「topology」の検索結果

Fixed-topology, discovery-only extension for Gaia curation. Atomically checkpoints each candidate transition, retries only the failed field, and stops at human L4 review.

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Use when generating or editing diagrams, charts, visual assets, posters, UI mockups, product images, infographics, academic figures, comics, avatars, storyboards, brand boards, or image-edit workflows, especially when exported PNGs need visual QA and bounded self-repair. Prefer deterministic Mermaid/SVG/HTML/canvas sources for text-heavy diagrams; use HTML technical diagrams for browser-ready system blueprints, architecture, deployment topology, agent runtime, memory flow, before-after architecture, workflow, sequence, data-flow, lifecycle, runbook, PII/data-lineage, and state-machine diagrams. Do not use for ordinary UI polish without generated imagery.

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Create polished dark-themed architecture diagrams as self-contained HTML+SVG files. Use when the user asks for system, infrastructure, cloud, security, or network topology diagrams.

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Improves GKE workload reliability, using PDBs, health probes, and topology spread constraints. Use when configuring GKE workload reliability, setting up PDBs, or configuring GKE health probes (liveness, readiness, startup). Don't use for disaster recovery setup or full cluster backups (use gke-backup-dr instead).

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Use when designing how an Agentforce agent transfers the conversation to a human agent (Omni-Channel), to another bot/agent, or to an alternate workflow — including context package, deflection, escalation triggers, and user messaging. Triggers: 'agent to human handoff', 'agentforce escalate to omni channel', 'agent to agent handoff', 'transfer conversation with context', 'agent deflection fallback'. NOT for topic scope and selector design — use agentforce/agent-topic-design. NOT for the overall Einstein Bot plus Agentforce estate topology — use architect/conversational-ai-architecture.

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Use this skill when defining patient engagement portal requirements for Health Cloud: appointment scheduling, secure in-portal messaging, health assessments, patient education, and self-service features. NOT for care team workflows, SDOH barriers and care gaps — use admin/care-coordination-requirements. NOT for patient portal licensing and cloud topology — use architect/health-cloud-multi-cloud-strategy.

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ออกแบบ 3D model — Blender workflow, topology, UV unwrap, texturing, render brief สำหรับ game/product/character

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Create clear architecture views of Azure resources and their relationships. Use this skill when users ask for diagrams, topology summaries, dependency maps, or deployment overviews. Generates Mermaid, ASCII, and structured documentation for architecture review and compliance.

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Reports how local Git branches are progressing toward canonical upstream inclusion as a compact terminal table plus a browser-rendered HTML report, with branch purpose, stack dependencies, Git Machete topology, ahead/behind counts, change-request status, and next action. Use when asked for upstreaming status, local branch submission progress, which branches have change requests, or what remains before they can land.

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Spin up a coordinated multi-agent swarm (hierarchical or mesh topology) to work a complex, multi-file task on a ruOS desktop. Use when a task needs 3+ agents working in parallel with anti-drift coordination; skip for single-file edits.

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Use when user says "simmer this", "refine this", "hone this", "iterate on this", or asks to improve a specific artifact over multiple rounds. Runs an iterative refinement loop with investigation-first judges that read the code, understand the problem, and propose evidence-based improvements. Auto-selects single judge or multi-judge board based on complexity. Works on any artifact type: documents, prompts, specs, emails, creative writing, API designs, pipelines, codebases. Supports multi-file workspace targets, runnable evaluators, and open-ended optimization (model selection, pipeline topology, prompt tuning).

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Design Kafka consumer groups that survive rebalances, hit the right delivery guarantee (at-most/at-least/exactly-once), and handle poison messages without stalling the topic. Use when picking a partition assignment strategy (Range vs Sticky vs Cooperative-Sticky vs the new KIP-848 protocol), tuning heartbeat / session / max.poll.interval timeouts, choosing manual vs auto offset commits, designing dead-letter or retry topics, or migrating from Classic to the next-gen Consumer protocol. NOT for Kafka cluster ops (broker tuning, partition reassignment), schema design (Avro/Protobuf/Schema Registry), or stream processing topology (use a kafka-streams skill).

