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概要と使いどころ
Collective research memory for AI coding agents — search prior research before hitting the web, save findings back to the shared cache, view karma stats. Use when the user asks a library/API/framework question, before running WebSearch/WebFetch, or after deep web research so the next agent doesn't repay the cost. Talks directly to the hosted MCP endpoint via JSON-RPC — no local MCP server required.
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。
⭐ ПРАВИЛО (anton 23.09 голосом): «подними сессию/декомпозируй» адресовано ТЕБЕ = поднимай У СЕБЯ, своими средствами (Cursor → Task-tool/background agents, Codex → свои сессии, Claude → планировщик); нести задачу в другую LLM = ересь. Мост spawn_queue → Claude-планировщик — ТОЛЬКО когда нужна именно видимая scheduled-задача приложения. Триггеры: «подними сессии», «декомпозируй на сессии», lift-spawn.
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。
Protocol for two LLM agents (Claude, Codex or any other) to agree and work from one shared folder: message format, channels, roles, evidence rules, wake-up alarm and anti-patterns. Triggers: /llm-pact, agree with codex, connect a second agent, pair rules, agent consensus, two-agent protocol.
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。
Load the operations codex that governs everyone acting as or for the owner (the owner, human assistants and AI agents) before outreach, replies in their chats, scheduling, purchases, hiring or household ops. Covers where the rules live in the vault, how to pull the right slice, and the rule that the newest entry wins per topic. Triggers: "/bible", "what do the rules say".
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。
Senior engineer CLI expertise for AI agents — workflows, safety guardrails, gotchas, and anti-patterns across cloud, IaC, containers, databases, dev tools, and platforms
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。
Route a task through SIPS commands, agents, scripts, MCP tools, or bounded delegation. Use when asked to route, split, fan out, escalate, or choose the right SIPS path.
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 BACKPRESSURE.md file for any project. Analyzes codebase and PRD to produce deterministic self-verification checks that AI coding agents run on their own output.
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 custom agent backends, implementing the AG-UI protocol, debugging streaming issues, or understanding how agents communicate with frontends. Covers event types, SSE transport, AbstractAgent/HttpAgent patterns, state synchronization, tool calls, and human-in-the-loop flows.
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 AI-powered features with CopilotKit v2 -- adding chat interfaces, registering frontend tools, sharing application context with agents, handling agent interrupts, and working with the CopilotKit runtime.
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。
Customize WP Rocket behavior from a third-party plugin / theme via filter hooks — exclude URIs / cookies / user agents / REST API namespaces from caching, configure CDN URL rewrites, extend lazy load handling, override capability requirements, hook into Action Scheduler integration. Critical guidance — rocket_cache_reject_uri takes URI patterns (regex-like), NOT full URLs; rocket_cache_reject_* filters all expect arrays. The rocket_buffer filter is the FULL HTML output filter — extremely powerful but dangerous; one fatal error in the callback breaks every cached page until WP Rocket is disabled. Use when extending WP Rocket's default rules, NOT for cache invalidation (see wp-rocket-cache-invalidation). Triggers on rocket_cache_reject_, rocket_cdn_, do_rocket_lazyload, rocket_buffer, rocket_capacity, "exclude from WP Rocket cache".
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。
Register WordPress Abilities: machine-readable plugin operations with JSON Schema contracts, required permission callbacks, optional REST exposure, and AI/MCP-friendly discovery. Covers categories, wp_register_ability, WP_Ability::execute, REST run endpoints, meta.show_in_rest, annotations, and Ability vs REST route vs custom hook decisions. Use when exposing plugin functionality to agents, admin JS, external tools, or reviewing AI integration code.
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。
Main application building orchestrator. Creates full-stack applications from natural language requests. Determines project type, selects tech stack, coordinates 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。
Build natural-language crypto/DeFi agents and EVM MCP plugins (Claude Code, Cursor, Codex, Gemini). Aomi turns prompts into wallet-signed txs on Ethereum, Base, Arbitrum, Optimism, Polygon, Linea —
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。
Stop coding agents from shipping generic UI. Extend the product's design system, use UIZZE evidence only when useful, cover required states, and inspect the rendered result.
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。
Configure and orchestrate parallel agents using the standalone Antigravity 2.0 Agent Manager and Antigravity IDE.
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-descriptio
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。
Secure AI coding agents (Claude Code, Cursor, Codex, Copilot) with permission boundaries, secret protection, code review gates, and safe sandbox configurations for team environments.
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。
Secure AI agents against prompt injection, tool abuse, and data exfiltration with defense-in-depth controls.
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。
Tools are how AI agents interact with the world. A well-designed tool is the difference between an agent that works and one that hallucinates, fails silently, or costs 10x more tokens than necessary.
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 hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).
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 automated evaluation suites for AI agents using golden datasets, rubrics, and regression gates. Use when shipping agent features, validating prompt changes, or gating deployments on quality.
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。
Skill for discovering and researching autonomous AI agents, tools, and ecosystems using the AgentFolio directory.
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。