「failure」の検索結果

Lookup documented AKS fixes only when the prompt includes an exact catalog signature and all of its qualifiers: VMCannotFitEphemeralOSDisk; NodePoolMcVersionIncompatible; 'NodeImageVersion is not accepted'; AKS SkuNotAvailable with size, location, and zone; ZonalAllocationFailed with insufficient zone capacity; OverconstrainedAllocationRequest with listed constraints; nested AKS vmssCSE/CSE VMExtensionError_OutboundConnFail, VMExtensionError_K8SAPIServerConnFail, or VMExtensionError_K8SAPIServerDNSLookupFail; or AllocationFailed with the full cataloged internal-error or insufficient-regional-capacity message. Never use for quota errors, code-only or bare wrappers, generic symptoms, incomplete signatures, or failures outside AKS; use aks-troubleshooting or azure-diagnostics.

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Assess and improve the reliability posture of PaaS Applications (Azure Functions and Azure App Service). Scans deployed resources for zone redundancy, ZRS storage, health probes, and multi-region failover. Presents a feature-pivoted checklist, then drives staged remediation (CLI or IaC patches) end-to-end with user confirmation. WHEN: "assess reliability", "check reliability", "zone redundant", "multi-region failover", "high availability", "disaster recovery", "single points of failure", "reliability posture", "resiliency".

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Build, deploy, evaluate, optimize, fine-tune, and manage Microsoft Foundry agents, models, and resources end to end. USE FOR: foundry, azd ai agent, azd provision/deploy, hosted agent scaffold/develop/run/deploy/troubleshoot, prompt agent create, create agent, update agent, add tool to agent, invoke agent, agent.yaml, agent insights, pull agent insights, evaluate agent, batch eval, continuous eval, continuous monitoring, agent CI/CD, optimize prompt, improve prompt, prompt optimizer, optimize agent instructions, Agent Optimizer scaffold, dataset curation from traces, deploy model, model fine-tuning (SFT/DPO/RFT), Foundry project, RBAC, role assignment, permissions, quota, capacity, region, deployment failure, AI Services, create Foundry resource, knowledge index, customize deployment, onboard, availability, training-data, grader, distillation, large file upload. DO NOT USE FOR: Azure Functions, App Service, general Azure deploy (use azure-deploy), general Azure prep (use azure-prepare).

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Plan, deploy, operate, and troubleshoot Azure Local (formerly Azure Stack HCI): sizing and prerequisites, Arc registration, lifecycle updates, workloads (Azure Local VMs, AKS on Azure Local, images, disks, logical networks), SDN and network security, and failure triage — starting read-only and confirming before risky changes. WHEN: Azure Local, Azure Stack HCI, Arc resource bridge, custom location, Azure Local VM, Arc VM, AKS on Azure Local, AKS hybrid, SDN, Lifecycle Manager, Azure Local update, disconnected site.

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Reviews test code to identify and fix common testing anti-patterns including flaky tests, over-mocking, brittle assertions, test interdependency, and hidden test logic. Flags bad patterns, explains the specific defect, and provides corrected implementations. Use when reviewing test code, debugging intermittent or unreliable test failures, or when the user mentions flaky tests, test smells, brittle tests, test isolation issues, mock overuse, slow tests, or test maintenance problems.

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Coordinates parallel investigation threads to simultaneously explore multiple hypotheses or root causes across different system areas. Use when debugging production incidents, slow API performance, multi-system integration failures, or complex bugs where the root cause is unclear and multiple plausible theories exist; when serial troubleshooting is too slow; or when multiple investigators can divide root-cause analysis work. Provides structured phases for problem decomposition, thread assignment, sync points with Continue/Pivot/Converge decisions, and final report synthesis.

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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.

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Write code evaluators for known failure modes with objective rules. Use when code can check the rule from a trace, with or without a reference answer. Use `write-judge-prompt` when the rule requires interpretation.

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Run error analysis on a dataset. Build a review UI, select diverse samples, monitor annotations, and organize failure modes.

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Create diverse synthetic test inputs for LLM pipeline evaluation using dimension-based tuple generation. Use when bootstrapping an eval dataset, when real user data is sparse, or when stress-testing specific failure hypotheses. Do NOT use when you already have 100+ representative real traces (use stratified sampling instead), or when the task is collecting production logs.

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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.

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This skill should be used when picking or diagnosing a training move (SFT, LoRA, DPO/KTO/ORPO, RFT, GRPO/PPO/RLOO, RLHF), or when the user mentions fine-tuning, post-training, training recipe, reward design, or weight updates. Decision tree by reward shape, smoke-run gate, three failure diagnostics, five false-progress patterns. Provider recipes and I/O contract in references/.