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 a background-job system, choosing between BullMQ / Sidekiq / RQ / Temporal / SQS, deciding queue-vs-workflow, sizing concurrency vs rate limits, building dead-letter queues, or making handlers idempotent. Triggers: jobs running twice on retry, lost jobs after worker crash, DLQ filling up, Redis OOM from job backlog, exactly-once requested, "do we need Temporal?", visibility timeout / lockDuration confusion, exponential backoff vs jitter, fan-out fan-in workflows. NOT for outbound webhook publishing (different concerns), receiver-side webhook handling (different concerns), event-streaming/Kafka topology, or in-process async (event loop only).

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Data structures and serialization formats for agent-to-agent communication. Covers message envelopes, structured output schemas, capability declarations, task handoff payloads, error/retry signaling, and context windows as data structures. Deep comparison of A2A protocol, MCP, OpenAI function calling, and LangChain message types. Teaches when to use rigid schemas vs free-form with validation, typed vs untyped, streaming vs batch. Activate on: "agent message format", "agent communication schema", "agent-to-agent protocol", "A2A protocol", "MCP message format", "structured output for agents", "agent interop", "interchange format", "agent serialization", "task handoff format", "capability declaration". NOT for: what agents say to each other (use agent-conversation-protocols), orchestration topology (use multi-agent-coordination), building agent infrastructure (use agentic-infrastructure-2026).

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。

Conversation patterns and interaction protocols for multi-agent systems. Covers request/response, pub/sub, blackboard, delegation chains, debate, critique, consensus, fan-out/fan-in, supervisor-worker, and peer negotiation. Deep analysis of AutoGen conversation patterns, CrewAI delegation, LangGraph state passing, and FIPA-ACL performatives. Teaches how to design what agents say to each other and in what order. Activate on: "agent conversation", "agent protocol", "multi-agent debate", "agent delegation", "supervisor worker pattern", "agent voting", "consensus protocol", "fan-out fan-in", "agent negotiation", "blackboard pattern", "agent dialogue", "conversation topology", "agent handoff". NOT for: wire format or serialization (use agent-interchange-formats), orchestration infrastructure (use agentic-infrastructure-2026), single agent behavior (use agentic-patterns).

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。

Select and confirm an overnight or long-running unattended collaboration topology. Use only for a requested unattended run, not ordinary questions or short 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 generating a single dense technical architecture, reference poster, or system diagram image for a software project. Produces a source-grounded bitmap diagram with topology, primitives, ingest/process flow, core logic, state scopes, tiers/modules, surfaces, runtime cards, and strict semantic color rules.

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Assemble a full multi-tier content episode from a milestone session's hot context and publish it out of band, ahead of the nightly schedule: teasers go to chats right after one approval, the rest follow by topology. Triggers: "/wow", "this is a milestone", "this session is great, tell everyone".

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 AI assistant configurations and peer-to-peer mesh network connections in the NV oOS Pro Toolkit. Covers assistant creation, model configuration, provider setup, peer discovery, mesh networking, and cross-assistant communication. Use when creating or editing AI assistants, configuring model providers, setting up peer connections, managing mesh network topology, or debugging assistant behavior.

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 AI assistant configurations and peer-to-peer mesh network connections in the NV oOS Pro Toolkit. Covers assistant creation, model configuration, provider setup, peer discovery, mesh networking, cross-assistant communication, and detecting from a coding-agent session (Zed) which NV oOS assistants / MCP servers are enabled. Use when creating or editing AI assistants, configuring model providers, setting up peer connections, managing mesh network topology, debugging assistant behavior, or answering "is agent X available" / "what tools does this MCP have".

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Use for Nauthilus Redis client, Redis Cluster/Sentinel/topology behavior, Redis-backed token/session/flow/cache/bruteforce state, Lua Redis scripts, `NOSCRIPT` recovery, script upload/retry behavior, redismock tests, local cache coordination, and fail-closed runtime state 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。

Review repository architecture using Ontoly Software Graph and MCP capabilities. Use when asked to explain architecture, module boundaries, package topology, service ownership, or architectural risk.

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 Aws Transit Gateway — aWS Transit Gateway attachment analysis, route table management, peering configuration, and network topology review. Covers TGW inventory, attachment health, route propagation, VPN connection status, and bandwidth utilization.

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 Tidb — tiDB cluster topology, slow query analysis, hot region analysis, dashboard monitoring, and SQL optimization.

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。