Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability.
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。
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概要と使いどころ
Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability.
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。
Expert C4 Container-level documentation specialist. Synthesizes Component-level documentation into Container-level architecture, mapping components to deployment units, documenting container interfaces as APIs, and creating container diagrams. Use when synthesizing components into deployment containers and documenting system deployment architecture.
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。
Expert C4 Code-level documentation specialist. Analyzes code directories to create comprehensive C4 code-level documentation including function signatures, arguments, dependencies, and code structure. Use when documenting code at the lowest C4 level for individual directories and code modules.
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。
Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration.
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 code refactoring context restore
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。
Pre-pipeline aggregator that scans AI agent cache directories (.claude, .cursor, .antigravity, .openclaw) or any user-specified directory for experimentation logs, extracts insights and numeric results, and formats them as PaperOrchestra-ready inputs (idea.md + experimental_log.md). TRIGGER when the user says "aggregate my agent logs for paper writing", "extract experiments from my coding agent history", "prepare PaperOrchestra inputs from my cache", "turn my agent logs into a paper", mentions a folder or directory they want to use as the basis for a paper, or wants to run PaperOrchestra but only has scattered agent experiment histories rather than structured inputs. Run this BEFORE paper-orchestra. Also called automatically by paper-orchestra when workspace/inputs/idea.md or workspace/inputs/experimental_log.md are missing.
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。
Convert long-form Markdown (plan, spec, system design, RFC, runbook, postmortem, brainstorm, notes) into a single self-contained HTML page with Mermaid diagrams, step timelines, callouts, sidebar TOC. Claude-orange light+dark theme. Multi-language. Portable across Claude Code / Codex / Antigravity / any AI agent.
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。
Connect OpenConstructionERP to AI coding assistants via MCP (Model Context Protocol): expose costs, BOQ, BIM and catalog endpoints as MCP tools so Claude Code / Antigravity / OpenCode can drive the platform. Use when wiring an assistant to the ERP.
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。
Forensic root cause analyzer for Antigravity sessions. Classifies scope deltas, rework patterns, root causes, hotspots, and auto-improves prompts/health.
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。
Core UI/UX engineering skill for building highly interactive, spatial, weightless, and glassmorphism-based web interfaces using GSAP and 3D CSS.
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 meta-skill that understands task requirements, dynamically selects appropriate skills, tracks successful skill combinations using agent-memory-mcp, and prevents skill overuse for simple tasks.
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 asked to ship a SaaS MVP, audit application security, build an AI agent, run browser QA, or design a domain model with multiple skills and verified checkpoints.
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。
Run protected AAS maintainer sweeps, PR merge batches, canonical sync, Core preview checks, and scripted releases. Use for repository maintenance, main alignment, CLI/MCP/Workbench changes, or release work; not ordinary contribution tasks.
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。
Delegate coding tasks to the Google Antigravity CLI (`agy`) only when the user explicitly requests it, while the orchestrator retains review and landing responsibility.
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 persistent coding sessions across Claude Code, Codex, Antigravity (agy), Cursor, and OpenCode engines. Use when orchestrating multi-engine coding agents, starting/sending/stopping sessions, running multi-agent council collaborations, cross-session messaging, ultraplan deep planning, ultrareview parallel code review, autoloop autonomous workspace iteration, ultraapp building deployable web apps from a structured Q&A interview, switching models/tools at runtime, exposing the orchestrator's 69 tools as an MCP server to Hermes Agent / Claude Desktop / Cursor / Cline / Continue / Zed / Windsurf / Goose, or running as an Agent Client Protocol (ACP) agent that Zed / JetBrains / Neovim / Emacs / VS Code / dsh can drive directly. Triggers on "start a session", "send to session", "run council", "ultraplan", "ultrareview", "autoloop", "ultraapp", "Forge tab", "build a web app", "one-click app", "AppSpec", "autonomous iteration", "iterate until goal", "deep paper review", "auto research", "switch model", "multi-agent", "coding session", "session inbox", "cursor agent", "opencode", "mcp server", "clawo-mcp", "hermes mcp", "model context protocol", "ultracode", "dynamic workflow", "fanout", "fan-out", "best-of-N", "steer turn", "interrupt turn", "fork thread", "rollback turns", "acp", "agent client protocol", "clawo acp", "zed agent", "jetbrains agent", "external agent", "dsh subagent", "deepseek harness".
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。
Expert in launching small, focused SaaS products fast - the indie hacker approach to building profitable software. Covers idea validation, MVP development, pricing, launch strategies, and growing to sustainable revenue. Ship in weeks, not months. Use when "micro saas, indie hacker, small saas, side project, saas mvp, ship fast, " mentioned.
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。
Expert in creating rich backstories, fictional universes, and ARGs (Alternate Reality Games). Covers world-building, mystery construction, community-driven storytelling, and transmedia narratives. Knows how to create depth that rewards exploration without overwhelming. Use when "lore, world building, backstory, ARG, mythology, fictional universe, hidden story, deep canon, " mentioned.
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。
Security patterns for MCP servers including OAuth 2.0, rate limiting, input validation, and audit loggingUse when "mcp security, mcp authentication, mcp oauth, mcp rate limit, secure mcp server, mcp, security, oauth, authentication, rate-limiting, validation" mentioned.
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。
Testing strategies for MCP servers including unit tests, integration tests, schema validation, and security testingUse when "mcp testing, test mcp server, mcp inspector, mcp validation, mcp, testing, unit-testing, integration-testing, schema-validation" mentioned.
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。
Expert in meme creation and virality - understanding why certain ideas spread while others die. Covers meme formats, timing, adaptation, meta-commentary, and platform-specific optimization. Knows the difference between forcing a meme and engineering conditions for organic spread. Use when "meme, viral content, make it memeable, shareable, internet culture, template, " mentioned.
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 space missions, computing launch windows, optimizing trajectories, analyzing payload constraints, or planning mission phases and contingencies. Use when "mission design, launch window, trajectory optimization, mission timeline, mass budget, propellant budget, mission phases, porkchop plot, C3, gravity assist, low thrust, mission architecture, LEO, GEO, interplanetary, " mentioned.
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 systems specialist for hierarchical memory, consolidation, and outcome-based learningUse when "memory system, memory hierarchy, memory consolidation, forgetting strategy, salience learning, outcome feedback, temporal memory levels, entity resolution, memory, zep, graphiti, mem0, letta, hierarchical, consolidation, salience, forgetting, ml-memory" mentioned.
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 implement Monte Carlo methods for uncertainty quantification, risk analysis, and probabilistic simulations across scientific and financial domains. Use when "monte carlo, random sampling, uncertainty quantification, risk analysis, stochastic simulation, MCMC, variance reduction, probabilistic, " mentioned.
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 for coordinating multiple LLM agents including sequential, parallel, router, and hierarchical architectures—the AI equivalent of microservicesUse when "multi-agent, agent orchestration, multiple agents, agent coordination, agent workflow, multi-agent, orchestration, llm, workflow, coordination, architecture" mentioned.
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。