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概要と使いどころ
"Apply rigorous survey design principles including construct operationalization, Likert scale development, reliability and validity assessment, and common method variance control. Use this skill when the user designs questionnaires, develops measurement items, needs to evaluate Cronbach's alpha or AVE, or when they ask 'how do I operationalize this construct', 'is my scale reliable', or 'how do I control for CMV'.".
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 6-phase debugging. Build feedback loop first, reproduce deterministically, hypothesize with ranked falsifiable theories, instrument one variable at a time, fix with regression test, cleanup. Use when a bug exists, tests fail unexpectedly, or behavior is wrong and cause is unknown.
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 a task needs the judgment of a DevOps/Site Reliability Engineer — designing deployment pipelines, setting SLOs/error budgets, responding to or reviewing an incident, or making infrastructure/reliability tradeoffs. Distinct from a backend software engineer role — this one owns the system's operational health across all services, not one service's feature code.
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 a task needs the judgment of a Chemical Plant and System Operator — reconciling a batch's mass balance to catch a leak or unaccounted reaction that individually normal readings would miss, responding to an early exotherm temperature trend before it accelerates, prioritizing simultaneous alarms by consequence severity rather than order of occurrence, or flagging a combination of near-limit variables that no single-variable check would catch.
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。
Write or refine a research hypothesis. Use when the user says "write a hypothesis", "frame my hypothesis", "is my hypothesis testable", "null and alternative hypothesis", "research hypothesis", "refine my hypothesis", "hypothesis statement", "H0 and H1", "operationalize my hypothesis", "turn my research question into a hypothesis", or needs to convert a research question or intuition into a falsifiable, testable hypothesis with clear variables and predictions - even if they don't explicitly say "hypothesis".
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。
Scope a milestone to a fixed time window with variable scope. Use when the user says "scope this milestone", "what fits in the sprint", "cut scope for the release", "define the MVP for this milestone", "we need to ship by [date] what do we cut", "shape the work to fit the cycle", or needs to make hard scope decisions under a time constraint - even if they don't explicitly say "milestone scoping". Based on "Shape Up" by Ryan Singer (appetite, shaping, betting table) and "The Art of Project Management" by Scott Berkun (milestones as forcing functions, scope as a variable).
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。
Master skill — set up the full agent harness for any repo so an agent can work it reliably: legible (map-not-manual docs + custom lints), executable (one-command dev stack), verifiable (e2e gate + a verify-before-ship loop), plus commit hygiene and entropy control. Use when onboarding a new/unfamiliar codebase to agent-driven development — "set up the harness", "make this repo agent-ready", "harness this codebase".
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。
Orchestrates comprehensive production readiness reviews and assessments for GKE clusters and workloads across scalability, security, reliability, observability, backup/DR, and cost optimization. Use when asked to productionize, prepare, assess, audit, or review a GKE cluster or workload before going live to production. Don't use for deep-dive single-domain implementation (use specific domain skills like gke-workload-scaling, gke-platform-security, gke-workload-security, gke-service-networking, gke-reliability instead).
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。
Configures best-practice alerting policies for AI agents using OpenTelemetry (OTel) metrics, generating output as Terraform (.tf) configuration files. Use when analyzing, writing, or deploying alerting policies to monitor agent latency, error rates, token usage, and quality metrics. Don't use for standard infrastructure monitoring unrelated to AI agents, or when the agent is not instrumented with OpenTelemetry (for Reliability, Cost, Safety, Security alerts). NOTE: Reliability, Cost, Safety, and Security alerts use generic OTel metrics and work across runtimes (such as Cloud Run, Vertex AI). Quality alerts rely on Vertex AI Online Monitors and are strictly bound to Vertex AI 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。
Design Agentforce conversations that span multiple turns without losing context: session variable scoping, conversation memory, clarifying-question patterns, topic-to-topic (now subagent) handoff, and the right abstractions for accumulating state across turns. NOT for deciding the topic boundaries themselves or out-of-scope behavior — use agentforce/agent-topic-design. NOT for single-turn agent actions and their input/output contracts — use agentforce/agent-actions.
