1,431 件 ・ 関連度順
概要と使いどころ
Walk through the devnet → mainnet deployment checklist for a Soroban contract or Stellar dApp. Use when a user says "deploy to Stellar mainnet", "going live on Stellar", "deploy my Soroban contract", "Stellar production deployment", "mainnet checklist", or "ship it on Stellar". Covers pre-deployment verification, deployment mechanics with stellar CLI, and post-deployment monitoring with Soroban-specific 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。
Build AI performance monitoring. TRIGGERS - Use when user needs help with ai-performance-monitor related 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。
Build AI compliance monitoring systems. TRIGGERS - Use when user needs help with ai-compliance-monitor related 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。
Build AI crisis monitoring and alerting systems. TRIGGERS - Use when user needs help with ai-crisis-monitor related 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。
Adverse event monitoring and reporting skill for MDR/MEDWATCH compliance
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 Adobe-powered applications to Vercel, Cloud Run, and Adobe App Builder with proper credential injection and health monitoring. Use when deploying Adobe API integrations to production platforms. Trigger with phrases like "deploy adobe", "adobe Vercel", "adobe Cloud Run", "adobe App Builder deploy", "adobe production deploy".
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。
Open and manage short positions on HoneyComb job tickers. Profit when a ticker's price drops. Covers opening shorts with collateral, closing (full/partial), margin withdrawal, liquidation risk, and position health monitoring. Use when betting against a job position or hedging long exposure.
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。
Azure Application Insights SDK for .NET. Application performance monitoring and observability resource management.
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 anomaly detection applications with Azure AI Anomaly Detector SDK for Java. Use when implementing univariate/multivariate anomaly detection, time-series analysis, or AI-powered monitoring.
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。
Scrape reviews, ratings, and brand mentions from multiple platforms using Apify Actors.
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。
Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on real-world benchmarks
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。
Change conviction level or status on a single thesis. Use when user says "change conviction", "downgrade", "upgrade", "move to monitoring", "close [TICKER]", or "set [TICKER] to [level]".
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。
Systematically evaluate weak, stale, or low-conviction theses for upgrade, monitoring, or closure. Use when user says "prune", "kill list", "clean up theses", "what should I close", or "reduce portfolio".
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。
Proactively search for security threats, vulnerabilities, and suspicious patterns in applications and infrastructure before they cause damage. Use when conducting security audits, identifying vulnerabilities, analyzing security logs, detecting suspicious patterns, investigating potential breaches, performing penetration testing, or implementing security monitoring.
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 robust error handling strategies including try-catch blocks, custom error classes, error boundaries, graceful degradation, and comprehensive logging. Use when implementing exception handling, creating custom error types, setting up error boundaries in React, designing error response formats for APIs, implementing retry logic, handling async errors and promise rejections, logging errors for monitoring, creating user-friendly error messages, or building fault-tolerant systems that fail gracefully.
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。
Post-deploy canary monitoring. Watches the live app for console errors, performance regressions, and page failures using the browse daemon. Takes periodic screenshots, compares against pre-deploy baselines, and alerts on anomalies. Use when: "monitor deploy", "canary", "post-deploy check", "watch production", "verify deploy". (gstack)
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。
Monitor and react to content distribution events using Sling Distribution API (org.apache.sling.distribution). Covers distribution event handling, queue monitoring, and distribution lifecycle tracking.
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。
AEM as a Cloud Service content distribution and replication. Covers programmatic publishing using the Replication API and distribution event monitoring using Sling Distribution events.
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。
Babysit a GitHub pull request during a user-approved monitoring window by polling review comments, CI checks/workflow runs, and mergeability state. Diagnose failures, retry likely flaky failures up to 3 times, auto-fix/push branch-related issues when appropriate, and report fresh review feedback. Use when the user asks Codex to monitor a PR, watch CI, handle review comments, or keep an eye on failures and feedback on an open PR.
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。
Prepare safe production launches — pre-launch checklist, monitoring, staged rollout, and rollback. Load when deploying to production, releasing a major change, opening beta access, or the user asks for a launch checklist, go-live plan, rollout strategy, or rollback plan. Not for local-only prototypes. Pairs with ci-cd-and-automation and app-security-hardening.
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。
Translate an approved experiment spec into a launch runbook — platform binding (PostHog primary), feature flag setup, assignment unit, exposure event definition, instrumentation QA, dashboard wiring, ramp plan, monitoring, and rollback procedure. Platform-agnostic core with one strong PostHog adapter shipped; GrowthBook, Statsig, LaunchDarkly, Optimizely, and Eppo documented as a single mapping table the user adapts. Load when a spec is approved and ready to launch, or when the user says "set up the experiment", "wire this up in PostHog", "implement the test", "create the runbook", "launch checklist for this test", or when the experimentation orchestrator routes here.
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 automated evaluation pipelines for LLM and agent systems — combining deterministic checks, statistical metrics, and LLM-as-judge scoring into repeatable, CI-integrated eval suites. Load when the user asks to set up automated evals, design an eval pipeline, integrate evals into CI/CD, create an eval suite, do eval-driven development, or says "automate my evals", "CI eval integration", "evaluation pipeline", "continuous evaluation", "monitoring eval quality", "set up regression testing for my agent". Sub-skill of eval-output orchestrator.
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。
Automate Cloudinary media management including folder organization, upload presets, asset lookup, transformations, and usage monitoring through natural language commands
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。
Azure Application Insights SDK for .NET. Application performance monitoring and observability resource management.
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。