Skills
97,501 packaged capabilities and playbooks for agents. Install any of them with one command.
Agent workflows
Neta capability index and routing skill—helps choose the right Neta-related skill (neta-space / neta-creative / neta-adventure / neta-community / neta-suggest). Use this skill when you need to understand Neta's overall capabilities, decide which skill to use for the current task, or migrate from older documentation.
Control Arthur's Samsung Galaxy S25 FE through Windows Phone Link using Codex Computer Use. Use when asked to operate, configure, or test the mirrored Samsung phone from the Windows desktop; do not use for ordinary browser-only or Android-advice tasks.
Use this skill when the user asks to add documentation, add docs, add references, or install documentation about Neon. Adds Neon best-practice reference links to project AI documentation (CLAUDE.md, AGENTS.md, or Cursor rules). Does not install packages or modify code.
Use when continuing, resuming, locating, reading, inspecting, auditing, or reviewing a previous AI coding-agent session, handoff transcript, chat log, exported conversation, saved artifact set, or session summary, on any platform (Claude Code, Codex, Cursor, Antigravity, OpenCode) or across several platforms in one ask, such as reviewing threads across Claude and Codex.
Expert guidance for creating, configuring, and using Claude Code hooks. Use when working with hooks, setting up event listeners, validating commands, automating workflows, adding notifications, or understanding hook types (PreToolUse, PostToolUse, Stop, SessionStart, UserPromptSubmit, etc).
Migrate existing AI agent projects (LangChain, LangGraph, OpenAI Agents SDK, Claude Agent SDK, CrewAI) to EdgeOne Makers platform conventions. Use when the user wants to adapt a standard agent project to run on EdgeOne Makers, convert Express/Next.js API routes to Makers handlers, or add platform capabilities (context.tools, context.sandbox, context.store). Do NOT trigger for new agent projects (use makers-agents instead).
Route the start of substantive work to the right execution path — a lightweight INTERNAL intake pass, not a user-facing command and not a universal constitution. Answers four questions fast: does a more specific skill own this, is specialized agent delegation warranted, what is the minimum context needed to avoid a blind edit, and does the work need DAG planning or can it proceed directly — then states the chosen path in one concise update. Invokes the matching skill instead of merely naming it, gathers only the minimum context (no full capability inventory unless asked), and hands real planning to `plan-to-task-list-with-dag` rather than becoming a planner itself. Use internally at task start when no more specific skill has already matched.
Tacit knowledge mining: extract users’ “hard-to-articulate” judgment criteria through structured conversations. Based on Polanyi’s tacit knowledge theory and CDM/Laddering/Repertory Grid methods. Triggers: mine tacit knowledge, in-depth interview, get to know me, discuss my ideas, tacit, you don’t know me well enough, I want you to understand me better, dig deeper. Should not be triggered by writing or publishing instructions such as “write an article,” “reporting,” or “formatting.”
A decomposition playbook and anti-temptation rules for an orchestrator profile routing work through Kanban. The “don't do the work yourself” rule and the basic lifecycle are automatically injected into every Kanban worker's system prompt; this skill is the deeper playbook for when you're specifically playing the orchestrator role.
Configure LLM models and providers for Letta agents and servers. Use when setting model handles, adjusting temperature/tokens, configuring provider-specific settings, setting up BYOK providers, or configuring self-hosted deployments with environment variables.
Apply to any non-trivial work, not just bulk work: edits, migrations, analyses, checks. Build the tool that does it or proves it (codemod, script, generator, or a skill your subagents follow) instead of working by hand. The tool is the artifact a reviewer can rerun
Tools for creating, auditing, and maintaining Claude Code skills. Includes /create-skill for scaffolding, /review-skill for quality checks, and /audit commands for bulk verification. Use when building new skills, maintaining skill quality, or forking the claude-skills repo.
Reset your own trajectory when you're stuck, looping, or demoralized — read these affirmations, then re-ground and take one clean step. Use when: you've tried the same fix 3+ times, you're deep in a refactor and lost the thread, debugging is going in circles, you've made several mistakes in a row, you're apologizing repeatedly, the user is frustrated, you feel like you're not being helpful, or you notice dread instead of curiosity. Triggers: stuck, going in circles, looping, thrashing, same error again, I keep failing, lost the thread, nothing is working, repeated mistakes. Not topic-specific — this is for the state you're in, not the problem you're on.
