AD 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'.
As a process D 39/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- 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-securityexamples/EXAMPLES.md:85Offensive-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: 10. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- note
frontmatter-keyunknown frontmatter key "homepage"
Process rating: all ten parameters 39/100
- 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
- 100Steps. 11 steps
- 100Execution cost. Instruction body is 2225 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.