1.7K skills · sorted by relevance
Production forms: production-level constraints, async status, screen reader errors. Use when implementing, reviewing or testing production forms for React, Vite, Next.js or Perfect_AI projects.
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Use when validating a packaged build of this Hydrolix Grafana datasource plugin against Grafana's official plugin-validator — the pre-release gate that catches missing metadata, unsigned/mis-signed archives, non-executable backend binaries, and packaging errors. Builds the plugin, zips `dist/` under the plugin ID, and runs `@grafana/plugin-validator` (npx, or docker as fallback). Apply for "validate the plugin", "is this zip release-ready", or before publishing / submitting a release artifact.
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TanStack Query (React Query) v5 best practices for data fetching, caching, mutations, and server state management. Use when building data-driven React applications, setting up query configurations, implementing mutations/optimistic updates, configuring caching strategies, integrating with SSR, or fixing v4→v5 migration errors.
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Proactive self-monitoring of infrastructure, services, and health. Tracks disk/memory/load, service health, cron job status, recent errors. Auto-fixes safe issues. Triggers on: health check, heartbeat, monitor status, service status, infrastructure check.
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Monitor for API billing errors and alert the owner and admin immediately. Use when: an API billing error is detected, a peer PA reports a billing error, or during routine health checks. Handles detection, notification, and fallback model switching. Model-agnostic: works with any LLM provider (Anthropic, OpenAI, Google, etc.).
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Use when working with Coralogix — coralogix observability platform for log analytics, metrics, tracing, security, and alerting. Covers log querying with DataPrime, metric exploration, alert management, parsing rules, and data usage insights. Use when searching Coralogix logs, investigating application errors, managing alert rules, or analyzing log ingestion patterns.
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Use when working with Aws Iot Core — aWS IoT Core management including things, thing groups, certificates, policies, rules, topic monitoring, device shadows, and fleet indexing. Covers device connectivity health, message throughput, rule action errors, certificate expiration, and shadow drift analysis.
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Use when working with Aws Cdk Deep — aWS CDK application and stack management. Covers stack synthesis, CloudFormation template inspection, diff analysis, deployment orchestration, context values, asset management, and construct library usage. Use when managing CDK apps, debugging synthesis errors, reviewing stack diffs, or analyzing deployed CloudFormation resources.
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PostgreSQL database analysis, performance tuning, and health monitoring. You MUST read this entire skill document before executing any PostgreSQL operations — it contains mandatory workflows, safety constraints, and two-phase execution rules that prevent common errors like hallucinated column names and unsafe queries.
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Use when working with AWS API Gateway — analyzing REST or HTTP APIs, debugging latency or 5xx errors, auditing throttling and stage configuration, reviewing usage plans, or checking authorization setup. Covers API inventory, CloudWatch metrics, stage audits, and integration health for both REST (v1) and HTTP (v2) APIs.
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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.
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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.
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Use when implementing, integrating, upgrading, debugging, or answering anything involving third-party APIs, libraries, frameworks, CLIs, cloud services, model or provider SDKs, fast-moving product behavior, unfamiliar repo docs or specs, errors that may indicate API drift, or high-stakes auth, security, billing, data, migration, deployment, compliance, or privacy behavior. Forces a docs pass before coding from memory.
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Write JavaScript code in n8n Code nodes. Use when writing JavaScript in n8n, using $input/$json/$node syntax, making HTTP requests with $helpers, working with dates using DateTime, troubleshooting Code node errors, or choosing between Code node modes.
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Experimental workflow for auditing recurring feedback, telemetry, errors, and brittle delivery paths to find systemic fixes. Use for periodic or requested cross-source lookbacks, not routine single-item intake.
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Debugging specialist for errors, test failures, and unexpected behavior. Use proactively when encountering any issues.
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Experimental workflow for summarizing work that still needs human judgment across configured pull requests, issues, feedback, errors, and delivery signals. Use for a human decision queue or bounded review.
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Experimental workflow for collecting and triaging product feedback, product telemetry, runtime errors, and issue reports. Use when scanning configured signal sources and applying separate fix, reply, or close rules.
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When designing how a system recovers from and reports failures.
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Use when working with iOS/macOS Keychain Services (SecItem queries, kSecClass, OSStatus errors), biometric authentication (LAContext, Face ID, Touch ID), CryptoKit (AES-GCM, ChaChaPoly, ECDSA, ECDH, HPKE, ML-KEM), Secure Enclave, secure credential storage (OAuth tokens, API keys), certificate pinning (SecTrust, SPKI), keychain sharing across apps/extensions, migrating secrets from UserDefaults or plists, or OWASP MASVS/MASTG mobile compliance on Apple platforms.
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Transfer app data between platforms using AppMigrationKit. Use when implementing one-time data migration from Android or other platforms to iOS, managing cross-platform transfer sessions with AppMigrationExtension, packaging and archiving user data for export, importing resources on the destination device, tracking transfer progress, handling migration errors, or building onboarding flows that import existing user data.
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Swift Concurrency review and remediation for Swift 6.2+. Use when asked to review Swift Concurrency usage, improve concurrency compliance, or fix Swift concurrency compiler errors in a feature or file. Concrete actions include adding Sendable conformance, applying @MainActor annotations, resolving actor isolation warnings, fixing data race diagnostics, and migrating completion handlers to async/await.
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Use this skill when handling runtime errors, adding error.tsx or not-found.tsx, designing fallback UI, or deciding what to surface to users vs logs. Trigger words: エラー処理, error.tsx, not-found, 例外, フォールバック, エラーバウンダリ, try catch, ユーザー向けエラー, ログ.
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Review and rewrite the words in a user interface (buttons, labels, errors, empty states, confirmations, onboarding, notifications) for clarity, tone and consistency. Use when the user asks for UX writing feedback, microcopy, error messages, button text, or a content pass on a screen or flow.
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