22.8K skills · sorted by relevance
Automate Agent Mail operations through Rube MCP, a connection between an AI agent and external tools. The skill finds available actions, verifies the service connection, and executes workflows using the input requirements returned by tool discovery.
Design specialized assistants that work with their own separate context. Define responsibilities, delegation conditions, working steps, and report formats for reusable subagents handling verification, debugging, security audits, or code reviews.
Set up monitoring for deployed agents to inspect execution paths, latency, and model interactions. It explains Cloud Trace, prompt and response logging, BigQuery analytics, and external monitoring integrations, with checks for missing data and unwanted content capture.
Enhancement overlay for multi-agent / tool-using coder agents. Encodes the per-agent tool- scoping discipline that role-based frameworks (CrewAI, LangChain) document only as a passing best-practice: which agent gets which tool, and why blanket-sharing every tool to every agent is a correctness and blast-radius risk. Activates when an agent system has tools AND there is more than one agent (or one agent holding many tools). Treat a tool as a capability grant; scope by least-privilege. ENHANCE overlay — read alongside [[crewai]], [[agentsop-http-tool-wrapping]], [[agentsop-llm-tool-idempotency]]. Search keywords: which tools per agent, least-privilege agent, agent tool access, tool permissions, limit agent tools, scope tools to roles.overlay_type: enhancement
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Cross-framework enhancement overlay for choosing a multi-agent topology BEFORE writing any agent. A binary-question rubric — is single-agent + tools enough? do agents need to know about each other? does the output need one voice? — maps the answer to single-agent / supervisor / swarm / sequential / hierarchical. Activates when a coder agent is tempted to "split the work into roles" or reaches for a multi-agent framework. Encodes the *selection rubric* that the per-framework skills assume but never surface. Search keywords: when to use multi-agent, single vs multi agent, do I need multiple agents, supervisor vs swarm, multi-agent vs single agent, agent team design.
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AWS Bedrock AgentCore comprehensive expert for deploying and managing AI agents at scale. Use when working with any AgentCore service including Gateway, Runtime, Memory, Identity, Code Interpreter, Browser, Observability, Agent Registry, or Evaluations. Covers agent deployment, MCP tool integration, credential management, agent discovery, governance workflows, and automated quality assessment. Essential when user mentions AgentCore, agent runtime, agent registry, agent evaluation, MCP gateway, deploy agent, register MCP server, discover agents, evaluate agent quality, agent credentials, or wants to build, deploy, catalog, or monitor AI agents on AWS.
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Build, modify, audit, repair, optimize, debug, and deploy agents with Agentforce Agent Script. TRIGGER when: user creates, reviews, or changes .agent files or aiAuthoringBundle metadata; asks to fix AgentScript, audit an existing agent, run an AgentScript health check, common-pitfall review, or baseline-versus-candidate repair loop; changes a response, action, subagent, route, state flow, or Agent Spec; previews, debugs, deploys, publishes, or tests agents; uses sf agent generate/preview/publish/test; or manages Agentforce MCP servers, tools, assets, or authentication. DO NOT TRIGGER when: Apex, Flow, Prompt Template, Experience Cloud, or general Salesforce CLI work is unrelated to Agent Script; or the primary input is a production session or trace ID rather than an agent artifact.
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Use this skill when architecting new Claude Code agents following Anthropic best practices. Guides through YAML frontmatter creation, agent structure definition, role boundaries, and validation. Critical for creating minimal agent files (~150 lines) with proper skill invocations. Examples: <example>Context: User wants to create a new agent for security auditing user: "Create a security-auditor agent" assistant: "I'll use the agent-creator skill to build this agent following Anthropic best practices" <commentary>New agent creation requires the agent-creator skill to ensure proper structure and YAML frontmatter.</commentary></example> <example>Context: Existing agent file is too verbose and needs refactoring user: "The architect agent is 2000 lines, refactor it to minimal structure" assistant: "I'll use the agent-creator skill to refactor this into a minimal ~150 line agent with a separate skill file" <commentary>Agent refactoring requires the agent-creator skill to separate identity from technical procedures.</commentary></example>
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Build custom AI agents in Claude Code from a user's problem statement. This skill analyzes the user's use case, asks smart clarifying questions, researches the internet for similar agents (GitHub repos, blogs, Claude Code community patterns), and then architects and builds production-ready Claude Code agents — including subagents, skills, hooks, slash commands, MCP integrations, and CLAUDE.md configuration. Use this skill whenever the user wants to create an agent, build an automation workflow, set up a Claude Code subagent, design a multi-agent system, or says things like 'build me an agent for...', 'automate this with Claude Code', 'I want a subagent that...', 'help me create a workflow', 'set up an agent pipeline', or any variation of wanting Claude Code to do something specialized. Also trigger when the user mentions agent architecture, agent SDK, agentic workflows, or task delegation in Claude Code — even if they don't use the word 'agent' explicitly.
