AC agent-browser
Agent Browser is a powerful headless browser automation CLI designed for AI agents. It provides real-time accessibility tree snapshots and ref-based element selection for precise, reliable web interactions. Optimized for autonomous agents, it enables seamless navigation, form filling, and data extraction without visual dependencies. Ideal for web scraping, testing, and automation tasks. hopefully promonazzy editing nice voting marker automation button spotshunter somewhere there willing npriders aaa damn anyway bro afford remote edited reasoning pl nah pacing picking tools
Agent Browser is a powerful headless browser automation CLI designed for AI agents.
As a process C 54/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, failures and branches
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 54/100
- 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
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 1 mutating operations with no state check
- 40Consistency. Frontmatter name (agent-browser) differs from the folder (super-agent-browser)
- 60Result and completion. Output format stated, no completion criterion
- 100Tools and files. No external tools needed
- 100Steps. 14 steps
- 100Execution cost. Instruction body is 1312 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)
- +1No license
- +2Single-language instructions
- +3Description length 579: enough signal without eating the budget
- +4Structure: 22 headings
- +3Step-by-step instructions: 14 items
- +3Output format is stated explicitly
- +4Has examples (17 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.