「loop」の検索結果

Create, track and update team goals via the Deeplake virtual filesystem at memory/goal/. Use whenever the user mentions a goal, objective, target, milestone, or asks to track progress on something measurable. ALSO use when the user says "task", "todo", "work item", "remind me to", "fix X", or any actionable work item — the goal system replaced the legacy `hivemind tasks` CLI and now covers both objectives and 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。

Run a force-robust Stormbreaker loop — route to real agents, execute a verified pipeline to completion.

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 the user types /agentlas-storm, /agentlas storm, /hep-storm, or says @Hephaestus storm <goal> to drive a goal through the force-robust Stormbreaker loop with real Agentlas specialists.

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 a meta-agent request is too ambiguous to safely generate, package, publish, or adapt without one to five targeted questions.

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 preserving product intent, acceptance criteria, decision memory, open loops, roadmap context, or the product-manager continuity layer of an agent team.

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 a force-robust Stormbreaker loop — route to real agents, execute a verified pipeline to completion.

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 live Azure Kubernetes Service (AKS) incidents with a read-only, evidence-first investigation. WHEN: pod crashes or Pending, CrashLoopBackOff, OOMKilled, ImagePullBackOff, node NotReady, DNS or ingress failure, connectivity timeout, network policy, SNAT exhaustion, node-pool scaling blocked by QuotaExceeded or InsufficientVCPUQuota, upgrade stuck, spot or zone disruption, a bare VMExtensionProvisioningError or AllocationFailed wrapper, an uncataloged capacity symptom, or 'investigate my AKS cluster'. DO NOT USE FOR: packet capture (use aks-network-capture); GPU or model-serving issues (use aks-gpu-inference); cluster creation or provisioning (use azure-kubernetes); cost (use cost-analysis from the optional azure-cost plugin); pod rightsizing (use azure-kubernetes); a fully qualified documented AKS signature with every required nested qualifier (use aks-known-issues); standalone failures on non-AKS Azure resources (use azure-diagnostics). Unqualified errors and open-ended incidents stay here only for AKS.

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 Azure production issues on Azure using AppLens, Azure Monitor, resource health, and safe triage. WHEN: debug production issues, troubleshoot app service, app service high CPU, app service deployment failure, troubleshoot container apps, container app 503 or target port mismatch, troubleshoot functions, function telemetry missing or stopped after a config/connection-string change, troubleshoot AKS, VM RDP, Linux SSH, VM black screen, can't connect to VM, reset VM password, NSG or firewall blocking, kubectl cannot connect, kube-system/CoreDNS failures, pod pending, crashloop, node not ready, upgrade failures, analyze logs, KQL, insights, image pull failures, cold start issues, health probe failures, resource health, root cause of errors, troubleshoot event hubs, troubleshoot service bus, messaging SDK error, AMQP connection failure, message lock lost, lock renewal during long-running processing, service bus dead letter.

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 a PixiJS v8 render layer: create the async Application, load textures with Assets, compose the scene graph with Container and Sprite, drive the ticker loop, wire pointer events, and group draws with render groups. Use when building or debugging PixiJS v8 — when the user mentions PixiJS, Pixi, Application, app.stage, Container, Sprite, Assets.load, app.ticker, or eventMode. Pins the v8 async init() API.

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。

Light and shade a three.js scene: choose materials (MeshStandardMaterial PBR vs unlit MeshBasicMaterial), add ambient/hemisphere/directional/point/spot lights, turn on shadow maps, and use an environment map (IBL) for realistic reflections. Use when a three.js model looks black, flat, or wrong — when the user mentions three.js materials, MeshStandardMaterial, lights, shadows, envMap, or PBR. For renderer/loop setup use threejs-scene-setup; for loading models use threejs-gltf-loading.

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 GDScript test suites from the command line with `godot --headless`, using a SceneTree/MainLoop runner script that exits non-zero on failure so CI can gate merges. Use when a Godot project needs unit tests without opening the editor, when wiring a CI job (GitHub Actions or similar) that must fail the build on a failing `.gd` test, or when a `godot --headless` invocation hangs, opens a window, or exits 0 despite failed assertions.

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。

Structure and debug a LÖVE (Love2D) game in Lua: the love.load/update/draw loop, delta-time movement, input, and screen states. Use when building a LÖVE 11.x game (main.lua, conf.lua, .love).

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 /debug to find and fix a bug's root cause: a test failing for an unclear reason, /check verify finding a failure, or behavior being wrong. Runs a reproduce, localize, hypothesize, test, fix, verify loop, makes the minimal fix, and hands a regression test to /test. No features, no extra refactors.

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 build the visual layer of a long-form talking-head video as one persistent world the camera moves through, instead of a sequence of separate graphics. Use when asked to edit a video, add motion graphics, design the visuals for a script, plan an edit, or fix an edit judged confusing, text-heavy, hard to follow, or "clearly made by an AI". Covers the persistent world, camera grammar, the spotlight rule, spatial open loops, ornament and token discipline, transcript timing, and the verification gates.

