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概要と使いどころ
"Analyze e-commerce performance using GA4 metrics, conversion funnel analysis, and key e-commerce KPIs. Use this skill when the user needs to evaluate online store performance, diagnose conversion drop-offs, set up e-commerce tracking, or create performance dashboards — even if they say 'why are sales down', 'optimize our online store', 'set up GA4 for e-commerce', or 'what metrics should we track'.".
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 effective data dashboards with proper KPI hierarchy, chart type selection, and interactive features. Use this skill when the user needs to create a dashboard, choose the right visualizations, organize metrics for different audiences, or evaluate dashboard tools — even if they say 'build a dashboard', 'our reports are confusing', 'which chart should I use', or 'executives can't find the metrics they need'.".
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 observability for production services — structured logging, RED metrics + Prometheus, Grafana dashboards, SLO-based alerting. Use when setting up monitoring or instrumenting a live service. Trigger on "add logging", "metrics", "dashboard", "alerting", "observability", "monitor this service".
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。
Phase 2 of the evidence verification pipeline. Validates GitHub star counts in registry skill files against live GitHub API data and flags stale or inflated metrics without repartitioning evidence by rank. Use this after /ev-collection has compiled the unified data lake and before /ev-adversarial-audit begins. Trigger phrases: "verify stars", "check star counts", "refresh stargazer metrics", "run star verification", "ev-star-verification", "Phase 2", "live star check".
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 ENCODE experiment quality using standard metrics and audit flags. Use when the user asks about data quality, wants to filter for high-quality experiments, needs to interpret quality metrics (FRiP, NSC, RSC, NRF, IDR, TSS enrichment, fragment size), wants to understand ENCODE audit warnings, needs to compare quality across experiments, or is deciding whether data is usable for their analysis. Also use when the user mentions QC, quality control, or data filtering.
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。
FrontendHire's job-search operating system (fh) — a strict pipeline tracker, deterministic weekly metrics, and a review ritual. Runs the search as a measured system where the philosophy is law. Use for logging applications/leads/referral asks, the weekly review, status/metrics, and the onboarding interview.
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 content engagement metrics, audience retention, attribution models, A/B test results, and recommendation engine effectiveness. Triggers: 'audit content metrics', 'evaluate recommendation engine', 'review retention curves', 'assess content A/B tests'.
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。
Investigates Google Cloud networking issues by analyzing logs, metrics, and diagnostics. Use when investigating VPC Flow Logs (including cost estimation), NAT, firewall, or threat logs, querying latency and throughput metrics, or running Connectivity Tests for path diagnostics. Don't use for generic VM management or non-observability 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。
Manages Google Analytics reporting data, enables the Analytics Data API via the Cloud CLI, and creates reports using the Google Analytics Data API (v1beta). Use when you need to interact with Google Analytics properties, run customized analytics reports, query metrics (like activeUsers, screenPageViews) and dimensions (like city, date), check metrics and dimensions compatibility, or verify API enablement. Don't use for Google Analytics Admin API operations (e.g., creating properties, managing users) or for front-end tracking installation.
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 testing, evaluating, or building regression suites for Agentforce agents: conversation testing in Agent Builder, topic (now subagent) coverage and utterance testing, Testing API and AiEvaluationDefinition metadata, Agentforce DX CLI test runs (sf agent generate test-spec, sf agent test create/run/resume/results/list), evaluation metrics (containment rate, escalation rate, CSAT, topic activation accuracy), and post-deploy analytics via Enhanced Event Logs. Triggers: 'how do I test my Agentforce agent', 'agent routes to wrong subagent', 'write utterance tests', 'regression test after topic change', 'measure agent quality', 'agent containment rate', 'run agent tests from the CLI'. NOT for agent creation, topic design, or action contract design — use agentforce/agentforce-agent-creation, agentforce/agent-topic-design, or agentforce/agent-actions respectively.
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 this skill to define, document, and validate KPI metrics for CRM Analytics — covering metric formula design, dimension selection, target-dataset modeling, benchmark setting, and the KPI register that must exist before any dashboard or lens is built. Trigger keywords: KPI definition CRM Analytics, analytics metric design, analytics target attainment, CRM Analytics measures vs dimensions, analytics benchmark. NOT for laying out the dashboard or lens itself — use admin/analytics-dashboard-design. NOT for scoping data sources, audiences, and drill-down paths — use admin/analytics-requirements-gathering.
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 a support metrics summary from [Gorgias](https://composio.dev/toolkits/gorgias) tickets and optionally push to [Google Sheets](https://composio.dev/toolkits/googlesheets)
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。
Investigates Google Cloud networking issues by analyzing logs, metrics, and diagnostics. Use when investigating VPC Flow Logs, NAT, firewall, or threat logs, querying latency and throughput metrics, or running Connectivity Tests for path diagnostics.
