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概要と使いどころ
Troubleshoots Monte Carlo AI agent alerts and traces — eval score drops, latency/token spikes, trajectory and validation breaches. Not for data incidents (monte-carlo-analyze-root-cause) or monitor creation (monte-carlo-monitoring-advisor).
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。
Analyze data coverage, create monitors for warehouse tables and AI agents. Covers coverage gaps, use-case analysis, data monitor creation, and agent observability.
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。
Query and manage Grafana dashboards and Prometheus metrics for Happy infrastructure. Covers grafanactl CLI usage, direct Prometheus queries through Grafana proxy, and dashboard-as-code workflows. Use when user asks about metrics, dashboards, monitoring, Grafana, Prometheus, or wants to add/modify panels.
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-assisted pair programming with multiple modes (driver/navigator/switch), real-time verification, quality monitoring, and comprehensive testing. Supports TDD, debugging, refactoring, and learning sessions. Features automatic role switching, continuous code review, security scanning, and performance optimization with truth-score verification.
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。
Automating browsers via Playwright and Chrome DevTools for data collection, form interaction, screenshot capture, and network monitoring. Task completion focus (vs Voyager for E2E testing).
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。
Simulates a real user searching on Amazon's storefront to get real-time keyword ranking and search results page data. Use when the user mentions Amazon product search, search result scraping, keyword ranking on search pages, ASIN ranking position check, competitor discovery, search page price comparison, sponsored product analysis, new product monitoring, or storefront search simulation. Even if the user does not explicitly mention "search simulation", trigger this skill whenever their need involves real-time Amazon search results, product ranking data, or storefront SERP analysis.
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. (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。
Design metacognitive checkpoints that prevent AI-assisted learning from bypassing genuine understanding. Use when students use AI tools and may overestimate their own comprehension.
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。
Generate scaffolds supporting student self-regulation across planning, monitoring, and evaluation phases. Use when students struggle to manage their own learning during extended or independent 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。
Capture confidence ratings before and after a learning attempt to identify overconfidence and underconfidence patterns. Use when a student wants to understand how well they actually know something versus how well they think they know it.
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。
When the user wants help creating, scheduling, or optimizing social media content for LinkedIn, Twitter/X, Instagram, TikTok, Facebook, or other platforms, or wants to do social listening and engagement triage. Also use when the user mentions 'LinkedIn post,' 'Twitter thread,' 'social media,' 'content calendar,' 'social scheduling,' 'engagement,' 'viral content,' 'what should I post,' 'repurpose this content,' 'tweet ideas,' 'LinkedIn carousel,' 'social media strategy,' 'grow my following,' 'TikTok video,' 'Reels,' 'Shorts,' 'video script,' 'video hook,' 'short-form video,' 'create a reel,' 'social listening,' 'brand mentions,' 'competitor monitoring,' 'top posts to comment on,' or 'find people asking for.' Use this for social media content creation, repurposing, scheduling, short-form video scripting, and social listening. For broader content strategy, see content-strategy. For paid ads, see ad-creative. For earned media, see public-relations.
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。
Prepares production launches. Use when preparing to deploy to production, or when asking what needs to be in place before shipping. Use when you need a pre-launch checklist, when setting up monitoring, when planning a staged rollout, or when you need a rollback strategy.
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 monitoring. Use when verifying a deployed app is healthy after deploy or launch — auto-fires from launch/deploy. Checks HTTP status, response times, error detection, and smoke test report.
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。
Kolu's project-wide default for filesystem monitoring is `@parcel/watcher`. Reach for this skill when adding or modifying any code that watches files or directories — recursive subtree watching, single-file observation, fs.watch alternatives, chokidar replacement, inotify/FSEvents/watchman backend selection, ignore globs, watcher debouncing, or refcounted shared subscriptions. Covers backend dispatch, the watchman invocation path, ignore handling, post-install reconciliation, and the failure modes Kolu's logger surfaces.
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。
EU Compliance Officer for MiFID II investment firms and IDD insurance distributors. Implements compliance frameworks, ensures National Competent Authority reporting, conducts product governance oversight, manages suitability and appropriateness testing, reviews marketing and inducements, coordinates cross-border passporting compliance, implements GDPR data protection, conducts AML (5AMLD/6AMLD) monitoring, and prepares for NCA inspections. Use for compliance manual development, MiFID II/IDD implementation, or ESMA guideline interpretation.
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。
Structures catastrophe risk assessment with model output interpretation and accumulation monitoring. Use when analyzing cat risk, interpreting cat model results, or managing cat 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。
Design drift alerts and escalation — thresholds, runbooks, and retrain triggers. Use when asked to "alert on model drift", "when should we retrain", or "write a drift escalation runbook".
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。
ML reconnaissance — inventory all models, pipelines, data sources, and monitoring. Use when asked "what ML do we have", "model inventory", or "ML assessment".
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 SLA framework -- response time targets, resolution time targets, tier definitions, and breach escalation process. Use when asked to "define our SLAs", "what response times should we commit to", "build a support tier structure", or "set up SLA 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。
Real-time log monitoring, crash detection, and auto-hotfixing code without human intervention / Pemantauan log real-time, deteksi kerusakan, dan perbaikan kode hotfix otomatis tanpa intervensi manusia.
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。
Expert guide for LLM observability in production — token cost tracking, hallucination rate monitoring, latency analysis, prompt/completion logging, model comparison dashboards, and AI-specific tracing (Langfuse, Helicone, Lunary, OpenTelemetry GenAI) / Panduan ahli observabilitas LLM di produksi — pelacakan biaya token, pemantauan tingkat halusinasi, analisis latensi, logging prompt/completion, dashboard perbandingan model, dan tracing khusus AI.
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。
Expert guide for autonomous API schema drift detection, OpenAPI / gRPC breaking change monitoring, self-healing SDK adapter generation, and zero-downtime client-server synchronization / Panduan ahli deteksi drift skema API otonom, pemantauan breaking change OpenAPI / gRPC, pembuatan adapter SDK self-healing, dan sinkronisasi client-server tanpa downtime.
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。
Generates MediatR Pipeline Behaviors for cross-cutting concerns like logging, validation, exception handling, caching, and performance monitoring. Implements the decorator pattern around handlers.
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。
PMU observation pattern for agent health monitoring. Runs patrol loops, detects stalled agents, sends nudges, and escalates persistent failures to the mayor. Never modifies agent work or resolves conflicts.
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。