Multi-model consensus system — send a query to 3+ different LLMs via OpenRouter simultaneously, then a judge model evaluates all responses and produces a winner, reasoning, and synthesized best answer. Like having a board of AI advisors. Use for important decisions, code review, research verification.
Live LLM model pricing and capabilities from OpenRouter. List top models, search by name, compare side-by-side, find best model for a use case, check pricing. Always up-to-date from the OpenRouter API. Triggers: model pricing, compare models, best model for, cheapest model, model cost, LLM comparison, what models are available.
Live LLM model intelligence from OpenRouter. Compare pricing, search models by name, find the best model for any task — code, reasoning, creative, fast, cheap, vision, long-context. Real-time data from 200+ models. Use when choosing models, comparing costs, or auditing your AI stack.
The definitive OpenRouter skill — intelligent model routing by task type, cost tracking with budget alerts, automatic fallback chains, side-by-side model comparison, and savings recommendations. Use for optimizing AI model selection, controlling costs, and building resilient LLM pipelines.
This skill should be used when the user wants to "optimize resume for job", "check ATS score", "improve resume bullets", "analyze resume gaps", "tailor resume to job description", "get ATS compatibility score", "improve bullet points", "match resume to job posting", "fix resume for ATS", or wants to maximize their resume's impact and ATS compatibility using the Placed platform at placed.exidian.tech.
Autonomous DeFi trading agent for BNB Chain with multi-strategy engine, network switching, and reinforced learning.
Multi-dimensional Quality Acceptance and Problem Convergence Engine - Deeply deconstruct requirements, eliminate extreme defects, define absolutely objective acceptance and failure criteria.
Reference reusable patterns for validation, error handling, and test scaffolding
Self-improvement layer with evaluation separation, rollback, and tiered operator gates. Observes outcomes across sessions, detects recurring patterns, proposes improvements, validates proposals through a separate evaluator invocation, and applies changes safely with snapshot/rollback capability.
调用阿里云百炼（DashScope/Model Studio）平台上的千问及万相2.6模型，完成图像生成与编辑任务。 当用户涉及以下任何场景时，必须使用此 skill： - 调用千问或万相2.6模型生成、编辑图片 - 百炼平台图像相关 API - 使用 dashscope SDK 访问 qwen-image / wan2.6 模型 - 文生图、图像编辑、图文混排输出 - 用户提及 qwen-image、wan2.6-image、wan2.6-t2i 等关键词
Auto retry & fix LLM tool calls with exponential backoff, format validation, error correction, boost tool call success rate by 90%
百变专家 · Universal Expert Engine v5.2 任意领域的专业分析、决策支持、信息验证。 触发：说"深度分析 XXX"、"完整分析 XXX"、"全面评估 XXX"时加载。 日常闲聊/简单问答不触发。累计修复28项逻辑缺陷。
Multi-model automatic fallback system. Monitors model availability and automatically falls back to backup models when the primary model fails. Supports MiniMax, Kimi, Zhipu and other OpenAI-compatible APIs. Use when: (1) Primary model API is unavailable, (2) Model response time is too slow, (3) Rate limit exceeded, (4) Need to optimize costs by using cheaper models for simple tasks.
自动化生成工业级机器学习分类算法代码、调用算法做预测、输出准确率对比和可视化结果，支持新手友好的结果解读。
智谱 GLM-4V/4.6V 视觉模型调用技能。用于图像/视频理解、多模态对话、图表分析等任务。 当用户提到：图片理解、图像识别、视觉模型、GLM-4V、GLM-4.6V、多模态分析、看图说话、图表分析、视频理解时使用此技能。
AI-powered code review with intelligent model routing — saves 40%+ vs always using the most expensive model
智能模型路由系统，根据任务自动选择最优 Qwen 模型（qwen3.5-plus/qwen-max/qwen-coder-plus），越用越聪明，节省成本
Cost-effective skill selector for maximizing ROI on AI operations (增收降本版 v1.0.0). Use when the user needs to accomplish a task and wants the optimal skill chosen automatically to minimize costs while maximizing quality. Evaluates skills based on quality (35%), token cost (30%), security/reliability (20%), and speed (15%). Searches both local skills and clawhub.com, presents top recommendations for user confirmation before execution.
Guides AI coding agents through the lmms-eval codebase - a unified evaluation framework for Large Multimodal Models (LMMs). Use when integrating new models, adding evaluation tasks/benchmarks, running YAML config-driven evaluations, orchestrating non-blocking training-time evaluation via the HTTP eval server, or navigating the evaluation pipeline architecture.
Run and analyze evaluations for verifiers environments using prime eval. Use when asked to smoke-test environments, run benchmark sweeps, resume interrupted evaluations, compare models, inspect sample-level outputs, or produce evaluation summaries suitable for deciding next steps.
Optimize environment system prompts with GEPA through prime gepa run. Use when asked to improve prompt performance without gradient training, compare baseline versus optimized prompts, run GEPA from CLI or TOML configs, or interpret GEPA outputs before deployment.