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Specialized agentic and context engineering prompt methodology for Claude. Use when designing AI agents — system prompts, tool definitions, context window architectures, failure mode planning, multi-agent coordination, agent evaluation. Trigger on: "design an agent," "build a system prompt for an agent," "architect an agent's context," "design tool definitions," "agent system prompt," "context window architecture," "tool interface design," "evaluate agent behavior," "multi-agent coordination," "context engineering for agents," "ACI design." Provides 12 tested approaches for agent architecture (designing agents, not using them). Do NOT use for evaluating or scoring individual non-agent prompts (use rootnode-prompt-validation if available). Do NOT use for general software architecture that hosts agents (use standard technical reasoning for that).
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Designing or reviewing Agentforce actions: Flow actions, Apex invocable actions, prompt-template actions, action naming, input and output contracts, confirmation requirements, and safe error behavior. Triggers: 'agent actions', 'build an agent that can look up an order', 'make the agent able to check order status', 'give the agent a capability', 'agent that can retrieve a record', 'flow action for agent', 'agent invocable action', 'action schema design'. NOT for writing the Apex class itself — use agentforce/custom-agent-actions-apex. NOT for the reason_code error envelope that stops an agent retry loop — use agentforce/agent-action-error-handling.
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Use this skill when designing, auditing, or troubleshooting behavioral boundaries for Agentforce agents — including topic (now subagent) Scope fields, agent-level system instructions, topic/action filters, the Escalation topic, restricted topics, and Instruction Adherence monitoring. Trigger keywords: agent guardrails, agent out of scope, restrict agent behavior, topic scope, subagent scope, agent fallback, Escalation topic, abuse prevention, action filters. NOT for Trust Layer content filtering (Einstein Trust Layer is a separate product concern), NOT for general topic routing design (use agentforce/agent-topic-design), NOT for prompt template construction (use agentforce/prompt-builder-templates).
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An Agentforce agent is moving from build or sandbox to live end-user traffic and the team needs a comprehensive readiness gate: coverage testing, Trust Layer config, guardrails, cost telemetry, observability, rate limits, permissions, rollout strategy, rollback plan, performance benchmarks. Triggers: 'we want to ship our Agentforce agent next week', 'pre-prod readiness review for our Service Agent', 'what do we need before turning the agent on for real customers', 'agent went live and is hallucinating, what should we have caught', 'cost monitoring for our internal sales agent', 'rollout strategy from internal pilot to GA'. NOT for the lighter five-block activation sign-off artifact — use agentforce/agent-deployment-checklist. NOT for authoring the guardrails themselves — use agentforce/agentforce-guardrails. NOT for building the test harness — use agentforce/agentforce-eval-harness. NOT for Trust Layer feature setup in isolation — use agentforce/einstein-trust-layer.
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Conversation patterns and interaction protocols for multi-agent systems. Covers request/response, pub/sub, blackboard, delegation chains, debate, critique, consensus, fan-out/fan-in, supervisor-worker, and peer negotiation. Deep analysis of AutoGen conversation patterns, CrewAI delegation, LangGraph state passing, and FIPA-ACL performatives. Teaches how to design what agents say to each other and in what order. Activate on: "agent conversation", "agent protocol", "multi-agent debate", "agent delegation", "supervisor worker pattern", "agent voting", "consensus protocol", "fan-out fan-in", "agent negotiation", "blackboard pattern", "agent dialogue", "conversation topology", "agent handoff". NOT for: wire format or serialization (use agent-interchange-formats), orchestration infrastructure (use agentic-infrastructure-2026), single agent behavior (use agentic-patterns).
