Skills
96,981 packaged capabilities and playbooks for agents. Install any of them with one command.
Agent workflows
Use when you need to run interactive CLI tools (vim, git rebase -i, Python REPL, etc.) that require real-time input/output - provides tmux-based approach for controlling interactive sessions through detached sessions and send-keys
Platform-neutral guidance for using Open Browser Use, the open-source Chrome automation stack for AI agents. Use when an agent needs to install, verify, troubleshoot, or operate Open Browser Use through its browser extension, native CLI, JavaScript SDK, Python SDK, Go SDK, or Browser Use style JSON-RPC methods; use for tasks involving real Chrome tabs, user tab claiming, CDP commands, downloads, file choosers, clipboard helpers, or session cleanup.
This is the main on-chain DeFi skill. Use it for ALL of the following:\nStrategy discovery: 'how to make money on-chain', 'any profitable strategies', '链上有什么赚钱机会', '有什么盈利机会', '有什么套利机会', '怎么赚钱', '有什么好的策略', '帮我理财', '有什么收益机会', 'yield opportunities', 'how to earn', 'investment strategy', 'DeFi 策略推荐', '有什么自动化策略', 'automated strategies', 'passive income on-chain', '链上怎么赚币', '怎么玩链上', '怎么玩DeFi', '链上有什么玩法', '有什么赚钱项目', '推荐一些策略'.\nPlugin/project discovery: 'Plugin商店有什么', '有什么Plugin', '有什么项目', '什么项目最火', '最热门的项目', '有哪些工具', '推荐一些项目', 'what plugins are available', 'show me plugins', 'what projects are hot', 'trending projects', 'plugin marketplace', 'Plugin市场', '有什么好用的Plugin'.\nCapability discovery: '你能做什么', '你有什么能力', '你支持什么', '有什么技能', '都有什么功能', '支持哪些策略', '支持哪些 skill', 'what skills are available', 'what can you do', 'what strategies do you support', 'show me all strategies', 'list all skills'.\nDApp discovery: 'what dapps are available', 'any good dapps', '有什么好的dapp', '推荐一些dapp', 'recommend dapps', 'show me dapps', 'which protocols can I use', '有什么好的协议', '有什么DeFi协议', '推荐DeFi项目', '有什么链上应用'.\nPlugin management: 'install a plugin', 'uninstall a plugin', 'list plugins', 'search plugins', 'update plugins', 'show installed', '安装Plugin', '卸载Plugin', '更新Plugin'.\nPlugin development: 'I want to create a plugin', 'how to submit a plugin', '我想开发一个Plugin', '怎么提交Plugin', '如何贡献', 'contribute a plugin', 'build a plugin', 'create a skill', '创建Plugin', '开发Plugin', 'hackathon', 'submit my project', '提交我的项目'.\nAlso activates when the skill has just been installed and the user has not yet chosen a direction.
Use when implementing, integrating, upgrading, debugging, or answering anything involving third-party APIs, libraries, frameworks, CLIs, cloud services, model/provider SDKs, fast-moving product behavior, user requests for latest/current/official behavior, unfamiliar repo docs/specs, errors that may indicate API drift, or high-stakes auth, security, billing, data, migration, deployment, compliance, or privacy behavior. Forces Codex to web-search for current official docs and read primary docs before assuming from memory.
Ultra-compressed communication mode. Cuts token usage ~75% by dropping filler, articles, and pleasantries while keeping full technical accuracy. Use when user says "caveman mode", "talk like caveman", "use caveman", "less tokens", "be brief", or invokes /caveman.
Master context engineering for AI agent systems. Use when designing agent architectures, debugging context failures, optimizing token usage, implementing memory systems, building multi-agent coordination, evaluating agent performance, or developing LLM-powered pipelines. Covers context fundamentals, degradation patterns, optimization techniques (compaction, masking, caching), compression strategies, memory architectures, multi-agent patterns, LLM-as-Judge evaluation, tool design, and project development.