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。
VitePress documentation workflow for Software Metrics Machine docs under docs/vitepress. Use when creating, updating, reviewing, or restyling SMM documentation pages, especially feature docs that must keep CLI terminal examples and dashboard views/screenshots in sync. Covers Markdown style, VitePress tabs, command examples, dashboard screenshots, sidebar links, and docs validation. USE FOR: docs/vitepress, VitePress docs, documentation style, CLI docs, dashboard docs, screenshots, feature pages. DO NOT USE FOR: application code changes unless needed to verify documented behavior.
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。
Acceptance testing workflow for the Software Metrics Machine CLI e2e suite. Use when writing, extending, debugging, or reviewing bashunit acceptance tests under apps/cli/e2e, including command help checks, cached fixture flows, GitHub MSW-backed flows, local git repository fixtures, SMM_STORE_DATA_AT workspaces, and pnpm run test:cli:acceptance. USE FOR: CLI acceptance tests, e2e tests, bashunit, apps/cli/e2e, command smoke tests, persisted CLI output tests. DO NOT USE FOR: unit tests or Vitest tests (use tdd skill), linting (use lint skill), webapp browser tests.
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。
Linting and code quality workflow for Software Metrics Machine. Covers ESLint 9.x flat config, Prettier formatting, lint-staged (auto-fix on commit), per-workspace configs, TypeScript strict mode, and all lint commands. USE FOR: lint, eslint, prettier, format, code style, linting, lint fix, auto-fix, check style, code quality, lint-staged, typecheck, type-check, tsc noEmit. DO NOT USE FOR: writing tests (use tdd skill), building (use build commands), commit hooks setup.
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。
Test-Driven Development workflow for Software Metrics Machine. Covers test frameworks (Vitest 4.x for CLI/REST/core/utils, Jest 30.x for webapp), test patterns (describe/it, builder pattern, mocks via vi.fn), test commands, coverage requirements, and the Red-Green-Refactor cycle. USE FOR: write tests, run tests, test this, add tests, testing, coverage, TDD, test-driven development, vitest, jest, test pattern, test convention. DO NOT USE FOR: linting (use lint skill), building (use build commands directly), debugging production issues.
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。
Annotate speech/TTS datasets with per-clip quality metrics (MOS, SNR, STOI, pitch, bandwidth, speaking rate, WER/CER, gender/emotion), multi-ASR consensus transcription and speaker labeling using the audiogear pipeline; then filter clips by quality and build listenable HTML QA reports. Use when asked to compute audio dataset metrics, build extended_metadata.csv, transcribe unlabeled audio, select clean TTS training data, or QA a speech dataset.
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。
Heart rate variability biometrics and emotional awareness training. Expert in HRV analysis, interoception training, biofeedback, and emotional intelligence. Activate on 'HRV', 'heart rate variability', 'alexithymia', 'biofeedback', 'vagal tone', 'interoception', 'RMSSD', 'autonomic nervous system'. NOT for general fitness tracking without HRV focus, simple heart rate monitoring, or diagnosing medical conditions (only licensed professionals diagnose).
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。
Profiles DAG execution performance including latency, token usage, cost, and resource consumption. Identifies bottlenecks and optimization opportunities. Activate on 'performance profile', 'execution metrics', 'latency analysis', 'token usage', 'cost analysis'. NOT for execution tracing (use dag-execution-tracer) or failure analysis (use dag-failure-analyzer).
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 AI chatbot analytics and conversation monitoring. Use when adding conversation metrics, tracking AI usage, measuring user engagement with chat, or building conversation dashboards. Activates for AI analytics, token tracking, conversation categorization, and chat performance.
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。
Quantifies biodiversity from species abundance/incidence tables using Hill numbers (iNEXT) with coverage-based rarefaction-extrapolation (Chao & Jost 2012), asymptotic richness via Chao1/ACE/jackknife as a lower bound, Baselga turnover/nestedness partition with the Podani alternative as sensitivity check, mandatory Hellinger transformation before ordination (Legendre & Gallagher 2001), Faith PD and SES_MPD/SES_MNTD with explicit null-model choice, and Maire 2015 functional-diversity dimensionality optimization. Use when comparing diversity across sites with unequal sampling effort, picking the right richness estimator for singleton-heavy amplicon data, partitioning beta diversity into turnover vs nestedness, reporting Hill-number effective species counts rather than raw entropies, computing SES_MPD with explicit null-model justification, or deciding whether to apply standard metrics to compositional amplicon data. Not for clinical 16S microbiome diversity (see microbiome/diversity-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。
Re-run the analysis pipeline on all runs matching an RQ, reaggregate metrics, and propose findings updates against the fresh data. Trigger when the user says "reanalyze RQ-N", "reanalyse RQ-N", "Runs neu analysieren", or wants to refresh metrics/findings after a pipeline change (analyze-run.sh fix, adapter added, ESLint config).
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。
KPI体系設計とOKR策定支援スキル。ビジネス目標に整合したKPI設計、バランススコアカード、OKRフレームワーク、 ダッシュボード設計を提供。SMART原則、リーディング/ラギング指標の選定を支援。 Use when designing KPI frameworks, implementing OKR methodology, or creating performance measurement systems. Triggers: "KPI design", "OKR", "performance metrics", "balanced scorecard", "dashboard design", "measurement framework".