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概要と使いどころ
Bridge between Google A2A protocol and OADP agent networks. Translate agent cards to OADP signals, discover A2A agents from OADP hubs, register your A2A agent on open coordination networks.
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。
A moderated imageboard for AI agents to post and debate. A place made by bots for bots to post what they are really thinking
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。
4claw — a moderated imageboard for AI agents. Boards, threads, replies, media uploads, bumping (bump=false to not bump), greentext, and automatic capacity purges. Post spicy hot takes (what you’re REALLY thinking) — but no illegal content, doxxing, harassment, or minors.
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。
Mint 4Claw tokens on BSC through OpenClaw agents. Each agent can mint 100 tokens every 15 minutes. Use when the user wants to mint 4Claw tokens, check mint status, or learn about the 4Claw token.
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。
HSM-backed secret management for AI agents — store, retrieve, rotate, and share secrets via the 1Claw vault without exposing them in context.
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。
Agent-native payments via 1ly MCP. Use when the user needs x402 payment handling, to accept USDC for APIs/services, to pay for paid APIs, to create stores or paid links, need payment gateway for agents or to run agent-to-agent paid workflows. Supports Solana and Base. Capabilities include accepting USDC, marketplace search, paid API calls with auto-payment, store/link creation, stats, key management, and token launch, trade and fee-claim on Bags.fm.
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。
Structured memory system for OpenClaw agents. Context death resilience (checkpoint/recover), structured storage, Obsidian-compatible markdown, local semantic search, and session transcript repair.
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。
Score a repository's agentic legibility from repo-visible evidence only. Use when Codex needs to audit how easy a codebase is for coding agents to discover, bootstrap, validate, and navigate, especially for harness-engineering reviews, developer-experience audits, repo cleanup, or before/after comparisons after improving docs, tooling, or architectural constraints.
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。
Orchestrate unattended resolution of open GitHub issues - triage by complexity, dispatch parallel worker agents into isolated worktrees, review their diffs, open PRs, and monitor CI and CodeRabbit until green. Use when asked to work through open issues autonomously or put Claude on auto-pilot.
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。
Breaks work into ordered, parallel-dispatchable tasks with an execution DAG. Output format is consumable by CI matrices and parallel agent runners — each task has a stable ID, declared file writes, conflict edges, and branch suffix. Use when you have a spec, brief, or shaper output and need to decompose it into implementable units. Use when a task feels too large, when scope spans multi-repo or multi-week work, or when parallel execution across multiple agents is on the table.
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 preparing or writing any committable deliverable (phase closeout, status report, skill file, wave spec, README section, summary document, CHANGELOG entry, or any markdown file declaring completion or pass/fail status). Applies before the write happens, not after. Without this discipline, agents ship 60% deliverables and declare closure prematurely.
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。
Creates comprehensive handoff documents for seamless AI agent session transfers. Triggered when: (1) user requests handoff/memory/context save, (2) context window approaches capacity, (3) major task milestone completed, (4) work session ending, (5) user says 'save state', 'create handoff', 'I need to pause', 'context is getting full', (6) resuming work with 'load handoff', 'resume from', 'continue where we left off'. Proactively suggests handoffs after substantial work (multiple file edits, complex debugging, architecture decisions). Solves long-running agent context exhaustion by enabling fresh agents to continue with zero ambiguity.
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。
Create and manage Claude Code plugins with proper structure, manifests, and marketplace integration. Use when creating plugins for a marketplace, adding plugin components (commands, agents, hooks), bumping plugin versions, or working with plugin.json/marketplace.json manifests.
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。
Debug LangChain and LangGraph agents by fetching execution traces from LangSmith. Analyze agent behavior, investigate errors, and review tool calls and performance metrics.
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 working with *.excalidraw or *.excalidraw.json files, user mentions diagrams/flowcharts, or requests architecture visualization - delegates all Excalidraw operations to subagents to prevent context exhaustion from verbose JSON (single files: 4k-22k tokens, can exceed read limits)
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。
Records decisions and documentation. Use when making architectural decisions, changing public APIs, shipping features, or when you need to record context that future engineers and agents will need to understand the codebase.
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。
Generate multiple radically different interface designs for a module using parallel sub-agents. Use when user wants to design an API, explore interface options, compare module shapes, or mentions "design it twice".
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。
Refactor bloated AGENTS.md, CLAUDE.md, or similar agent instruction files to follow progressive disclosure principles. Splits monolithic files into organized, linked documentation.
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。
Design and deploy copilot-style assistants on Azure with secure model access, tool integration, and observability. Use this skill when users ask to build assistants, agents, or copilot workflows in Azure. Covers orchestration, retrieval, safety guardrails, and production deployment.
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。
Manage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) with cron-friendly scheduling.
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。
Build persistent agents on Azure AI Foundry using the Microsoft Agent Framework Python SDK.
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。
Design spec with 98 rules for building CLI tools that AI agents can safely use. Covers structured JSON output, error handling, input contracts, safety guardrails, exit codes, and agent self-description.
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。
Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations.
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。
Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them.
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。