Browse reusable Agent Skills, each with a clear purpose and practical guidance.
How Hermes monitors and steers long-running sandboxed.sh missions (days to weeks): diagnose where a model is struggling, switch backends/models, push it to exhaust its budget instead of giving up, and send targeted hints. Trigger terms: mission, sandboxed.sh, babysit, monitor, /goal, switch backend, stalled, resume, keep going.
Boss skill for parallel worker orchestration. Analyze, split, delegate, monitor, integrate. Do not implement directly.
Prescriptive Q&A workflow for designing agentic pipelines, multi-model councils, sub-agent hierarchies, and tool-loop hardening for any domain. Use when the user asks to "design an agent", "design a multi-agent system", "should I use a council/debate", "build a [domain] review agent" (HAZOP, finance, tutorial, marketing, compliance, accounting), "real agency vs workflow", "how to add sub-agents", "AI for [domain] review", or names patterns like "orchestrator-worker", "evaluator-optimizer", "Magentic", "ReAct", "plan-and-execute", "handoffs". Walks the user through 12 stages one question at a time and emits a buildable design doc with citations. Do NOT use for general coding questions, single-shot prompt tuning, or bare "use Claude to do X" requests with no agency requirement.
Decide whether your agent actually needs persistent memory, feedback loops, or closed-loop learning, then design the smallest thing that pays for itself. Use when the user says "add memory", "give my agent context management", "make my agent learn", "self-improving / closed-loop", "Reflexion / mem0 / Letta / MemGPT", "AriGraph", "agent memory architecture", "long-term memory for chatbot", "why does my agent keep forgetting / making the same mistake", "fine-tune from agent traces", or asks for a memory schema / experience store / reward model. Filters ruthlessly — most teams want a state cache, not memory + learning. Default position is scratchpad-only with a stateless agent shipped first.
ElevenLabs の新モデル追加時に使用。provider2agent.ts のモデルリスト更新とテストスクリプト更新を行う。
Adaptive lifecycle orchestrator for explicit Ralph lifecycle, continuation, or engine requests
Use when modifying recce/mcp_server.py, MCP tool handlers, error classification, or MCP-related tests. Also use when adding new MCP tools or changing tool response formats.
Lightweight session execution skill. Resumes existing team-coordinate sessions for pure execution via team-worker agents. No analysis, no role generation -- only loads and executes. Session path required. Triggers on "Team Executor".
Swarm intelligence team skill — ACO-driven multi-agent exploration with hybrid LLM coordinator + Python optimization controller. Coordinator generates swarm-config from user task, then runs K iterations of N parallel ants guided by pheromone state. Universal task space via config (nodes + scoring rule). Triggers on "team swarm", "swarm intelligence", "蚁群".
Operate the 10xProductivity assistant inbox workflow. Use when running or debugging 10x-host, Slack self-DM polling, macOS notification triggers, scheduling runtime, or trigger-to-workflow routing.