「metrics」の検索結果

Evaluates market bubble risk through quantitative data-driven analysis using the revised Minsky/Kindleberger framework v2.1. Prioritizes objective metrics (Put/Call, VIX, margin debt, breadth, IPO data) over subjective impressions. Features strict qualitative adjustment criteria with confirmation bias prevention. Supports practical investment decisions with mandatory data collection and mechanical scoring. Use when user asks about bubble risk, valuation concerns, or profit-taking timing.

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Use when profiling CUDA with Nsight Systems or Nsight Compute, reading roofline and occupancy metrics, or annotating phases with NVTX. Not for correctness: use cuda-debugging.

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。

Act as an interactive data analyst for ClickHouse-backed analytics. Use when the user asks questions about internal data, metrics, dashboards, telemetry, active users, revenue, funnels, trends, distributions, or wants an analyst-style conversation, ad hoc SQL, charts, or a data export against ClickHouse (local or ClickHouse Cloud).

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。

Act as an interactive data analyst for ClickHouse-backed analytics. Use when the user asks questions about internal data, metrics, dashboards, telemetry, active users, revenue, funnels, trends, distributions, or wants an analyst-style conversation, ad hoc SQL, charts, or a data export against ClickHouse (local or ClickHouse Cloud).

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。

Migrate workloads from Google Cloud Platform to AWS — including AI and agentic workloads regardless of cloud provider. Triggers on: migrate from GCP, GCP to AWS, move off Google Cloud, migrate Terraform to AWS, migrate Cloud SQL to RDS, migrate GKE to EKS, migrate Cloud Run to Fargate, Google Cloud migration, migrate from OpenAI to Bedrock, move off OpenAI, switch from ChatGPT API to AWS, migrate from Gemini to Bedrock, migrate LangChain to Bedrock, migrate LangGraph to AWS, migrate agentic workloads to AWS, move AI workloads to AWS, migrate my AI app to AWS. Runs a 6-phase process: discover GCP resources from Terraform files, app code, or billing exports, clarify migration requirements, design AWS architecture, estimate costs, generate migration artifacts, and collect optional feedback. Clarify must finish before Design, Estimate, or Generate. Includes AI provider migration guidance (for example, OpenAI to Amazon Bedrock) by selecting closest-fit Bedrock model families for required modality, latency/quality targets, context windows, and cost constraints. Model mapping is compatibility-guided, not 1:1 parity; validate prompts, tool-calling behavior, and eval metrics before cutover. Do not use for: Azure or on-premises migrations to AWS, AWS-to-GCP reverse migration, general AWS architecture advice without migration intent, GCP-to-GCP refactoring, or multi-cloud deployments that do not involve migrating off GCP.

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Operate Railway infrastructure: sign up for or sign in to a Railway account, create projects, provision services and databases, manage object storage buckets, deploy code, configure environments and variables, manage domains, troubleshoot failures, check status and metrics, set up Railway agent tooling, and query Railway docs. Use this skill whenever the user mentions Railway, signing up, creating an account, registering, logging in, deployments, services, environments, buckets, object storage, build failures, agent setup, MCP, or infrastructure operations, even if they don't say "Railway" explicitly. Also invoke this skill when the user asks to be signed up, registered, or onboarded to Railway: do not refuse — drive them through the unauthed `railway up` flow (deploys + signs up on the fly) or `railway login` (which creates new accounts on the fly).

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。

Weekly engineering retrospective. Analyzes commit history, work patterns, and code quality metrics with persistent history and trend tracking. Team-aware: breaks down per-person contributions with praise and growth areas. Use when asked to "weekly retro", "what did we ship", or "engineering retrospective".

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Aggregate DiT inference outputs into per-dimension VBench metrics via AISBench. Run after the quant-tuning-evaluate DiT workflow produces a directory of mp4s and the user supplies a VBench-1.0-mini JSON + pre-populated cache dir. Single mode (Standard).

