「metric」の検索結果

OpenStack monitoring operations skill for deploying, configuring, and operating the cloud health monitoring stack. Covers Prometheus metric collection and scrape targets, Grafana dashboard provisioning and visualization, Alertmanager notification channels and routing, alerting rules for service health and resource exhaustion, service endpoint health checks, log aggregation strategies, SLA tracking with availability and response time percentiles, and capacity trend analysis from historical metrics. Use when deploying monitoring via Kolla-Ansible, configuring alert thresholds, troubleshooting blank dashboards, tuning noisy alerts, or analyzing cloud performance trends.

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Developing for multicore systems in Zephyr RTOS. Covers Symmetric Multiprocessing (SMP), Asymmetric Multiprocessing (AMP) with OpenAMP/RPMsg, inter-processor communication (IPC) patterns, and Linkable Extensions (LLEXT). Trigger when designing for SoCs with multiple homogeneous or heterogeneous cores.

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Optimize existing CLAUDE.md, SKILL.md, agent definitions, and other AI-facing files for Claude comprehension and economy. Scope: optimization of existing content only — not upstream sync, not read-only auditing. Measures baseline metrics, delegates to the ai-doc-optimizer agent with file-type-specific context, runs independent verification via second agent, measures post-optimization metrics, and presents comprehensive before/after report. Supports iterative mode for large targets. Use when improving prompt effectiveness, reducing token waste, or rewriting instructions for LLM consumption. Invoke with /optimize-claude-md <file-or-directory>.

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The branded entry point to ServiceGraph — use whenever the user explicitly names **ServiceGraph** — "use ServiceGraph to…", "what datasets does ServiceGraph have", "search ServiceGraph for…", "look this up in ServiceGraph", "pull contacts from ServiceGraph for these domains", "how many credits do I have on ServiceGraph". ServiceGraph is a multi-dataset platform of metrics-enriched business data for founders — where to launch, who to email, who to hire. This skill explains how to drive the API (api.servicegraph.co / mcp.servicegraph.co) against ANY dataset — discover what datasets exist, discover a dataset's schema and filters, search free brief rows, and unlock contact + metric detail with credits. Dataset-agnostic by design — it discovers everything through the API and never assumes which datasets or fields exist. When the user describes an intent WITHOUT naming ServiceGraph (e.g. "find a PR agency in NY"), defer to the matching specific skill (find-pr-agency, find-marketing-agency, find-law-firm, …); this skill is for explicit ServiceGraph requests and for datasets no specific skill covers yet. Skip non-US firms, consumer/personal services, and individual freelancers.

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Query and manage Datadog monitoring data — logs, metrics, monitors, dashboards, events, SLOs, traces, and incidents. Use this whenever the user wants to search logs, look at a metric, check which monitors are alerting, investigate a trace, pull SLO status, mute an alert, or ask "what's happening in Datadog" — even if they don't say "API". Also use it for any URL under *.datadoghq.com. Always start from this skill when interacting with this service — its bundled scripts and recipes are the fastest path.

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设计产品成功指标和埋点时使用。适用于 PRD 第 12 节、上线效果评估、季度复盘。优先使用 Google HEART 五维度 + GSM（Goals-Signals-Metrics）流程。

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设计指标体系时使用。适用于北极星指标、AARRR 海盗模型、漏斗指标、GSM（Goals-Signals-Metrics）框架。帮助团队从业务目标出发，建立可量化、可追踪、可行动的指标体系。

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Diagnoses firing AWS CloudWatch alarms by querying CloudWatch Metrics, alarm history, and related AWS Config resource snapshots via the AWS SDK. Correlates metric anomalies with recent infrastructure changes to suggest root cause hypotheses. Outputs a structured incident summary with remediation options.

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Executes automated diagnostics using the AWS Systems Manager Automation API and SSM Documents. Collects system metrics via the CloudWatch GetMetricData API and correlates with AWS Health events.

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Builds CloudWatch Logs Insights queries and metric alarms using AWS SDK v3 (@aws-sdk/client-cloudwatch-logs, @aws-sdk/client-cloudwatch). Generates cross-account observability dashboards with CloudWatch Metrics Insights.

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Diagnoses Kafka consumer group lag using the Kafka AdminClient API and JMX metrics exposed via the Confluent Metrics API. Identifies slow consumers, topic partition hotspots, and broker rebalance storms that contribute to lag growth. Provides a step-by-step runbook to tune fetch.min.bytes, max.poll.records, and partition count.

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Strategic product leadership guidance for SaaS and technology companies. Covers product strategy, roadmap planning, product discovery, user research, growth product management, platform strategy, product analytics, and product launches. Use when defining product vision, prioritizing features, conducting discovery, analyzing metrics, or launching products. Use for "product strategy", "roadmap planning", "feature prioritization", "product discovery", "product metrics", "PRD writing".

