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概要と使いどころ
Post-processing: production-level bloom, vignette, depth, composer lifecycle. Use when implementing, reviewing or testing post-processing for React, Vite, Next.js or Perfect_AI projects.
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。
Postgres performance optimization and best practices from Supabase. Use this skill when writing, reviewing, or optimizing Postgres queries, schema designs, or database 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。
Azure PostgreSQL Flexible Server SDK for .NET. Database management for PostgreSQL Flexible Server deployments.
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。
Connect to Azure Database for PostgreSQL Flexible Server from Node.js/TypeScript using the pg (node-postgres) package.
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。
Comprehensive AWS cost analysis and optimization recommendations using AWS CLI and Cost Explorer
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。
Implement API Security Posture Management to continuously discover, classify, and score APIs based on risk while enforcing security policies across the API lifecycle.
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。
iOS プロジェクトのエラー・警告を XcodeBuildMCP 経由で診断する。「check」「errors」「warnings」「diagnostics」「issues」「compile check」「lint」「エラーチェック」「コンパイルチェック」「警告チェック」などのキーワードで自動適用。
コスト見積もりエージェント - クラウドインフラ、ScalarDB、その他ライセンスの見積もりを作成。/estimate-cost [対象パス] で呼び出し。
Configures host-based intrusion detection systems (HIDS) to monitor endpoint file integrity, system calls, and configuration changes for security violations. Use when deploying OSSEC, Wazuh, or AIDE for endpoint monitoring, building file integrity monitoring (FIM) policies, or meeting compliance requirements for change detection. Activates for requests involving HIDS configuration, file integrity monitoring, OSSEC/Wazuh deployment, or host-based detection.
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。
Facilitate structured post-incident reviews to identify root causes, document what worked and failed, and produce actionable recommendations to improve future incident response.
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。
Conducts comprehensive network penetration tests against authorized target environments by performing host discovery, port scanning, service enumeration, vulnerability identification, and controlled exploitation to assess the security posture of network infrastructure. The tester follows PTES methodology from reconnaissance through post-exploitation and reporting. Activates for requests involving network pentest, infrastructure security assessment, internal network testing, or external perimeter 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。
Guides and best practices for working with Neon Serverless Postgres. Covers getting started, local development with Neon, choosing a connection method, Neon features, authentication (@neondatabase/auth), PostgREST-style data API (@neondatabase/neon-js), Neon CLI, and Neon's Platform API/SDKs. Use for any Neon-related questions.
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。
Required reading before writing any HogQL/SQL or calling execute-sql against PostHog. Use whenever the user wants to search, find, or do complex aggregations PostHog entities (insights, dashboards, cohorts, feature flags, experiments, surveys, hog flows, data warehouse, persons, etc.) and query analytics data (trends, funnels, retention, lifecycle, paths, stickiness, web analytics, error tracking, logs, sessions, LLM traces). Covers HogQL syntax differences from ClickHouse SQL, system table schemas (system.*), available functions, query examples, and the schema-discovery workflow.
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。
Focused Signals scout for PostHog projects using feature flags. Watches the flag roster and the `$feature_flag_called` evaluation stream for contradictions between a flag's configured state and its real traffic: evaluation cliffs on healthy flags, ghost flags (code calling keys that no longer exist), response-distribution shifts with no corresponding flag edit, and flag debt (stale, fully-rolled-out, or dead flags still burning evaluations). Emits findings only when they clear the confidence bar; otherwise writes durable memory and closes out empty. Self-contained peer in the signals-scout-* fleet — no dependencies on other skills.
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。
Add PostHog LLM analytics to trace AI model usage. Use after implementing LLM features or reviewing PRs to ensure all generations are captured with token counts, latency, and costs. Also handles initial PostHog SDK setup if not yet installed.
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。
Add PostHog SDK integration to your application. Use when setting up PostHog for the first time or reviewing PRs that need PostHog initialization. Covers SDK installation, provider setup, and basic configuration for any framework.
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。
Resolves a PostHog experiment reference from natural language to a concrete experiment ID by browsing `experiment-list` (not feature-flag tools), with disambiguation when multiple experiments match. Use when the user names or quotes an experiment ("split test demo", "the File engagement boost experiment", "onboarding retention test", "landing page hero experiment", "pricing experiment"), describes it loosely ("the signup experiment", "my pricing test", "the one with the new checkout"), uses a relative reference ("latest", "most recent", "the one I created yesterday"), filters by status (running, draft, stopped, archived), or otherwise refers to an experiment by anything other than its concrete ID.
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。
ABSOLUTE MUST to debug and inspect LLM/AI agent traces using PostHog's MCP tools. Use when the user pastes a trace or session URL (e.g. /ai-observability/traces/<id> or /ai-observability/sessions/<id>), asks to debug a trace, figure out what went wrong, check if an agent used a tool correctly, verify context/files were surfaced, inspect subagent behavior, investigate LLM decisions, or analyze token usage and costs. Also use when raw SQL/HogQL against `events.properties.$ai_input` / `$ai_output_choices` returns empty — message content lives only on the dedicated `posthog.ai_events` table.
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。
Help users debug PostHog Error Tracking stack-trace symbolication for any supported platform — JavaScript/TypeScript web, React Native (Hermes), Android (Proguard / R8), or iOS / macOS (dSYM). The PostHog symbol-set lookup flow is universal across platforms; build-tool and artifact details live in per-platform references (JavaScript is fleshed out, others come as we encounter them). Use when stack frames stay minified or obfuscated after symbols are uploaded, PostHog symbol sets show last_used but frames are not readable, chunk IDs or dSYM UUIDs do not match, "Token not found" appears, uploaded source maps / dSYMs / Proguard mappings look empty, or bundler / symbol-upload configuration needs troubleshooting.
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。
Diagnostic test / biomarker accuracy — sensitivity, specificity, PPV, NPV, likelihood ratios, accuracy from a 2x2 table; ROC curve, AUC, and the optimal cutoff (Youden) for a continuous biomarker; and post-test probability via Bayes. Use when you have test results vs a gold standard (binary 2x2, or a continuous score + true labels) and need to judge how good the test is, pick a threshold, or compute the probability of disease given a result. Emphasizes the prevalence-dependence of PPV/NPV.
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。
Builds diagnostic and prognostic classifiers on omics feature matrices with regularized logistic regression, random forest, and gradient-boosted trees, handling the p>>n regime, batch shortcut learning, class imbalance, and probability calibration. Use when building a classifier from expression, methylation, or variant data, choosing an algorithm for high-dimensional small-n data, or diagnosing a suspiciously perfect AUC. For unbiased evaluation see machine-learning/model-validation; for feature selection see machine-learning/biomarker-discovery; for time-to-event outcomes see machine-learning/survival-analysis.
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Builds and validates predictive time-to-event models on clinical and omics data with penalized Cox, random survival forests, gradient-boosted and deep survival models, and prediction-grade evaluation (Uno's C, time-dependent AUC, integrated Brier, calibration, competing risks). Use when building an individualized risk predictor or prognostic omics signature, choosing a survival model, or evaluating one beyond the C-index. For Kaplan-Meier, log-rank, and classical Cox hazard-ratio inference in a trial see clinical-biostatistics/survival-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。
Manage AI costs with usage tracking and optimization. TRIGGERS - Use when user needs help with ai-cost-management 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。
Implement activity-based costing systems. TRIGGERS - Use when user needs help with activity-based-costing 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。