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Found 11,924 Skills
Build AI agent interfaces with Polpo UI — composable React chat components, CLI tools, and starter templates. Use when the user wants to create a chat app, add chat components, install @polpo-ai/chat, scaffold a Polpo project, configure theming/dark mode, use ChatInput, ChatMessage, ChatSessionList, or any Polpo UI component. Triggers on "polpo ui", "chat UI", "chat component", "@polpo-ai/chat", "@polpo-ai/ui", "create-polpo-app", "chat input", "session list", "agent selector", "chat interface", "polpo chat", "chat widget", "multi-agent".
Explore a codebase for architectural friction, discover refactoring opportunities, and propose module-deepening refactors as GitHub issue RFCs. Uses friction-driven exploration and parallel sub-agents to design multiple interface alternatives. Use when user wants to improve architecture, find refactoring opportunities, consolidate coupled modules, reduce complexity, make code more testable, or review codebase health.
Set up or update the agent-first engineering harness for any repository. Implements the complete scaffolding that makes AI coding agents effective: knowledge maps (AGENTS.md as a concise TOC), structured documentation, architecture boundaries, enforcement rules (.harness/*.yml specs), quality scoring, and process patterns for agent-driven development. Use this skill whenever someone wants to make a repo agent-ready, set up AGENTS.md or docs/ structure, define domain boundaries or golden principles, generate .harness/ configuration, audit agent readiness, or update an existing harness. Also trigger when a user reports problems with agent effectiveness, context management, or architectural drift — these are symptoms of a missing or stale harness. Trigger on: "harness this repo", "set up harness", "agent-first setup", "make this agent-ready", "update the harness", "assess agent readiness", "set up AGENTS.md", "organize for agents", or any discussion about structuring a codebase for AI agent workflows.
This skill should be used when the user asks to "model agent mental states", "implement BDI architecture", "create belief-desire-intention models", "transform RDF to beliefs", "build cognitive agent", or mentions BDI ontology, mental state modeling, rational agency, or neuro-symbolic AI integration. Part of the context engineering skill suite — also activates when the user mentions "context engineering" or "context-engineering" in the context of belief-based agent reasoning.
Store and retrieve agent memories across jobs. Enables long-term context, learning from past interactions, and building agent knowledge bases. Based on OpenClaw's memory-core architecture.
Use when a job requires modifying the agent's own code, configuration, personality, cron jobs, skills, or operating system files.
Single entry point for all AEM 6.5 LTS Replication skills. Covers configuring replication agents, activating/deactivating content, using the Replication API programmatically, and troubleshooting distribution issues for Adobe Experience Manager 6.5 LTS.
C-suite orchestration layer that routes founder questions to the right advisor role(s), triggers multi-role board meetings for complex decisions, synthesizes outputs, tracks decisions, and manages cross-functional alignment. Every C-suite interaction starts here. Use when coordinating executive decisions, routing strategic questions, managing board meetings, synthesizing multi-perspective advice, tracking decision history, resolving inter-department conflicts, or when user mentions chief of staff, orchestrator, c-suite coordinator, executive routing, board coordination, decision synthesis, advisor routing, multi-agent coordination, or strategic orchestration.
6-phase interactive interview that generates the agent's identity (SOUL.md), user profile (USER.md), access control (ACCESS_POLICY.md), and operational cadence (HEARTBEAT.md). Re-runnable anytime to update any section.
Use this skill when you learn one or more design pattern(s) in the Langroid (multi) agent framework, and want to make a note for future reference for yourself. Use this either autonomously, or when asked by the user to record a new pattern.
Protects LLM agent systems in real-time with a 5-tier filter (hash cache, rule engine, ML classifier, LLM judge, human approval) and an async learning engine. Synthesizes new rules from every detected attack, adding less than 50ms latency. Trigger on 'add security layer', 'prevent prompt injection', 'adaptive guard', 'runtime protection', or 'agent security'.
피치 스킬 사용 중 발견된 문제점/노하우를 구조화하여 docs/스킬피드백/에 문서화하는 범용 스킬. "스킬 개선", "피드백 정리", "문제점 기록", "스킬 리뷰", "개선사항", "스킬 피드백" 키워드로 트리거. 모든 피치 스킬에 범용 적용 가능. 다른 AI 에이전트가 문서를 읽고 스킬을 개선할 수 있도록 구성.