全部能力

Build autonomous game-playing agents using AI and reinforcement learning. Covers game environments, agent decision-making, strategy development, and…

Orchestrates end-to-end autonomous AI research projects using a two-loop architecture. The inner loop runs rapid experiment iterations with clear optimization…

Build agents specialized in conducting thorough research, gathering information from multiple sources, and synthesizing findings. Covers research planning,…

Cross-border e-commerce expansion advisor. Scores target markets on 8 weighted dimensions (market size, ecommerce penetration, competition, regulatory…

ddd-aggregate — an installable skill for AI agents, published by ruvnet/ruflo.

搜索 Amazon.com，提取商品数据，并提供排名推荐。适用于用户要求在亚马逊购物、查找商品、比较商品或进行调研等场景……

cost-optimize — an installable skill for AI agents, published by ruvnet/ruflo.

Trace agent execution by collecting spans and building a trace tree for a task

iot-witness-verify — an installable skill for AI agents, published by ruvnet/ruflo.

federation-init — an installable skill for AI agents, published by ruvnet/ruflo.

federation-status — an installable skill for AI agents, published by ruvnet/ruflo.

ddd-validate — an installable skill for AI agents, published by ruvnet/ruflo.

iot-anomalies — an installable skill for AI agents, published by ruvnet/ruflo.

federation-audit — an installable skill for AI agents, published by ruvnet/ruflo.

Build applications with the Letta API — a model-agnostic, stateful API for building persistent agents with memory and long-term learning. Covers SDK patterns…

iot-register — an installable skill for AI agents, published by ruvnet/ruflo.

ddd-context — an installable skill for AI agents, published by ruvnet/ruflo.

Push the LLM to reconsider, refine, and improve its recent output. Use when user asks for deeper critique or mentions a known deeper critique method, e.g.…

cost-report — an installable skill for AI agents, published by ruvnet/ruflo.

Technical guide for creating a new Paperclip agent adapter. Use when building a new adapter package, adding support for a new AI coding tool (e.g. a new CLI agent, API-based agent, or custom process), or when modifying the adapter system. Covers the required interfaces, module structure, registration points, and conventions derived from the existing claude-local and codex-local adapters.