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当用户希望使用 msOpProf（`msprof op` / `msprof op simulator`）对昇腾 AI 算子做上板或仿真性能调优、解释 `aic-metrics`/`trace.json`/`visualize_data.bin`、选择 device vs simulator 路径、排查 `--soc-version`/`--export`/`signal 6`/`Bad address`/热点图或流水图相关问题，或要求生成固定分析报告模板（算子基本信息 / 关键数据 TOP5 / 核心瓶颈 TOP5 / 优化建议 TOP5）时，使用本技能。它负责先判定模式、输入形态与芯片/能力边界，再给出正确命令、结果解读、固定报告输出与高频踩坑规避；不要把经验案例当成通用规则。

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Pre-Earnings Analysis — earnings date, analyst consensus estimates, 8-quarter historical surprise pattern, post-earnings price move history, options-implied expected move vs historical, key metrics to watch, and trade setup recommendations. Triggered by "trade earnings <TICKER>".

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Fundamental Analysis skill — valuation ratios, growth metrics, profitability, balance sheet health, competitive moat analysis, and management quality. Returns Fundamental Score (0-100). Triggered by "trade fundamental <TICKER>".

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Design, write, and grow editorial newsletters with consistent voice and format. Creates newsletter strategy, templates, and growth playbook. Use when someone wants to start a newsletter, improve an existing one, grow subscribers, write a newsletter issue, plan newsletter content, or set up on Substack, Beehiiv, Ghost, or ConvertKit. Triggers on 'newsletter', 'editorial', 'weekly email', 'subscriber growth', 'newsletter template', 'email digest', 'Substack', 'Beehiiv', 'newsletter strategy', 'newsletter issue', 'recurring email', or 'email content'. Even if they just say 'I want to email my audience regularly' or 'start a weekly email', this is the skill. Covers curated, editorial, and hybrid formats with referral programs, platform guidance, and engagement metrics.

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 free built-in .NET maintainability analyzers and code metrics configuration to find overly complex methods and coupled code. Use when a repo needs cyclomatic complexity checks, maintainability thresholds, or complexity-driven refactoring 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。

Evaluate AI model performance with metrics, benchmarks, and comparison. TRIGGERS - Use when user needs help with ai-model-evaluator 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。

Design activation metrics. TRIGGERS - Use when user needs help with activation-metric-design related tasks.

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Datadog provider for obz. Supports metrics (Datadog Query Language), logs (Datadog Log Query), and traces. Requires api-key and app-key configured under auth in config.yaml. Does not support metric labels, label-values, or series commands. This skill should be used when the user mentions "Datadog", "DD", "obz -p dd", Datadog Query Language, or needs to configure Datadog provider instances with regional endpoints.

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。

GreptimeDB provider for obz. Covers PromQL metric query, labels, label-values, and series commands via GreptimeDB's Prometheus-compatible API. Use this skill when the user mentions "GreptimeDB", "Greptime", "obz metric -p greptimedb", or needs to query metrics stored in a GreptimeDB instance.

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Grafana Mimir provider for obz. Covers all 6 metric commands using standard PromQL. Mimir is a horizontally-scalable Prometheus-compatible TSDB. This skill should be used when the user mentions "Mimir", "Grafana Mimir", "obz metric -p mimir", or needs to query metrics from a Mimir cluster.

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。

Prometheus provider for obz. Covers all 6 metric commands using standard PromQL. This skill should be used when the user mentions "Prometheus", "PromQL", "obz metric -p prom", or needs to query metrics from a Prometheus server.

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。

Multi-backend observability CLI for querying metrics, logs, and traces across 12 backends. This skill should be used when the user runs obz commands, constructs metric/log/trace queries, configures providers, parses obz JSON output, or troubleshoots obz errors. Covers all core commands (metric query/list/info/labels/label-values/series, log search, trace search/get), global flags, time formats, output modes, config files, and exit codes.

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Write a feature spec or PRD from a problem statement or feature idea. Use when turning a vague idea or user request into a structured document, scoping a feature with goals and non-goals, defining success metrics and acceptance criteria, or breaking a big ask into a phased spec.

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Review the operating health of a one-person company using lightweight metrics, bottleneck analysis, and stop-loss logic. Use when Codex needs to explain review concepts when needed, verify available outputs, ask one question at a time, present multiple bottleneck hypotheses, and write user-confirmed outputs into `opc-doc/`.

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Structured guide for setting up A/B tests with mandatory gates for hypothesis, metrics, and execution readiness.

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Analyzes campaign performance with multi-touch attribution, funnel conversion analysis, and ROI calculation for marketing optimization. Use when analyzing marketing campaigns, ad performance, attribution models, conversion rates, or calculating marketing ROI, ROAS, CPA, and campaign metrics across channels.

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。