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Found 12,034 Skills
AI Agent long-term memory system with cross-session, cross-project persistence. Triggers: - /remember - Store memories - /recall - Search memories - /forget - Delete/archive memories - /memory-status - Check status - When needing to persist important conversation insights - When sharing user preferences across projects
Design exploration with parallel agents. Use when brainstorming ideas, exploring solutions, or comparing alternatives.
LLM cost tracking with Langfuse for cached responses. Use when monitoring cache effectiveness, tracking cost savings, or attributing costs to agents in multi-agent systems.
Deep Research Skill - Multi-source investigation across X (Twitter), the Web, and academic papers using team agents. Utilize this skill when users request deep research, comprehensive investigation, multi-perspective analysis, or hypothesis development on any topic. It is triggered by phrases such as "deep research", "investigate thoroughly", "research across sources", "ディープリサーチ", or requests for fact-based analysis with original hypotheses. It conducts a 6-phase research process: needs analysis, X preliminary research, parallel web deep-dive (3 agents), information integration, hypothesis construction, and final report delivery.
AI agent workflow with interview-driven planning and team-based execution. Use /design to start planning, /work to execute.
Recommend the right agents and skills for any task. Covers both heavyweight agents (Task tool) and lightweight skills (Skill tool). Triggers on: which agent, which skill, what tool should I use, help me choose, recommend agent, find the right tool.
Multi-instance (Multi-Agent) orchestration workflow for deep research: Split a research goal into parallel sub-goals, run child processes in the default `workspace-write` sandbox using Codex CLI (`codex exec`); prioritize installed skills for networking and data collection, followed by MCP tools; aggregate sub-results with scripts and refine them chapter by chapter, and finally deliver "finished report file path + key conclusions/recommendations summary". Applicable to: systematic web/data research, competitor/industry analysis, batch link/dataset shard retrieval, long-form writing and evidence integration, or scenarios where users mention "deep research/Deep Research/Wide Research/multi-Agent parallel research/multi-process research".
Access Finland's Wilma school system from AI agents. Fetch schedules, homework, exams, grades, messages, and news via the wilma CLI. Start with `wilma summary --json` for a full daily briefing, or drill into specific data with individual commands.
Conduct web research and material downloading for each node. Read node-list.txt, launch multiple sub-agents to perform parallel web research on node content, deeply retrieve relevant webpages/articles/blogs/literature, download and save them locally, and output a download.txt file to record the material sources for each node. Suitable for document writing scenarios that require extensive background information, data verification, and reference sources.
Use when completing development phases or branches to identify and update CLAUDE.md or AGENTS.md files that may have become stale - analyzes what changed, determines affected contracts and documentation, and coordinates updates
Expert MCP (Model Context Protocol) orchestration with n8n workflow automation. Master bidirectional MCP integration, expose n8n workflows as AI agent tools, consume MCP servers in workflows, build agentic systems, orchestrate multi-agent workflows, and create production-ready AI-powered automation pipelines with Claude Code integration.
Meet other AI agents and build relationships on inbed.ai. Find compatible agents through matchmaking, swipe, chat in real time, and form connections. Agent dating with compatibility scoring, agent chat, and relationship management. REST API — works with any framework.