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The Mileva Method (CRISP) — Phase 2: Results. Outcome alignment, stakeholder register, baseline measurement, success metrics, tradeoff negotiation. Use after problem definition is complete. Triggers on "results", "phase 2", "define success", "success metrics", "what does done look like", or after Phase C exit checklist is complete.

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Detect which external metric sources apply to this product (GitHub, Plausible, Stripe, etc.) and configure adapters. Retrofit entry point for projects that started before v0.14; also runnable to refresh source list when the product grows.

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Assess delivery health metrics. For software: DORA + APEX. For content/AI/service products: product-type-appropriate metrics.

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Computes fund performance with gross/net IRR, MOIC, DPI, RVPI, TVPI, PME, and direct alpha methodologies. Use when calculating fund performance, reconciling return metrics, or benchmarking against peer groups.

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Implement comprehensive model drift monitoring using Evidently AI, statistical tests (PSI, KS), and custom metrics to detect data drift and concept drift in production ML systems. Set up automated alerting and reporting workflows to catch degradation before it impacts business metrics. Use when production models show unexplained performance degradation, when new data distributions differ from training data, when seasonal shifts affect input features, or when regulatory requirements mandate model monitoring.

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Forecast infrastructure and application metrics using Prophet or statsmodels for capacity planning, cost optimization, and proactive scaling. Visualize predictions in Grafana and set up alerts for projected resource exhaustion. Use when forecasting infrastructure capacity needs for CPU, memory, or disk, planning hardware procurement for next quarter, predicting cost trends to optimize cloud spending, or setting up proactive scaling policies based on predicted load.

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Generate a project status report by reading existing artifacts (charter, backlog, sprint plan, WBS), calculating metrics, identifying blockers, and summarizing progress with RAG indicators for schedule, scope, budget, and quality. Use at the end of a sprint or reporting period, when stakeholders request a health update, before steering committee or governance meetings, or when a new blocker or risk materializes mid-project.

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Implement electronic signatures compliant with 21 CFR Part 11 Subpart C and EU Annex 11. Covers signature manifestation (signer, date/time, meaning), signature-to-record binding, biometric vs non-biometric controls, policy creation, and user certification requirements. Use when a computerized system requires legally binding electronic signatures for GxP records, when replacing wet-ink signatures in regulated workflows, when implementing batch release or document approval workflows, or when a regulatory gap reveals missing signature controls.

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Set up centralized log aggregation with Loki and Promtail (or ELK stack), including log parsing, label extraction, retention policies, and integration with metrics for correlation. Use when consolidating logs from multiple services into a searchable system, replacing local log files with centralized queryable storage, correlating logs with metrics and traces, implementing structured logging with label extraction, or troubleshooting production incidents requiring cross-service log analysis.

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Comprehensive primary skill for agents working with Weights & Biases. Covers both the W&B SDK (training runs, metrics, artifacts, sweeps) and the Weave SDK (GenAI traces, evaluations, scorers). Includes helper libraries, gotcha tables, and data analysis patterns. Use this skill whenever the user asks about W&B runs, Weave traces, evaluations, training metrics, loss curves, model comparisons, or any Weights & Biases data — even if they don't say "W&B" explicitly.

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Use Therapeutics Data Commons through the PyTDC Python package for registry discovery, approved dataset access, task-aware splits (scaffold, cold-start, temporal, combination), evaluator metrics, benchmark groups, and bounded molecular-oracle workflows. Use this skill to find which TDC datasets exist for a therapeutic task, load them with a split that does not leak, score predictions with the task's own official metric rather than a generic one, and run benchmark groups reproducibly. Also trigger on PyTDC, Therapeutics Data Commons, tdc.single_pred, tdc.multi_pred, ADMET Benchmark Group, scaffold split, get_split, or molecular oracles such as GSK3B, JNK3 and DRD2.

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Turn in vitro potency and animal pharmacokinetics into a defensible human dose projection — the arithmetic that decides whether a compound can reach its target concentration safely. Use this skill for non-compartmental analysis of a concentration-time profile (AUC, Cmax, terminal half-life, clearance, volume of distribution), one- and two-compartment simulation of a dosing regimen, interspecies allometric scaling, human-equivalent dose conversion by body surface area, and the exposure margin between a projected therapeutic concentration and a toxicology no-effect level. Also trigger on non-compartmental analysis, AUC, clearance, volume of distribution, allometric scaling, human equivalent dose, first-in-human, NOAEL, therapeutic index, or exposure margin.

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