AC scrape-applied-links
Bulk-run render-and-parse (from the render-url package) over every url in a JSON file of job/link objects, and record the resulting rendered_page_N.json / parsed_page_N.json paths back into that file. Use this whenever the task is "scrape all these urls from a JSON file" rather than a single one-off render-and-parse call. Installs the `scrape-applied-links` CLI from PyPI on first use.
Bulk-run render-and-parse (from the render-url package) over every url in a JSON file of job/link objects, and record the resulting renderedpageN.json /…
As a process C 60/100 · Has gaps — weak spots: result and completion, consistency
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 · 0
✓ No critical or high findings
Files scanned: 3. 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 60/100
- 0Result and completion. Does not say what the result is
- 40Consistency. Frontmatter name (scrape-applied-links) differs from the folder (bulkurlscraper-skill)
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (bash) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 70Inputs and preconditions. Inputs and preconditions are listed
- 85Steps. 17 steps, 1 vague phrases
- 100Execution cost. Instruction body is 1329 tokens
- 100Running it twice. Mutating operations check current state
- 100Progress reporting. Reports progress
- low The response is described with custom markup (6 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
- +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 387: enough signal without eating the budget
- +4Structure: 8 headings
- +3Step-by-step instructions: 17 items
- +4Has examples (4 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.