「failures」の検索結果

Use when working with Aws Codebuild — aWS CodeBuild project management and build analysis. Covers build project inventory, build history, build phase details, environment configurations, source credentials, report groups, and build metrics. Use when inspecting build projects, debugging build failures, reviewing build environments, or analyzing build performance.

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 Argocd — argoCD GitOps continuous delivery management for Kubernetes. Covers application sync status, deployment health, rollback operations, repository management, cluster registration, RBAC analysis, and application diff review. Use when checking deployment status, investigating sync failures, managing GitOps applications, or auditing ArgoCD configurations.

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 Airflow — apache Airflow workflow orchestration management. Covers DAG management, task instance monitoring, executor health, pool status, variable management, connection audit, and scheduler diagnostics. Use when checking DAG run status, investigating task failures, managing Airflow resources, or auditing configurations.

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 Amplify — aWS Amplify application management and deployment analysis. Covers Amplify apps, branches, backend environments, build status, domain associations, and webhook configurations. Use when inspecting Amplify deployments, debugging build failures, reviewing hosting configurations, or auditing Amplify app settings.

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 Apache Druid Deep — apache Druid deep management — monitor cluster health, datasources, ingestion tasks, segments, query performance, and coordinator/overlord status. Use when debugging ingestion failures, inspecting segment distribution, optimizing query latency, or auditing cluster capacity.

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 Ansible — ansible automation and configuration management. Covers playbook execution, inventory management, role management, vault secrets, ad-hoc commands, fact gathering, and task debugging. Use when running playbooks, managing inventories, debugging task failures, or auditing Ansible configurations.

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 App Runner — aWS App Runner service management and health analysis. Covers service inventory, deployment status, auto-scaling configurations, custom domain associations, VPC connector settings, and observability configurations. Use when inspecting App Runner services, debugging deployment failures, reviewing scaling behavior, or auditing service configurations.

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 Great Expectations — great Expectations data quality framework analysis. Covers expectation suite management, validation result review, data docs generation, checkpoint execution, datasource configuration, and batch analysis. Use when reviewing data quality results, managing expectation suites, investigating validation failures, or auditing data quality configurations.

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。

Home Artificial Intelligence by Lynn Greiner Many autonomous agents doomed by governance failures Ne

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 creating or editing cron jobs - prevents timeouts, silent failures, and delivery gaps.

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。

RED-GREEN-REFACTOR for process documentation - baseline without skill, write addressing failures, iterate closing loopholes

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。

Understand how ideas evolved over time to find old solutions for new problems and avoid repeating past failures

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 doing ANY task involving Supabase. Triggers: Supabase products (Database, Auth, Edge Functions, Realtime, Storage, Vectors, Cron, Queues); client libraries and SSR integrations (supabase-js, @supabase/ssr) in Next.js, React, SvelteKit, Astro, Remix; auth issues (login, logout, sessions, JWT, cookies, getSession, getUser, getClaims, RLS); Supabase CLI or MCP server; schema changes, migrations, declarative schemas, security audits, Postgres extensions (pg_graphql, pg_cron, pg_vector); debugging and troubleshooting errors or unexpected behavior on Supabase projects (HTTP errors, Postgres errors, RLS surprises, permission denied, schema cache issues, timeouts, Edge Function crashes, Realtime drops, Storage failures) and reading or querying logs (Logs Explorer, ClickHouse).

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。

Deploy and configure web frameworks on Netlify — build settings and SSR/edge adapters plus local platform emulation and env vars. Use when setting up or fixing a framework deploy (Next.js / Astro / Nuxt / SvelteKit / Remix / React Router / TanStack Start / SolidStart / Gatsby / Angular / Vite / Express / Hydrogen / Hugo / Eleventy / Vue / React), adding SSR or edge functions or middleware wired to Netlify context, fixing SPA redirect and catch-all rules, setting a build command or publish directory, or debugging "why isn't my env var updating" and framework build failures.

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。

Optimize MongoDB client connection configuration (pools, timeouts, patterns) for any supported driver language. Use this skill when working/updating/reviewing on functions that instantiate or configure a MongoDB client (eg, when calling `connect()`), configuring connection pools, troubleshooting connection errors (ECONNREFUSED, timeouts, pool exhaustion), optimizing performance issues related to connections. This includes scenarios like building serverless functions with MongoDB, creating API endpoints that use MongoDB, optimizing high-traffic MongoDB applications, creating long-running tasks and concurrency, or debugging connection-related failures.

