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Found 1,779 Skills
Initialize evo for the current repository by exploring the codebase, proposing unexplored optimization dimensions, constructing the benchmark inside a baseline worktree, and running the first experiment. Use when the user invokes /evo:discover, mentions setting up evo, wants to instrument a codebase for autonomous optimization, or asks to start a new evo run on a project.
Rewrite and optimize existing blog posts for Google rankings (December 2025 Core Update, E-E-A-T) and AI citations (GEO/AEO). Full rewrite for both Google rankings AND AI citations. For AI-citation-only audit (no Google work), use blog-geo instead. Replaces fabricated statistics with sourced data, applies answer-first formatting, adds Pixabay/Unsplash images, generates built-in SVG charts, injects FAQ schema, performs AI content detection, adds citation capsules and information gain markers, and updates freshness signals. Works with any blog format (MDX, markdown, HTML). Use when user says "rewrite blog", "optimize blog", "update blog", "improve blog", "fix blog", "refresh blog post", "blog optimization".
Quantitative strategy generation and optimisation framework via Longbridge — create, modify, and backtest quant strategies: parameter grid search, walk-forward validation, overfitting detection (in-sample vs. out-of-sample), strategy combination (multi-strategy correlation diversification), Sharpe / Calmar ratio optimisation. Generates Python code frameworks for local execution. Triggers: "策略优化", "策略生成", "参数优化", "网格搜索", "回测优化", "过拟合", "walk-forward", "策略回测优化", "策略組合", "策略優化", "策略生成", "參數優化", "網格搜索", "回測優化", "strategy optimization", "strategy generation", "parameter optimization", "grid search", "overfitting", "walk-forward validation", "strategy backtest", "Sharpe ratio", "Calmar ratio".
Generates performance-focused guidance for Google Cloud workloads based on the design principles and recommendations in the Performance Optimization pillar of the Google Cloud Well-Architected Framework (WAF). Use this skill to evaluate a workload, identify performance requirements, and provide actionable recommendations for resource allocation, modular design, and elasticity.
Mean-variance portfolio optimization via Conjugate Gradient — 40-60× faster than the legacy Neumann path (ADR-126 Phase 3, ADR-123 Wedge 8)
Comet Preset Path: Non-bug Minor Tweaks. Skip brainstorming and full plan, directly proceed with open → lightweight build → light verify → archive. Suitable for partial optimizations of copy, configurations, documents, or prompts.
React and Next.js performance optimization patterns adapted from Vercel Engineering's React Best Practices (https://github.com/vercel-labs/agent-skills). Organizes 70+ rules across 8 priority categories — waterfalls, bundle size, server-side, client fetching, re-render, rendering, JS micro-perf, advanced. Use when writing, reviewing, or refactoring React/Next.js code for performance.
Train Mixture of Experts (MoE) models using DeepSpeed or HuggingFace. Use when training large-scale models with limited compute (5× cost reduction vs dense models), implementing sparse architectures like Mixtral 8x7B or DeepSeek-V3, or scaling model capacity without proportional compute increase. Covers MoE architectures, routing mechanisms, load balancing, expert parallelism, and inference optimization.
Multi-cloud orchestration for ML workloads with automatic cost optimization. Use when you need to run training or batch jobs across multiple clouds, leverage spot instances with auto-recovery, or optimize GPU costs across providers.
Comprehensive PostgreSQL database engineering skill covering indexing strategies, query optimization, performance tuning, partitioning, replication, backup and recovery, high availability, and production database management. Master advanced PostgreSQL features including MVCC, VACUUM operations, connection pooling, monitoring, and scalability patterns.
Python 3.11+ performance optimization guidelines (formerly python-311). This skill should be used when writing, reviewing, or refactoring Python code to ensure optimal performance patterns. Triggers on tasks involving asyncio, data structures, memory management, concurrency, loops, strings, or Python idioms.
Expert skill for integrating cloud AI APIs (Claude, GPT-4, Gemini). Covers secure API key management, prompt injection prevention, rate limiting, cost optimization, and protection against data exfiltration attacks.