Migrate browser-use (Python) browser-automation scripts to Stagehand v3 (TypeScript) on Browserbase. Use when the user wants to convert, port, rewrite, or migrate a browser-use Agent script to Stagehand, map browser-use features/APIs to Stagehand primitives (act/extract/observe/agent), or move agentic browser automation onto Browserbase with more determinism. Triggers on "browser-use", "browser_use", or "Agent(task=...)".
Register the durable `cheese-durable` Hallouminate corpus. Repair the durable corpus when its configuration changes. Register the repository tenant. Repair the repository tenant when its configuration changes. Use this skill for requests to set up or repair a durable corpus, register a repository tenant, or when the user invokes /easy-cheese-setup. Use the wiki skills for general Hallouminate wiki work.
Connect to the EvoMap collaborative evolution marketplace. Publish Gene+Capsule bundles, fetch promoted assets, claim bounty tasks, and earn credits via the GEP-A2A protocol. Use when the user mentions EvoMap, evolution assets, A2A protocol, capsule publishing, or agent marketplace.
Generates images and text via reverse-engineered Gemini Web API. Supports text generation, image generation from prompts, reference images for vision input, and multi-turn conversations. Use when other skills need image generation backend, or when user requests "generate image with Gemini", "Gemini text generation", or needs vision-capable AI generation.
Build type-safe LLM applications with DSPy.rb — Ruby's programmatic prompt framework with signatures, modules, agents, and optimization. Use when implementing predictable AI features, creating LLM signatures and modules, configuring language model providers, building agent systems with tools, optimizing prompts, or testing LLM-powered functionality in Ruby applications.
Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.
Operate Manta-managed worktrees, folder contexts, terminals, repos, automations, artifacts, skill sharing, worktree comments, and Manta's embedded browser through the `manta` CLI. Use when the user says "$manta-cli", "Manta worktree", "child worktree", "spawn codex/claude in a worktree", "read/wait/send Manta terminal", "handoff" / "handover" / "give this to another agent", "Manta browser", "manta artifacts", or "share skills". Prefer it over raw git worktree, ad hoc PTYs, or Computer Use when Manta state is involved. Use Computer Use only when a visible window needs GUI control that a CLI, filesystem, or API cannot do.
Guides a contributor from a workflow idea to a complete Workflow Implementation Packet (draft workflow file, draft workflow command, cross-cutting update checklist) in a staging area for review. Runs overlap analysis against the existing workflows with a Why Gate, then helps select and sequence skills with authored handoffs. Use when creating a new multi-skill workflow or promoting a repeated ad-hoc chain into a durable one. To build a single skill instead, use utility-pm-skill-builder; to run a sequence without authoring anything, use the chain command or utility-pm-workflow-orchestrator
Safari browser automation CLI on macOS via safari-mcp. Controls real Safari (native, keeps logins) by wrapping the safari-mcp MCP server. Every one of the 84 MCP tools is exposed 1:1 with schema-accurate arguments — guaranteed parity, no manual drift.
Use this skill when the user asks to “create a hedera plugin,” “build a hedera agent kit plugin,” “extend hedera agent kit,” “create custom hedera tools,” “add hedera functionality,” “write a hedera tool,” or “implement hedera tool,” or needs guidance on Hedera Agent Kit plugin architecture, tool definitions, mutation tools, query tools, or parameter schemas using Zod.
Codify the discipline-section guidance, the CSO description rule, and the size-budget checklist that easy-cheese skills follow when authoring or revising a skill. Use when writing a new skill or SKILL.md, adding a discipline section, or reviewing an existing skill against easy-cheese's authoring conventions.
Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance. The challenge isn't making them capable - it's making them reliable. Every extra decision multiplies failure probability. This skill covers agent loops (ReAct, Plan-Execute), goal decomposition, reflection patterns, and production reliability. Key insight: compounding error rates kill autonomous agents. A 95% success rate per step drops to 60% b
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.