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Builds and ships iOS/macOS apps with XcodeGen, SwiftUI and SPM: Xcode build fixes, device deployment, Apple Developer signing/notarization, CI/CD pipelines. Use for XcodeGen project.yml setup, "Library not loaded @rpath" crashes, code-signing errors (Error -25294, adhoc fallback), or Electron notarization failures. Not for TCC permission dialogs (use macos-permissions) or disk cleanup (use macos-cleaner).

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Fetch existing YouTube transcripts/subtitles or archive spoken video content as text, and download YouTube videos/audio or HLS streams (m3u8) with yt-dlp and ffmpeg. Use for transcript/subtitle retrieval, text-first video archiving, video/audio downloads, protected streams with authentication headers, and download failures including nsig, 403 or browser-cookie issues.

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Audits, diffs, copies, and lists providers/models, adds models, and switches the default model in local openclaw.json configs; diagnoses gateway/plugin failures (down, parked, broken, or out of date after an upgrade). Use for 虾/龙虾 config audits. Not for model-error repair (use openclaw-model-switch).

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MUST be used first for tunnel / proxy-path connectivity failures or slow throughput (网络故障 / 连不上 / 代理或隧道下超时). Diagnoses Tailscale, proxy, TUN, DNS and SSH-tunnel problems on macOS and Windows/WSL, e.g. Tailscale ping works but SSH fails, curl works but browser gets 503, Git/Docker fails behind the proxy, or downloads crawl despite a reachable proxy. Not for application-layer bugs (use debugging-network-issues).

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Investigates network, streaming and protocol failures with falsification-first layered experiments, the general fallback for unknown root causes. Use for ECONNRESET / RST_STREAM / SSE stalls, CDN/CGNAT timeouts or certificate errors, throughput collapse where everything returns 200 but transfers crawl, or LAN identity/topology mysteries. Not for known Tailscale/TUN/DNS/proxy-path conflicts (use tunnel-doctor).

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Work out which part of the octane monorepo owns an unfamiliar failure or task. Use when the area is unclear, before picking a more specific skill.

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Interview you into a durable personal MEMORY.md — your decision rules, patterns, past failures, and preferences — that any AI can read to help you better, with privacy guardrails built in. Use when asked to build my memory file, help my AI remember me, create a MEMORY.md, or set up context about myself. Produces a structured personal-context file drawn out through good questions (how you decide, what you keep repeating, what you don't want to repeat), organized for an AI to use — while explicitly refusing to store sensitive data like credentials, financial, health, or others' personal info.

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Diagnose a bug systematically instead of guessing — reproduce, isolate, form hypotheses, and test them to root cause. Use when debugging, chasing a defect, an intermittent failure, or 'why is this happening?'. Produces a structured diagnosis: a reliable repro, the narrowed-down location, ranked hypotheses with how to test each, and the root cause + fix once found.

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Set up a backup system that actually protects your photos, files, and devices — built on the 3-2-1 rule and, crucially, tested so it works when you need it. Use when asked how to back up my data, set up backups, protect my photos/files, or what's a good backup strategy. Produces a 3-2-1 plan tailored to your devices and data, specific what-to-back-up priorities, an automation setup so it happens without you, a restore-test step, and protection against the failure that ruins backups (ransomware/sync-deletes reaching the backup).

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Get your attention back with a 30-day protocol that assumes you'll break it — a screen-time ledger without moralizing, friction engineering (what to delete, grayscale, where the phone sleeps), planned relapses, and honest replacement activities for the boredom that shows up on day 3. Use when someone says 'my screen time is 7 hours', 'I want a dumbphone', 'digital detox', 'I can't read books anymore', or 'my attention span is gone'. Produces the ledger, a personal friction plan, and the 30-day protocol with expected failure points.

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Review an LLM agent design and find where it will be unreliable, expensive, or unsafe. Use when asked to review an agent architecture, critique a multi-step/tool-using agent, debug an agent that loops or goes off-task, or harden an agent before launch. Produces a structured review — task fit, control flow, tools, memory/context, failure handling, cost, and safety — with prioritised findings and fixes.

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Run a blameless postmortem for an incident caused by an AI agent or LLM feature — hallucinated facts shipped to users, runaway tool use, prompt injection, cost blowouts, or wrong actions taken autonomously. Use when asked to write up an AI incident, analyse why an agent did something wrong, or produce corrective actions after an LLM failure. Produces a structured postmortem with trace reconstruction, a root-cause layer analysis, and corrective actions including a permanent regression case. For non-AI production incidents use incident-postmortem.

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。