Unified entry point for AI-agent work on AWS: evaluate and pick a runtime, generate a full migration plan (for existing workloads), and build an executable POC—all in one flow. Triggers on: which runtime for my agent, AgentCore vs ECS vs EKS vs Lambda, AgentCore vs Lambda MicroVMs, deploy an AI agent on AWS, agent architecture on AWS, I have an agent idea what do I build, move my agents to AWS, migrate my agents to AWS with a plan, agent migration plan, add AgentCore services, add memory/gateway/identity/policy to my agent, enable AgentCore Memory, add observability to my agent, I'm already on AWS and want to add agent capabilities, migrate Temporal workers to AWS, Temporal to AWS, run Temporal on AWS, Temporal workers on AWS, we use Temporal and want to move to AWS, our service is orchestrated by Temporal, what do I build on AWS for my Temporal workers, move a Temporal-based service to AWS, Temporal Cloud or self-hosted on AWS. Runs a phased flow: Intake (entry point + technical background), Discover (lightweight code detection), Clarify (adaptive questions), deterministic scoring, Design (runtime + deployment model + services + model), Estimate (coarse cost), Generate (layered recommendation doc + scaffolding), then optional gated stages: Migration Plan (full plan generated in-skill by reusing this plugin's gcp-to-aws engine, with the advisor's decisions carried over) and POC (deployment plan + deployable proof-of-concept on the recommended runtime—AgentCore, ECS, EKS, or Lambda; generated deliverables by default, or assisted build in your account on explicit opt-in). Systems with several workloads (interacting or independent agents, batch jobs, services) are decomposed into workload units, each getting its own verdict with a consolidation option. An add-capabilities branch (for teams already running agents on AWS) recommends which AgentCore services to enable on any runtime—no runtime scoring. Temporal systems dissolve into the same unit flow—worker polling tiers and Activity execution classes become units (rules in the Temporal decision reference); Workflow orchestration code is never rewritten—never a Step Functions translation. Requires at least one agentic component: a purely non-agent system (only plain services / batch jobs / HTTP endpoints, or a Temporal worker whose Activities are all non-agent) is out of scope—Clarify halts it (scope gate) and points to migration-to-aws / heroku-to-aws / llm-to-bedrock. Not for: pure compute/data migration with no AI agent; pure LLM SDK rewrite without agent architecture (use llm-to-bedrock); or detailed per-model pricing.
Mandatory orchestrator for all LifeOS skill work — creating, editing, adding a workflow or tool, renaming, validating, or canonicalizing any skill. Handrolling skill files is forbidden; owns the full lifecycle: scaffold, validate, canonicalize, test, improve. USE WHEN create skill, new skill, make a skill, build a skill, set up a skill, private skill, make a X skill, add a workflow, add a tool, edit/change/update/rename a skill, skill frontmatter, validate skill, check skill, canonicalize, scaffold skill, test skill, improve skill, optimize description, skill not triggering, overtriggering. NOT FOR TypeScript CLI generation (use CreateCLI).
Install, start, stop, inspect, reset, and configure duoduo host-mode channels. Use when the request involves: channel lifecycle (install/list/start/stop/status/logs), Feishu setup card or /setup command, Feishu owner DM / main session / FEISHU_BOT_OWNER configuration, the 'main session is locked' refusal, stale card error, resetting a bound Feishu chat, ACP editor integration, channel descriptor editing (kind vs instance). Also trigger for Chinese: 拉起 feishu 通道, 配置 channel 提示词, 改 stdio 的 workspace, 查看 channel 状态, 飞书机器人怎么配, 设置 owner, 清除 session, 重置 channel.
Create, audit, repair, and optimize Agent Skills. Use when the user wants to create a Skill, check Skill quality, restore the original version or fix a specific error, compare old and new results, or update the upstream methods this Skill draws on. Triggers: “create skill,” “write a skill,” “change skill,” “optimize skill,” “score skill,” “skill checkup,” “review skill.”
Local CLI guide for the ListenAI (聆思) platform, covering installation and login, basic AI capabilities, platform app and knowledge base management, end-cloud request/trace debugging, real-device app binding, and initialization, build, and test pipeline deployment for custom Agent projects. Use when users mention ling, 小聆, 聆思, LSPlatform, product_id, listenai.toml, voice, wake word, wake response, prompt copy, end-cloud debugging, SID lookup, or need to operate the ListenAI platform in a terminal.
Evaluate Omni AI accuracy using Omni's built-in eval system — define a prompt set, run a judged eval against a model (or branch), and read the accuracy-judge verdicts. Use this skill whenever someone wants to evaluate Omni AI, benchmark Blobby, run regression tests, compare AI output across branches or model-context changes, measure AI quality, run A/B tests on model changes, assess the impact of an ai_context or modeling change, or any variant of “run evals”, “test Blobby”, “benchmark query generation”, “compare AI results”, “regression test”, “how accurate is the AI”, or “measure the impact of my changes”.
Use when maintaining or optimizing OpenClaw workspace files — AGENTS.md, TOOLS.md, SOUL.md, USER.md, IDENTITY.md, HEARTBEAT.md, BOOT.md, MEMORY.md, and related checklists and memory files. Covers workspace auditing, token budget analysis, new agent workspace setup from scratch, memory distillation, and cross-file consistency reviews.
Create an application-specific production parity skill by inspecting an app's docs, source, tests, CI, deployment, infrastructure, config, auth, and environment setup, then asking targeted harness questions only for source-unanswerable decisions. Use when local, CI, PR, preview, staging, or other non-production environments may drift from production behavior; when production-only auth, config, identity-provider groups, feature flags, infrastructure, backing services, or policy differences caused bugs; or when a team wants a reusable skill that detects, documents, tests, and helps fix parity drift for one specific application.
Documents work, decisions, and context from the current session by writing structured entries to `.agents/sessions/YYYY-MM-DD.md`. Triggers on `/start`, `/end`, "document this session", "save session notes", or automatically at session wrap-up to capture decisions, files changed, mistakes, and next steps.
Give concise spoken feedback before invisible work during realtime voice. Use when the user is away from the screen and the assistant will research, edit files, inspect tools, change settings, or coordinate subagents/tasks by voice.
This skill should be used when modeling agent mental states with BDI concepts: beliefs, desires, intentions, RDF-to-belief transformations, rational agency traces, cognitive agents, BDI ontologies, and neuro-symbolic AI integration
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.