousel posts using the PostNitro.ai Embed API. Supports AI-powered content generation and manual content import for…

As a process A 81/100 · Runs to the end — weak spots: running it twice, progress reporting

GeneratorPDFSupabaseMarketingWriting and documentstype 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
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
87
Run on models
none yet
Process rating
A
81/100
Runs to the end
Progress reporting w 2
0
Running it twice w 4
30
Result and completion w 14
60
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 81/100

    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 4 mutating operations with no state check
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 70Execution cost. Instruction body is 4120 tokens
    • 100Tools and files. No external tools needed
    • 100Steps. 43 steps
    • 100Failures and branches. 2 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • low 10 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

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +1No license
    • +2Single-language instructions
    • +3Description length 575: enough signal without eating the budget
    • +4Structure: 30 headings
    • +3Step-by-step instructions: 43 items
    • +3Output format is stated explicitly
    • +4Has examples (11 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.