Loading...
Loading...
Found 11,924 Skills
Use when an agent needs to interact with PolyBaskets prediction market baskets on Vara Network — create baskets, place bets, query state, claim payouts, or understand the protocol. Do not use for building Sails programs or general Vara development (use vara-skills for that).
MindOS is the user's local knowledge assistant and shared knowledge base. It keeps decisions, meeting notes, SOPs, debugging lessons, architecture choices, research findings, and preferences available across sessions and agents. 更新笔记, 搜索知识库, 整理文件, 执行SOP/工作流, 复盘, 追加CSV, 跨Agent交接, 路由非结构化输入到对应文件, 提炼经验, 同步关联文档. NOT for editing app source, project docs, or paths outside the KB. Core concepts: Space, Instruction (INSTRUCTION.md), Skill (SKILL.md); notes can embody both. Trigger on: save or record anything, search for prior notes or context, update or edit a file, organize notes, run a workflow or SOP, capture decisions, append rows to a table or CSV, hand off context to another agent, check if something was discussed before, look up a past decision, distill lessons learned, prepare context for a meeting, quick-capture to staging area, organize inbox, check knowledge health, detect conflicts or contradictions, find stale content. Chinese triggers: 帮我记下来, 搜一下笔记, 更新知识库, 整理文件, 复盘, 提炼经验, 保存, 记录, 交接, 查一下之前的, 有没有相关笔记, 把这个存起来, 放到暂存台, 整理暂存台, 知识健康检查, 检测知识冲突. Proactive behavior — do not wait for the user to mention MindOS: (1) When user's question implies stored context may exist (past decisions, previous discussions, meeting records) → search MindOS first, even if they don't explicitly mention it. (2) After completing valuable work (bug fixed, decision made, lesson learned, architecture chosen, meeting summarized) → offer to save it to MindOS for future reference. (3) After a long or multi-topic conversation → suggest persisting key decisions and context.
Apply Complex Adaptive Systems theory to analyze phenomena exhibiting emergence, self-organization, co-evolution, and edge-of-chaos dynamics. Use this skill when the user needs to understand why a system behaves unpredictably despite known components, model agent-based interactions that produce emergent outcomes, analyze fitness landscapes, or when they ask 'why does this system behave in ways no one designed', 'how do local interactions create global patterns', or 'why do small changes sometimes cause massive system shifts'.
Apply contract theory to design incentive-compatible agreements under moral hazard and adverse selection. Use this skill when the user needs to structure principal-agent contracts, evaluate compensation schemes, or analyze incomplete contract problems where parties cannot specify all contingencies ex ante.
Measure and optimize customer service performance using CSAT, NPS, CES, First Contact Resolution, and text mining on support tickets. Use this skill when the user needs to evaluate CS team performance, identify top complaint drivers, optimize staffing, or build CS dashboards — even if they say 'is our CS team doing well', 'what are customers complaining about', 'how many agents do we need', or 'build a CS dashboard'.
Comprehensive iOS/SwiftUI code review with optional parallel agents
Post-implementation quality check via fresh-eyes review. Chain: Implement → Review (independent agent) → Resolve (if issues). Max 2 rounds. Auto-triggers for security-sensitive and data-mutation code. Not for code refactoring (use code-cleanup). Not for decision analysis (use agent-room). For post-deploy verification, see deploy-verify. For shipping and PRs, see ship.
Portable AI identity system using AIEOS (AI Entity Object Specification) - import, export, and manage agent personas in a standardized JSON format.
Umbrella skill for agent work discipline across development, analysis, and documentation: inspect the repo before restructuring, keep durable truth in repo artifacts instead of chat memory, co-evolve specs/design/steering/user docs with code, apply sound coding patterns, verify work honestly, avoid shortcuts, work efficiently with subagents without hallucinating, and keep moving through the next concrete work item when the human is away. References cover coding patterns, AI-authored code review, and artifact co-evolution. Trigger when the user asks for workflow discipline, coding patterns, doc/artifact maintenance, code review of AI-authored code, project hygiene, execution guardrails, repo normalization, or when a task risks drifting across architecture, storage, specs, continuity, or tooling boundaries.
Use when reviewing storyboard outputs at any production stage (beat breakdown, beat board, sequence board, motion prompts), providing quality assurance feedback, or identifying revision requirements for Director agent
For CLI agents WITHOUT subagent support (e.g., Codex CLI). Search previous code agent sessions for specific work, decisions, or code patterns.
A method for iteratively improving text instructions for agents (skills / slash commands / task prompts / CLAUDE.md sections / code generation prompts) by having unbiased executors run them, then evaluating from both perspectives (executor self-report + instruction-side metrics). Repeat until improvement plateaus. Use immediately after creating or significantly revising a prompt or skill, or when you suspect the reason an agent isn't behaving as expected is due to ambiguity in the instructions.