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Build and adopt production AI agent infrastructure in 2026. Covers framework selection (LangGraph, CrewAI, AutoGen, MCP), orchestration patterns, evaluation, observability, memory systems, and tool use. Also covers the SOCIAL dimension: how to sell agent infrastructure internally, change management, measuring ROI, building trust in autonomous systems, and scaling adoption across teams. Activate on: "agent infrastructure", "agent framework comparison", "which agent framework", "sell AI tools internally", "agent adoption", "agent observability", "agent evaluation", "MCP architecture", "agentic mesh", "enterprise AI agents", "AI change management", "agent ROI". NOT for: building specific agents (use ai-engineer), designing agent behavior patterns (use agentic-patterns), prompt tuning (use prompt-engineer).
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Data structures and serialization formats for agent-to-agent communication. Covers message envelopes, structured output schemas, capability declarations, task handoff payloads, error/retry signaling, and context windows as data structures. Deep comparison of A2A protocol, MCP, OpenAI function calling, and LangChain message types. Teaches when to use rigid schemas vs free-form with validation, typed vs untyped, streaming vs batch. Activate on: "agent message format", "agent communication schema", "agent-to-agent protocol", "A2A protocol", "MCP message format", "structured output for agents", "agent interop", "interchange format", "agent serialization", "task handoff format", "capability declaration". NOT for: what agents say to each other (use agent-conversation-protocols), orchestration topology (use multi-agent-coordination), building agent infrastructure (use agentic-infrastructure-2026).
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Claude Code（.claude）と Codex（.codex / .agents / AGENTS.md）に分裂したエージェント設定を、 実ファイル1つ + symlink と双方向変換で単一化・同期するスキル。 実ファイルをどちら側に置くか（symlink の向き）は共通設定 source で選べる。 プロジェクト単位の導入ウィザード、既に分裂しているリポジトリを危険度の低い順に解決する手順、 片方しか無いリポジトリへもう片方を生成する scaffold、書き換え前のバックアップと復元、常駐の管理を含む。 「エージェント設定を同期して」「.codex と .claude がずれている」「Codex でも同じスキルを使いたい」 「AGENTS.md と CLAUDE.md を一本化したい」「MCP を Codex でも使えるようにして」 「サブエージェントを Codex 側にも作って」「設定の分裂を解消して」「元に戻して」 「agents-sync」「同期デーモン」などと言われたら使う。 Keywords: agents-sync, エージェント設定同期, .claude, .codex, .agents, AGENTS.md, symlink 統合, symlink の向き, 実ファイルを置く側, 双方向同期, scaffold, 分裂解消, MCP 同期, hooks 同期, サブエージェント同期, バックアップ, 復元, fswatch, launchd, 常駐デーモン
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Connect RuView sensing data to AI agents through tools for presence, breathing, and heart rate. Explore agent coordination and prototype storing agent state and decisions alongside sensing data in a shared RVF container.
Design and build AI agents for business, research, operations, or creative work. Define essential actions and knowledge, start with a simple execution loop, and add progress tracking or separate child agents as needs emerge.
Automate Agentql operations through Rube MCP, a connection for agent tools. The skill covers discovering available actions, checking authentication, and executing tasks with current input requirements, including bulk operations and paginated results.
Plan and review systems where several AI agents work together. Assess whether multiple agents are useful, choose roles and communication methods, and define shared files, verification, and resource limits to address conflicts, spreading errors, and uncontrolled execution.
Set up Antigravity 2.0 Agent Manager alongside Antigravity IDE to coordinate parallel development agents. Assign roles and folder boundaries, review their changes in the editor, and use Git checkpoints to manage shared work.
Add controls to AI agents that use APIs, databases, and files. Use Python patterns for tool permissions, approval requirements, call limits, suspicious input checks, audit logs, and trust scores that guide delegation between agents.
Create a dedicated test project linked to the current worktree and check Agent Builder through APIs and browser interactions. It covers agent and skill management, permissions, defaults, and related features, with selectable test sections.