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AC schema-markup

When the user wants to add or optimize structured data (Schema.org, JSON-LD). Also use when the user mentions "schema," "structured data," "JSON-LD," "rich results," "rich snippets," "Google rich snippets," "featured snippet schema," "add schema to page," "missing structured data," "schema validation error," "Schema Markup Validator," "Google Rich Results Test," "FAQ schema," "Article schema," "Organization schema," "JobPosting," "HowTo," "Event," "SoftwareApplication," "BreadcrumbList," "WebSite," "Recipe," "Product," "Dataset," or "GEO."

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 2 files body ≈ 4 783 tokens Open the sourcegithub.com↗ analyzed 2 d ago

When the user wants to add or optimize structured data (Schema.org, JSON-LD). Also use when the user mentions "schema," "structured data," "JSON-LD," "rich…

As a process C 63/100 · Has gaps — weak spots: inputs and preconditions, failures and branches, consistency

ProcedureWriting and documentsMarketingData and analyticstype 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
97/100
safety, quality, tests
Safety 60%
100
Quality 40%
92
Run on models
none yet
Process rating
C
63/100
Has gaps
Inputs and preconditions w 11
0
Failures and branches w 10
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

    • note edit-residue the text marks something as outdated (lines 31, 87, 135): check that old rules are not kept next to new ones — the full check reads the text for contradictions

    Process rating: all ten parameters 63/100

    • 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
    • 40Consistency. Frontmatter name (schema-markup) differs from the folder (kostja94-schema-markup)
    • 60Result and completion. Output format stated, no completion criterion
    • 70Execution cost. Instruction body is 4783 tokens
    • 85Steps. 31 steps, 3 vague phrases
    • 100Tools and files. No external tools needed
    • 100When it triggers. States when to use and when not to
    • 100Running it twice. Mutating operations check current state
    • low 15 top-level sections: this looks like several domains in one skill
    • low The response is described with custom markup (5 tags): a typed call is more reliable

    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

    • +4Description does not say when NOT to use the skill (false activations)
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 24 example trigger phrases
    • +3Description length 545: enough signal without eating the budget
    • +4Structure: 25 headings
    • +3Step-by-step instructions: 31 items
    • +3Output format is stated explicitly
    • +4Has examples (4 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 92.