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Build AI-powered browser agents that autonomously navigate, scrape, fill forms, and extract data from dynamic websites using LLM reasoning + Playwright. Use when building a scraper that must survive site redesigns, an agent that completes multi-step web workflows, or a replacement for brittle Selenium/Puppeteer scripts.

日本語の概要は準備中です。原文の説明を表示しています。

Scaffolds a headless agent in TypeScript using @openrouter/agent and Bun — for CLI tools, API servers, queue workers, and pipelines. No terminal UI. Use when building an OpenRouter-based agent that runs without a terminal UI, such as a CLI tool, HTTP service, or queue worker.

日本語の概要は準備中です。原文の説明を表示しています。

Check any AI agent codebase against the OWASP Agentic Security Initiative (ASI) Top 10 risks. Use this skill when: - Evaluating an agent system's security posture before production deployment - Running a compliance check against OWASP ASI 2026 standards - Mapping existing security controls to the 10 agentic risks - Generating a compliance report for security review or audit - Comparing agent framework security features against the standard - Any request like "is my agent OWASP compliant?", "check ASI compliance", or "agentic security audit"

日本語の概要は準備中です。原文の説明を表示しています。

Also use for agentic data engineering pipelines, code generation agents, agent-powered CLI tools, MCP server development, or any system where an AI model calls tools, makes decisions, or loops

日本語の概要は準備中です。原文の説明を表示しています。

Generates a complete AGENTS.md at the repository root following the agents.md format: setup, build, test, style, and PR conventions coding agents need. Use when a repo has no AGENTS.md, when onboarding coding agents to a codebase, or when the existing agent instructions are stale.

日本語の概要は準備中です。原文の説明を表示しています。

Builds AI-powered trading and financial analysis agents with multi-agent LLM architectures: research pipelines, sentiment signals, portfolio optimization, backtesting, risk guardrails, and market monitoring. Use when designing or implementing an LLM-based trading or market-analysis system.

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。

Use when diagnosing agent benchmarks or runtime failures from trajectories instead of aggregate pass rates. Builds shared action-observation decision landscapes, identifies productive cores and trap regions, profiles Access/Trap/Repair behavior, and designs conservative trap-triggered recovery experiments.

日本語の概要は準備中です。原文の説明を表示しています。

Patterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when: - Building AI agents that call external tools (APIs, databases, file systems) - Implementing policy-based access controls for agent tool usage - Adding semantic intent classification to detect dangerous prompts - Creating trust scoring systems for multi-agent workflows - Building audit trails for agent actions and decisions - Enforcing rate limits, content filters, or tool restrictions on agents - Working with any agent framework (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)

日本語の概要は準備中です。原文の説明を表示しています。

Use when designing evaluation suites for agentic systems, choosing agent benchmarks, measuring multi-agent performance, or critiquing single-score evaluation of agents. Based on arXiv:2605.26112v1. Trigger phrases: "evaluate my agent", "benchmark agent", "measure agent reliability", "pass^k evaluation", "agent metrics beyond success rate", "trajectory quality", "memory hygiene metric", "longitudinal agent eval", "why my agent scores well on benchmarks but fails in production".

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。

Use when designing long-horizon agents that need subagents, isolated context windows, filesystem-backed work, persistence, human approval, MCP tools, or production tracing.

日本語の概要は準備中です。原文の説明を表示しています。

Use when executing a previously agreed autonomous agent plan — running the steps within declared boundaries, checkpointing progress, and escalating when a stop condition is hit.

日本語の概要は準備中です。原文の説明を表示しています。

Turns a structured-autonomy PR plan (plans/{feature}/plan.md) into copy-paste-ready implementation documentation with complete code for every step, saved as implementation.md. Use after a plan exists and before implementation, when each step needs fully specified changes.

日本語の概要は準備中です。原文の説明を表示しています。

Scaffolds a complete agent TUI in TypeScript using @openrouter/agent — like create-react-app for terminal agents. Generates a customizable terminal interface with three input styles, four tool display modes, ASCII banners, streaming output, session persistence, and configurable tools. Use when building an agent, creating a TUI, scaffolding an agent project, or building a coding assistant.

日本語の概要は準備中です。原文の説明を表示しています。

Use when designing retryable or crash-resilient agent workflows, especially when tools can send, charge, publish, provision, or otherwise create duplicate side effects.

日本語の概要は準備中です。原文の説明を表示しています。

Routes requests to the BMad Method (BMM) agile framework's four phases — analysis, planning, solutioning, implementation — and their persona and workflow sub-skills. Use when the user mentions BMad or BMM, or wants a structured agile flow from research and PRD through architecture, stories, and delivery.

日本語の概要は準備中です。原文の説明を表示しています。

Use when turning expert materials, execution traces, repository evidence, or long-form documents into an inspectable, correctable, and empirically verified agent skill. Covers source governance, capability-vs-behavior separation, contrastive induction from successes and failures, intervention testing, versioning, rollback, and task-conditioned vs. reusable-library evaluation.

日本語の概要は準備中です。原文の説明を表示しています。

Use when building Hugging Face smolagents, code-executing agents, Python-action agents, Hub-shared tools, or lightweight agent prototypes that need sandboxing and provider flexibility.

日本語の概要は準備中です。原文の説明を表示しています。

Use when designing or auditing an agentic AI system holistically. Activates the six-component harness framework (ℛ Reasoning, ℳ Memory, 𝒞 Context, 𝒮 Skills, 𝒪 Orchestration, 𝒢 Governance) from arXiv:2605.26112. Trigger phrases: "design an agent system", "why is my agent unreliable", "harness architecture", "system-level agent design", "beyond model scaling", "agentic infrastructure", "scale my agent system", "agent performance bottleneck".

日本語の概要は準備中です。原文の説明を表示しています。

Use when building a Python terminal UI inspired by Claude Code with Rich. Translates a detailed TypeScript/Ink-style visual reference into a staged Python architecture for logo, message stream, streaming Markdown, tools, diffs, prompt, footer, spinners, permissions, transcript mode, themes, accessibility, and tests. Separates Rich rendering from the input and widget responsibilities that require an application-owned event loop or an optional Textual layer.

日本語の概要は準備中です。原文の説明を表示しています。

Verify supply chain integrity for AI agent plugins, tools, and dependencies. Use this skill when: - Generating SHA-256 integrity manifests for agent plugins or tool packages - Verifying that installed plugins match their published manifests - Detecting tampered, modified, or untracked files in agent tool directories - Auditing dependency pinning and version policies for agent components - Building provenance chains for agent plugin promotion (dev → staging → production) - Any request like "verify plugin integrity", "generate manifest", "check supply chain", or "sign this plugin"

日本語の概要は準備中です。原文の説明を表示しています。

This skill should be used when the user asks to "share memory between agents", "KV cache compaction for multi-agent", "orchestrator worker context", "latent briefing", "reduce worker tokens", "cross-agent memory without summarization", or discusses Attention Matching compaction, recursive language models with workers, or token explosion in hierarchical agents.

日本語の概要は準備中です。原文の説明を表示しています。

Use when designing scalable MCP tool routing, dynamic tool retrieval, agent-tool graphs, tool inventory indexing, or routing policies for agents with many available tools.

日本語の概要は準備中です。原文の説明を表示しています。