BC scrapeless-scraping-browser
Cloud browser automation CLI for AI agents powered by Scrapeless. Use when the user needs to interact with websites using cloud browsers, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task with residential proxies and anti-detection features. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "use a proxy", "bypass detection", or any task requiring cloud browser automation.
Cloud browser automation CLI for AI agents powered by Scrapeless.
As a process C 55/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency
What is at stake
Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
The skill contains commands that delete files, rewrite disks or execute code fetched from the network. The agent may run them without asking if it believes the instructions require it.
Replace destructive commands with safe equivalents that ask for confirmation, scope them to one folder, and stop piping curl into a shell: pin a version and a checksum.
How to improve
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- 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
-
medium Dangerous commands
cmd-shell-rcreferences/authentication.md:24Writes to a shell startup fileecho 'export SCRAPELESS_API_KEY=your_api_key_here' >> ~/.zshrc
Files scanned: 5. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 6116 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 55/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 16 mutating operations with no state check
- 40Consistency. Frontmatter name (scrapeless-scraping-browser) differs from the folder (scrapeless-scraping-browser-skill)
- 50Failures and branches. 0 branches, has a failure section
- 70When it triggers. States when to use, but not when not to
- 70Execution cost. Instruction body is 6116 tokens
- 100Tools and files. Tools declared in frontmatter
- 100Steps. 27 steps
- low The response is described with custom markup (12 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)
- +3Output format is not stated: the model decides each time
- -41 reference files, but SKILL.md never points to them: the model will not open them
- +1No license
- +2Single-language instructions
- +5Description quotes 8 example trigger phrases
- +3Description length 574: enough signal without eating the budget
- +4Structure: 57 headings
- +3Step-by-step instructions: 27 items
- +4Has examples (37 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.