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 build the visual layer of a long-form talking-head video as one persistent world the camera moves through, instead of a sequence of separate graphics. Use when asked to edit a video, add motion graphics, design the visuals for a script, plan an edit, or fix an edit judged confusing, text-heavy, hard to follow, or "clearly made by an AI". Covers the persistent world, camera grammar, the spotlight rule, spatial open loops, ornament and token discipline, transcript timing, and the verification gates.

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。

Drive structured autoresearch iteration after evo:discover and the baseline commit. Use when the user invokes /evo:optimize or asks to try ideas, try variants, run experiments, use available GPUs, improve the current best/frontier, continue an evo search, or compare candidate changes in an evo workspace. The orchestrator plans and spawns optimization subagents; candidate edits/runs belong to those subagents. Width is set via subagents=N (1 for serial workloads, larger for parallel); the loop's structural value applies at any width.

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。

Diagnoses macOS system-level slowness, heat and fleet-wide timeouts: high load average, leaked per-session child processes (MCP servers, helpers), fork storms, busy loops. Use when the Mac feels slow or hot, fans spin up, or everything times out at once (电脑发烫/卡顿/负载高/全部超时). Not for single-app bugs, network slowness (use tunnel-doctor), launchd watchdog design (use macos-watchdog), or disk-full (use macos-cleaner).

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。

Designs and audits macOS launchd watchdogs. Use for LaunchAgent/LaunchDaemon setup, plist changes, scheduled self-healing, repeated notifications, apps reopening after quit, crash or repair loops hammering the Mac, or a watchdog alert shown as Script Editor. Covers stop/disable/restart, cooldown/backoff, notification throttling and alert decisions. 中文：launchd 守护进程、常驻任务、开机自启、后台监控、定时自愈、通知来源排查。

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。

睡前/离开前额度耗尽时，设置一次性本机定时器，在 Claude 订阅额度重置后自动发一条极简 claude -p 消息，立刻开启新的 5 小时用量窗口——用户睡醒时新窗口已跑掉大半，等于白赚一个 冷却期。Use when 用户说「还有 X 小时重置额度，帮我定时戳一下 Claude」「我去睡觉了， 到点 ping 一下开启新窗口」「额度用完了，重置后帮我踩个点」「设个定时器开启新的 5 小时 冷却期」，或提到 定时戳、戳 Claude、quota ping、开窗、额度重置后自动发消息。 仅限一次性定时任务与 macOS 本机（caffeinate/BSD date）；周期性任务用 /loop 或 cron， 查询 ChatGPT/Codex 何时重置用 tibo-reset-codex，分析额度为什么烧完用 claude-usage-analyst。

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。

Tests/benchmarks a third-party LLM endpoint (OpenAI- or Anthropic-compatible): availability, fidelity, speed, concurrency, protocol compliance, quality regression. Use before hand-rolling a curl loop, when onboarding a provider, or debugging "system prompt 不生效" / a tok/s claim — 测评/压测一个模型或渠道. Not for TTS/voice-clone supplier eval (use the audio 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。

Set up the essentials in the right order after moving to a new country — the ID/registration, bank account, phone, address, and social/tax number that unlock each other — so you don't get stuck in the chicken-and-egg loops that trap newcomers. Use when someone says 'I just moved to a new country', 'what do I do first after arriving', 'set up my life in [country]', or 'I can't open a bank account without an address but can't rent without a bank'. Produces a sequenced arrival checklist with dependencies and the official offices for each. Routes to official sources; rules are local.

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。

Take a board game idea from 'wouldn't it be cool if' to a playtestable prototype — core loop, tension source, components you can make tonight, balance starting-points, and a real playtest protocol with kill criteria. Use when someone says 'I have a board game idea', 'design a game about X', 'my game drags in the midgame', or 'how do I playtest this'. Produces a design one-pager, a print-and-play prototype spec, and a 3-session playtest plan.

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。

Specify an autonomous or tool-using AI agent before building it. Use when asked to design an AI agent, define an agent's tools and guardrails, scope what an agent is allowed to do, or write an agent spec/PRD. Produces an agent spec — goal & scope, tools with permissions, the control loop, guardrails & approval gates, memory, escalation/handoff, evaluation, and failure handling.

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。

Make an AI agent or automation reliable enough to trust — the tests, checks, and guardrails that catch its failures before they reach anything real. Use when asked how do I test my AI agent, make my automation reliable, my agent works sometimes, or how do I trust an AI workflow in production. Produces a map of where the agent can fail (bad input, hallucination, wrong tool call, edge cases, silent errors), the checks that catch each (validation, evals on real cases, human-in-the-loop gates, monitoring), a right-sized reliability plan scaled to the stakes, and a rollout that earns trust incrementally.

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。