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。

**MANDATORY prerequisite** — you MUST invoke this skill BEFORE every `use_figma` tool call. NEVER call `use_figma` directly without loading this skill first. Skipping it causes common, hard-to-debug failures. Trigger whenever the user wants to perform a write action or a unique read action that requires JavaScript execution in the Figma file context — e.g. create/edit/delete nodes, set up variables or tokens, build components and variants, modify auto-layout or fills, bind variables to properties, or inspect file structure programmatically.

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。

AI demos and GPU compute with Gradio Spaces and Hugging Face Spaces ZeroGPU. Use when writing or reviewing code that uses `@spaces.GPU`, configuring `python_version` or `requirements.txt` for a ZeroGPU Space, or handling ZeroGPU-specific code constraints — pickle-based process isolation, `gr.State` semantics across the worker boundary, no `torch.compile` (use AoTI instead), CUDA wheel-only builds (no `nvcc` at build or runtime), large vs xlarge sizing, and dynamic duration callables. Make sure to use this skill whenever the user mentions ZeroGPU, `@spaces.GPU`, or the `spaces` Python package, or hits ZeroGPU-specific code errors like `PicklingError` across the worker boundary, `illegal duration`, or `flash-attn` wheel-build failures — even when the user does not explicitly ask for ZeroGPU coding guidance. Trigger on `import spaces` or `@spaces.GPU` in code.

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。

Pick the right serving container for a SageMaker model deployment and find its current image URI. Use this skill whenever about to deploy a model to a SageMaker endpoint and an image URI needs to be chosen — including when the user says "deploy this LLM", "host this HuggingFace model", "serve this fine-tuned model", "deploy this embedding model", "host a reranker", "serve a sentence-transformers model", or when about to hardcode any container URI in deployment code. HuggingFace-curated Deep Learning Containers are ALWAYS preferred: HuggingFace vLLM (LLMs and generative rerankers), HuggingFace vLLM-Omni (multimodal), TEI (embeddings/cross-encoder rerankers), HF Inference Toolkit (other transformers). Generic images (AWS vLLM, DJL-LMI, SGLang) are used only when no HuggingFace image is compatible — never merely because they carry a newer version. Never hardcode a container URI from memory and never default to TGI. Prevents stale-image failures and wrong-region URIs.

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 resilient applications with robust error handling strategies that gracefully handle failures and provide excellent debugging experiences.

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 .NET Framework CLR activation issues using CLR activation logs (CLRLoad logs) produced by mscoree.dll. Use when: the shim picks the wrong runtime, fails to load any runtime, shows unexpected .NET 3.5 Feature-on-Demand (FOD) dialogs, unexpectedly does NOT show FOD dialogs, loads both v2 and v4 into the same process causing failures, or any time someone is wondering "what is happening with .NET Framework activation?"

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。

Detects MSBuild projects with conflicting OutputPath or IntermediateOutputPath. Only activate in MSBuild/.NET build context. USE FOR: builds failing with 'Cannot create a file when that file already exists', 'The process cannot access the file because it is being used by another process', intermittent build failures that succeed on retry, missing outputs in multi-project builds, multi-targeting builds where project.assets.json conflicts. Diagnoses when multiple projects or TFMs write to the same bin/obj directories due to shared OutputPath, missing AppendTargetFrameworkToOutputPath, or extra global properties like PublishReadyToRun creating redundant evaluations. DO NOT USE FOR: file access errors unrelated to MSBuild (OS-level locking), single-project single-TFM builds, non-MSBuild build systems. INVOKES: binlog MCP server tools (overview, projects, evaluations, properties, double_writes); falls back to dotnet msbuild binlog replay + grep when the MCP is unavailable.

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。

Debugging specialist for errors, test failures, and unexpected behavior. Use proactively when encountering any issues.

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 tools that agents can use effectively, including architectural reduction patterns. Use when creating new tools for agent systems, debugging tool-related failures or misuse, or optimizing existing tool sets for better agent performance.

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 or maintain the full tdmcp repo quality-audit team: command sweeps, all package/Makefile/CI gates, security review, usability/flow review, refactor/test-gap analysis, coverage hardening, QA, and follow-up fix waves. Use whenever the user asks for a complete audit, improve repo/code quality, test all commands, find security/usability failures, refactor debt, add missing tests, re-run the audit, continue a previous quality wave, or verify the repo is ready.

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。