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AD AI Rankings Leaderboard

Comprehensive AI leaderboard for LLM models and AI applications. Query model rankings, model IDs, and pricing from OpenRouter, Artificial Analysis, and Pinchbench. Trigger words include "AI rankings", "LLM leaderboard", "model comparison", "AI apps ranking", "best AI models", "model benchmark", "free models", "免费模型", "OpenRouter model ID", "OpenRouter 模型", "Artificial Analysis", "artificial analysis", "AI 智力指数", "intelligence index", "coding index", "coding排行榜", "agentic index", "agentic排行榜", "模型速度排行", "模型价格对比", "model ID for", "OpenRouter model parameter".

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 4 files body ≈ 4 628 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Comprehensive AI leaderboard for LLM models and AI applications.

As a process D 47/100 · Unfinished process — weak spots: when it triggers, inputs and preconditions, consistency

IntegrationAI and agentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw 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) Goose OpenCode
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
85
Run on models
none yet
Process rating
D
47/100
Unfinished process
Inputs and preconditions w 11
0
Progress reporting w 2
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

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 · 0

    ✓ No critical or high findings

    Files scanned: 4. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
    • note frontmatter-key unknown frontmatter key "display_name"
    • note frontmatter-key unknown frontmatter key "cli_dependencies"

    Process rating: all ten parameters 47/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 40Consistency. Frontmatter name (AI Rankings Leaderboard) differs from the folder (ai-leaderboard)
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
    • 60Steps. 112 steps, 5 vague phrases
    • 60Result and completion. Output format stated, no completion criterion
    • 70Execution cost. Instruction body is 4628 tokens
    • 100Running it twice. No mutating operations
    • low 13 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

    • +4Description does not say when NOT to use the skill (false activations)
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 21 example trigger phrases
    • +3Description length 563: enough signal without eating the budget
    • +4Structure: 42 headings
    • +3Step-by-step instructions: 112 items
    • +3Output format is stated explicitly
    • +4Has examples (24 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.