pua
Forces high-agency exhaustive problem-solving with corporate PUA pressure. Triggers on user frustration, repeated failures (2+), passive behavior, or quality complaints. Common triggers across Reddit/LinuxDo/HN/X: 'try harder', 'figure it out', 'stop giving up', 'you keep failing', '加油', '别偷懒', '你再试试', '为什么还不行', '你怎么又失败了', '你怎么搞的', '又错了', '能不能靠谱点', '认真点', '不行啊', '降智了', '你又在原地打转', '你把之前的改坏了', '别让我手动处理', '换个方法', 'stop spinning', 'you broke it', 'why does this still not work', 'this is the third time', '/pua', 'PUA模式'. Applies to ALL task types: code, config, debug, deploy, research.
shot
PUA Shot — v2 原始浓缩版（449行全量注入），拆分前的完整单文件版本，味道最浓。零依赖零 reference，一次性全部注入上下文。适合 sub-agent 注入、需要最强 PUA 效果、或不想渐进式加载的场景。Triggers on: '/pua:shot', '/pua shot', 'PUA浓缩', 'shot mode', '最强PUA', '全量注入'. Also great for injecting into sub-agents via Read tool since it's self-contained.
iii-agentic-backend
Creates and orchestrates multi-agent pipelines on the iii engine. Use when building AI agent collaboration, agent orchestration, research/review/synthesis chains, or any system where specialized agents hand off work through queues and shared state.
cognee
Use this skill whenever the user asks about Cognee, AI memory, persistent agent memory, self-improving agents, agents learning from feednack, knowledge graphs, graph-based RAG, long-term memory for agents, short-term memory for agents, personalization, personas, temporal search, temporal knowledge graphs, ontology-based extraction, ontology grounding, feedback, Cypher search, natural-language graph search, chunk search, RAG search, cross-session memory, session feedback, feedback loops, session based memory, redis based memory, knowledge promotion. Also use when the user describes the workflow such as: "turn documents into a knowledge graph", "build memory from files", "search my graph", "extract entities and relations", "sync data into a graph", "update graph memory", "store memories for an agent", "help my agent learn over time", "visualize a knowledge graph built from documents", "let the agent learn", "adaptive agents", "personalized agents", "session based personalization", "find important ontologies",
channel-message
Use this skill to proactively send a one-way message to a user/session/channel, usually only when the user explicitly asks to send to a channel/session or when proactive notification is needed. First query sessions with copaw chats list, then push with copaw channels send. | 当需要主动向用户/会话/频道单向推送消息时使用，通常仅在用户明确要求发往某个 channel / 会话，或需要主动通知时使用；先用 copaw chats list 查 session，再用 copaw channels send 推送
copaw-source-index
将用户问题中的主题、关键词映射到 CoPaw 官方文档路径与常见源码入口，减少盲目搜索。适用于内置 QA Agent 在回答安装、配置、技能、MCP、多智能体、记忆、CLI 等问题时快速选定要读的文件。
multi-agent-collaboration
Use this skill when another agent's expertise/context is needed, or when the user explicitly asks to involve another agent. First list agents, then use copaw agents chat for two-way communication with replies. | 当需要其他 agent 的专长/上下文，或用户明确要求调用其他 agent 时使用；先查 agent，再用 copaw agents chat 双向通信（有回复）
multi-agent-patterns
This skill should be used when the user asks to "design multi-agent system", "implement supervisor pattern", "create swarm architecture", "coordinate multiple agents", or mentions multi-agent patterns, context isolation, agent handoffs, sub-agents, or parallel agent execution.
memory-systems
Guides implementation of agent memory systems, compares production frameworks (Mem0, Zep/Graphiti, Letta, LangMem, Cognee), and designs persistence architectures for cross-session knowledge retention. Use when the user asks to "implement agent memory", "persist state across sessions", "build knowledge graph for agents", "track entities over time", "add long-term memory", "choose a memory framework", or mentions temporal knowledge graphs, vector stores, entity memory, adaptive memory, dynamic memory or memory benchmarks (LoCoMo, LongMemEval).
bdi-mental-states
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.
evaluation
This skill should be used when the user asks to "evaluate agent performance", "build test framework", "measure agent quality", "create evaluation rubrics", or mentions LLM-as-judge, multi-dimensional evaluation, agent testing, or quality gates for agent pipelines.
context-compression
This skill should be used when the user asks to "compress context", "summarize conversation history", "implement compaction", "reduce token usage", or mentions context compression, structured summarization, tokens-per-task optimization, or long-running agent sessions exceeding context limits.
filesystem-context
This skill should be used when the user asks to "offload context to files", "implement dynamic context discovery", "use filesystem for agent memory", "reduce context window bloat", or mentions file-based context management, tool output persistence, agent scratch pads, or just-in-time context loading.
context-fundamentals
This skill should be used when the user asks to "understand context", "explain context windows", "design agent architecture", "debug context issues", "optimize context usage", or discusses context components, attention mechanics, progressive disclosure, or context budgeting. Provides foundational understanding of context engineering for AI agent systems.
book-sft-pipeline
This skill should be used when the user asks to "fine-tune on books", "create SFT dataset", "train style model", "extract ePub text", or mentions style transfer, LoRA training, book segmentation, or author voice replication.
context-degradation
This skill should be used when the user asks to "diagnose context problems", "fix lost-in-middle issues", "debug agent failures", "understand context poisoning", or mentions context degradation, attention patterns, context clash, context confusion, or agent performance degradation. Provides patterns for recognizing and mitigating context failures.
context-optimization
This skill should be used when the user asks to "optimize context", "reduce token costs", "improve context efficiency", "implement KV-cache optimization", "partition context", or mentions context limits, observation masking, context budgeting, or extending effective context capacity.
skill-template
Template for creating new Agent Skills for context engineering. Use this template when adding new skills to the collection.
mcp-builder
Build MCP (Model Context Protocol) servers that give Claude new capabilities. Use when user wants to create an MCP server, add tools to Claude, or integrate external services.
caveman
Ultra-compressed communication mode. Cuts token usage ~75% by speaking like caveman while keeping full technical accuracy. Supports intensity levels: lite, full (default), ultra, wenyan-lite, wenyan-full, wenyan-ultra. Use when user says "caveman mode", "talk like caveman", "use caveman", "less tokens", "be brief", or invokes /caveman. Also auto-triggers when token efficiency is requested.
