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AC schema-markup

When the user wants to implement, audit, or validate structured data (schema markup) on their website. Use when the user mentions 'structured data,' 'schema.org,' 'JSON-LD,' 'rich results,' 'rich snippets,' 'schema markup,' 'FAQ schema,' 'Product schema,' 'HowTo schema,' or 'structured data errors in Search Console.' Also use when someone asks why their content isn't showing rich results or wants to improve AI search visibility. NOT for general SEO audits (use seo-audit) or technical SEO crawl issues (use site-architecture).

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 5 files body ≈ 2 723 tokens Open the sourcegithub.com↗ analyzed 2 d ago

When the user wants to implement, audit, or validate structured data (schema markup) on their website.

As a process C 56/100 · Has gaps — weak spots: inputs and preconditions, failures and branches, consistency

AnalyzerMarketingtype 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
100/100
safety, quality, tests
Safety 60%
100
Quality 40%
99
Run on models
none yet
Process rating
C
56/100
Has gaps
Inputs and preconditions w 11
0
Failures and branches w 10
0
Running it twice w 4
30
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: 5. 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    • note edit-residue the text marks something as outdated (lines 86, 132): check that old rules are not kept next to new ones — the full check reads the text for contradictions

    Process rating: all ten parameters 56/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 30Running it twice. 5 mutating operations with no state check
    • 40Consistency. Frontmatter name (schema-markup) differs from the folder (cs-schema-markup)
    • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 85Steps. 68 steps, 3 vague phrases
    • 100When it triggers. States when to use and when not to
    • 100Execution cost. Instruction body is 2723 tokens
    • 100Progress reporting. Reports progress
    • low 11 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

    • +5Description has no quoted example phrases that should trigger the skill
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 530: enough signal without eating the budget
    • +4Structure: 22 headings
    • +3Step-by-step instructions: 68 items
    • +3Output format is stated explicitly
    • +4Has examples (1 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)
    • +3All 1 scripts are documented
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 99.