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AB playwright-skill

IMPORTANT - Path Resolution: This skill can be installed in different locations (plugin system, manual installation, global, or project-specific). Before executing any commands, determine the skill directory based on where you loaded this SKILL.md file, and use that path in all commands below.

sickn33/agentic-awesome-skills Claude Code author: sickn33 MIT 5 files body ≈ 3 470 tokens Open the sourcegithub.com↗ analyzed 2 d ago

IMPORTANT - Path Resolution: This skill can be installed in different locations (plugin system, manual installation, global, or project-specific).

As a process B 67/100 · Nearly there — weak spots: result and completion, when it triggers

ProcedurePlaywrightAI and agentstype and topics are labelled automatically from the skill text
Runs in: 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) OpenClaw Goose OpenCode
JSON
Technical rating
A
91/100
safety, quality, tests
Safety 60%
99
Quality 40%
79
Run on models
none yet
Process rating
B
67/100
Nearly there
When it triggers w 12
20
Result and completion w 14
40
Failures and branches w 10
50
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: playwright-skill (sickn33/agentic-awesome-skills)

The same skill appears in 2 more places: agentic-awesome-skills, agentic-awesome-skills

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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Dangerous commands cmd-privilege lib/helpers.js:47
      Privilege escalation / world-writable permissions (detector / deny-list definition; string literal in code, not executed)
      args: ['--no-sandbox', '--disable-setuid-sandbox']
      detectorcode literal

    Files scanned: 5. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "risk"
    • note frontmatter-key unknown frontmatter key "source"
    • note frontmatter-key unknown frontmatter key "date_added"
    • note frontmatter-key unknown frontmatter key "plugin"

    Process rating: all ten parameters 67/100

    • 20When it triggers. No condition that starts the skill
    • 40Result and completion. Does not say what the result is
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash, web, node) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 48 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3470 tokens
    • 100Running it twice. Mutating operations check current state
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 14 top-level sections: this looks like several domains in one skill

    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
    • -2localhost URLs: will not work for another user
    • +1No license
    • +2Single-language instructions
    • +3Description length 294: enough signal without eating the budget
    • +4Structure: 23 headings
    • +3Step-by-step instructions: 48 items
    • +4Has examples (20 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.