SKILLEMALL.ai
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AF opengraph-io

Extract web data, capture screenshots, scrape content, and generate AI images via OpenGraph.io. Use when working with URLs (unfurling, previews, metadata), capturing webpage screenshots, scraping HTML content, asking questions about webpages, or generating images (diagrams, icons, social cards, QR codes). Triggers: 'get the OG tags', 'screenshot this page', 'scrape this URL', 'generate a diagram', 'create a social card', 'what does this page say about'.

modbender/skill-library-mcp Agent Skills author: modbender MIT 9 files body ≈ 2 289 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Extract web data, capture screenshots, scrape content, and generate AI images via OpenGraph.io. Use when working with URLs (unfurling, previews, metadata)…

As a process F 33/100 · Will not run — weak spots: result and completion, when it triggers, inputs and preconditions

GeneratorSoftware developmentAI and agentstype 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
94/100
safety, quality, tests
Safety 60%
99
Quality 40%
87
Run on models
none yet
Process rating
F
33/100
Will not run
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
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 Risky intent intent-offensive-security examples/EXAMPLES.md:85
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (test fixture / example file; quoted — discussed, not commanded)
      "title": "SecureCoders - Expert Cybersecurity Services & Penetration Testing",
      fixturequoted

    Files scanned: 9. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "homepage"

    Process rating: all ten parameters 33/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 3 mutating operations with no state check
    • 40Consistency. Frontmatter name (opengraph-io) differs from the folder (opengraph-io-skill)
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 60Steps. 11 steps, 4 vague phrases
    • 100Execution cost. Instruction body is 2289 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)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 457: enough signal without eating the budget
    • +4Structure: 23 headings
    • +3Step-by-step instructions: 11 items
    • +4Has examples (14 code blocks)
    • +4Reference files are cited in the instructions (6 of 6)

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