「metrics」の検索結果

Analyze trends across session metrics. Computes windowed aggregates, deltas, and compares against MEMORY.md findings. Use periodically for progress 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。

Compute metrics for Claude Code sessions. Discovers via ccrider, filters trivial, computes friction/opportunity/fingerprint scores. Use for broad session triage.

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。

Claude Code session analysis, tool call patterns, efficiency metrics, and anti-pattern 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。

Code quality checks, formatting, and metrics via qlty CLI

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。

Chunking strategies, embedding model selection, hybrid search, reranking, eval 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。

Structured logging with Pino/Winston, OpenTelemetry tracing, metrics collection, Grafana dashboards, and alerting rules.

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。

Autonomous experiment loop: hypothesize > modify > test > evaluate > keep/discard > repeat. Run N experiments automatically with measurable metrics. Works for performance optimization, A/B testing, prompt engineering, and any measurable improvement task.

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。

CTO-level advisory - build vs buy decisions, tech debt quantification, team scaling, ADRs, technology evaluation (RICE), budget planning, TCO, vendor management, DORA/SPACE 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。

Developer relations - community building, documentation, DevRel 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。

Content marketing strategy - content audit, editorial calendar, SEO optimization, content funnel (TOFU/MOFU/BOFU), performance metrics, developer content, social media planning

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。

Time-series database implementation for metrics, IoT, financial data, and observability backends. Use when building dashboards, monitoring systems, IoT platforms, or financial applications. Covers TimescaleDB (PostgreSQL), InfluxDB, ClickHouse, QuestDB, continuous aggregates, downsampling (LTTB), and retention policies.

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。

Creates comprehensive dashboard and analytics interfaces that combine data visualization, KPI cards, real-time updates, and interactive layouts. Use this skill when building business intelligence dashboards, monitoring systems, executive reports, or any interface that requires multiple coordinated data displays with filters, metrics, and visualizations working together.

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。

Implements onboarding and help systems including product tours, interactive tutorials, tooltips, checklists, help panels, and progressive disclosure patterns. Use when building first-time experiences, feature discovery, guided walkthroughs, contextual help, setup flows, or user activation features. Provides timing strategies, accessibility patterns (keyboard, screen readers, reduced motion), and metrics for measuring onboarding success.

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 communication patterns for live updates, collaboration, and presence. Use when building chat applications, collaborative tools, live dashboards, or streaming interfaces (LLM responses, metrics). Covers SSE (server-sent events for one-way streams), WebSocket (bidirectional communication), WebRTC (peer-to-peer video/audio), CRDTs (Yjs, Automerge for conflict-free collaboration), presence patterns, offline sync, and scaling strategies. Supports Python, Rust, Go, and TypeScript.

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。

Monitoring, logging, and tracing implementation using OpenTelemetry as the unified standard. Use when building production systems requiring visibility into performance, errors, and behavior. Covers OpenTelemetry (metrics, logs, traces), Prometheus, Grafana, Loki, Jaeger, Tempo, structured logging (structlog, tracing, slog, pino), and alerting.

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 LLM systems using automated metrics, LLM-as-judge, and benchmarks. Use when testing prompt quality, validating RAG pipelines, measuring safety (hallucinations, bias), or comparing models for production deployment.

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。

Conduct multi-round deep research on any GitHub Repo. Use when users request comprehensive analysis, timeline reconstruction, competitive analysis, or in-depth investigation of GitHub. Produces structured markdown reports with executive summaries, chronological timelines, metrics analysis, and Mermaid diagrams. Triggers on Github repository URL or open source 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。

Audit a test file or suite; produce a severity-ranked diagnostic report. ALWAYS USE for tests that verify nothing, missing/tautological assertions, swallowed/broad exceptions, flaky/order-dependent tests, duplication, or magic values. Polyglot. DO NOT USE for direct edits: writing-mstest-tests owns supplied MSTest assertions/attributes/lifecycle; code-testing owns new tests. Exclude running tests, migration, assertion metrics (assertion-quality), raw .NET coverage collection (run-tests), non-.NET coverage collection/analysis (native tooling), project-wide .NET coverage/CRAP (coverage-analysis), named-target .NET CRAP (crap-score), behavioral/pseudo-mutation gaps (test-gap-analysis), test-mix/ happy-vs-error classification and trait distributions (test-tagging), or the testsmells.org catalog (test-smell-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。

Audits existing tests in any language using formal, research-backed test smell names and the testsmells.org 19-smell academic taxonomy. Use when the caller asks for an academic or citable test-smell review, named smell categories, or a formal severity-ranked smell assessment. Covers Assertion Roulette, Conditional Test Logic, Mystery Guest, Eager Test, Sleepy Test, Unknown Test, Sensitive Equality, and the rest of the catalog across .NET, Python, JavaScript/TypeScript, Java, Go, Ruby, Rust, Swift, Kotlin, PowerShell, and C++. DO NOT USE FOR a quick pragmatic test review (use test-anti-patterns), writing or running tests, framework migration, coverage, or assertion-diversity 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。

Activation requires either supplied .NET coverage reports/percentages/line, branch, or condition metrics, or an explicit request to collect .NET coverage for analysis. USE FOR: interpreting that evidence, including Cobertura data, partial conditions, plateaus, target arithmetic, project-wide coverage-backed CRAP, and coverage-backed refactoring safety. Analyze supplied reports directly without rerunning tests or installing tools. DO NOT USE FOR: requests with neither coverage evidence nor explicit coverage-collection intent, including hypothetical change-survival questions (use test-gap-analysis); CRAP or refactoring safety for one named target (use crap-score); or requests owned by test-tagging, find-untested-sources, test-anti-patterns, run-tests, or code-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。

Build structured semantic layer documentation for metrics, dimensions, and entities. Activate when you need to define a business metric, document a data model, or create YAML definitions compatible with dbt Semantic Layer or similar frameworks.

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 when you need to verify Java performance optimizations by comparing profiling results before and after refactoring — including baseline validation, post-refactoring report generation, quantitative before/after metrics comparison, side-by-side flamegraph analysis, regression detection, or creating profiling-comparison-analysis and profiling-final-results documentation. This should trigger for requests such as Verify performance fix; Verify the performance; Verify the memory; Verify the threading; Compare before and after profiling results; Check for performance regressions after refactoring. Part of Plinth Toolkit

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公众号信息源 — Daily scan of AI WeChat public accounts, finding the hottest articles by read count, auto-clustering into topics, and generating a styled HTML report with cover images, metrics, search, and subscription support. Use when the user wants AI daily reports, 公众号爆款, AI content curation, 每日AI热点, or 公众号日报.

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。

Auto-loaded context for Portfolio Buddy 2 development. Use for ANY task involving: React 19 development, TypeScript, portfolio analysis features, metrics calculations, trading strategy comparison, or working with the Portfolio Buddy 2 codebase. Contains tech stack, known issues, and architectural constraints.

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。