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 calculus expert covering limits, derivatives, integrals, sequences, series, and multivariable calculus with rigorous proofs and practical applications
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。
Drafts a U.S. ocean Bill of Lading (B/L) functioning as receipt, contract of carriage, and document of title under COGSA or Hague-Visby. Covers negotiable and straight B/Ls, cargo description, freight allocation, and core carriage clauses. Use when drafting or reviewing a bill of lading, master/house B/L, ocean shipment documentation, or title transfer. Trigger keywords: bill of lading, B/L, ocean carriage, COGSA, Hague-Visby, negotiable, straight, master bill, house bill.
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。
Generates structured U.S. aviation-law summaries by synthesizing FAA, DOT, and TSA rules, treaties, and precedent into actionable legal analysis. Use when briefing on air traffic rights, aircraft operations, airworthiness, carrier liability, or incident/compliance exposure. Trigger: aviation law, FAA, DOT, TSA, 14 CFR, Part 121/125/135, Montreal Convention, Warsaw Convention, ICAO, route authority, code-sharing, slot allocation, accident liability.
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。
Produces structured aviation law summaries with Bluebook citations covering U.S. and international regulatory frameworks, treaties, and case law. Use when drafting an aviation law summary, FAA/DOT/TSA compliance overview, Montreal/Warsaw Convention liability analysis, air service agreement review, accident liability synopsis, or aviation regulatory research memo.
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。
Environment configuration manager for 12-factor config, feature flags, and multi-environment management. Activate on: environment variables, ConfigMap, feature flags, 12-factor config, dotenv management, config per environment, runtime config. NOT for: secret storage (use secret-management-expert), IaC provisioning (use terraform-module-builder), CI/CD variables (use github-actions-pipeline-builder).
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 building Grafana dashboards backed by Prometheus, Loki, or Tempo, designing PromQL/LogQL queries, wiring template variables, setting alert rules, building SLO dashboards, or maintaining dashboards as code. Triggers: rate() vs increase() confusion, irate vs rate, label_replace, recording rules, alerting rule expressions, multi-dimensional template variables, ad-hoc filters, dashboard JSON model, provisioning via Terraform/grafonnet, p99 / histogram_quantile usage. NOT for Datadog/New Relic dashboards (vendor-specific), Grafana plugin development, or Loki ingestion pipeline tuning.
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。
Assigns confidence scores to agent outputs based on multiple factors including source quality, consistency, and reasoning depth. Produces calibrated confidence estimates. Activate on 'confidence score', 'how confident', 'certainty level', 'output confidence', 'reliability score'. NOT for validation (use dag-output-validator) or hallucination detection (use dag-hallucination-detector).
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Designs genomics experiments so technical nuisance variation (batch, lane, plate, flow cell, operator, reagent lot, processing day) is balanced against the biological variable of interest and therefore estimable rather than confounded, using constrained sample-to-batch assignment (designit, OSAT), the confounder/mediator/collider distinction, and the principle that no post-hoc correction recovers a fully confounded design. Covers detecting hidden batches with surrogate variable analysis, a decision table for downstream correction (ComBat-seq, RUVSeq, SVA) whose execution is deferred to differential-expression/batch-correction, and reproducibility metadata. Use when assigning samples to sequencing batches/lanes/plates, avoiding batch-condition confounding, deciding whether a design is salvageable by correction, choosing a correction method, or estimating the number of hidden batches. For the experimental unit, randomization, and blocking concepts see experimental-design/randomization-blocking.
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。
Suggests initiating inpatient CGM for select diabetic patients at high risk for hypoglycemia based on Table 3 criteria. Triggers include "Should I initiate CGM for this hospitalized diabetic patient at high risk for hypoglycemia?" or "Is inpatient CGM warranted for this patient?"
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。
This skill determines whether inpatient diabetic patients meet high-risk criteria for CGM initiation using Table 3 factors such as age ≥65 years, BMI ≤27 kg/m², type 1 diabetes, insulin regimen, and specific clinical conditions. It is triggered by clinician questions like "Should I assess this inpatient for CGM initiation based on hypoglycemia risk?" or "Is this patient a candidate for inpatient CGM?"
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。
This skill checks for contraindications or sources of inaccuracy that may affect continuous glucose monitoring (CGM) reliability in hospitalized adults. It is triggered when clinicians ask, “Are there factors that could make CGM unreliable?” or when assessing for skin infection, hypoperfusion, hypovolemia, vasoactive/pressor therapy, or medications such as acetaminophen >4 g/day, dopamine, vitamin C, or hydroxyurea.
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。
Suggests that clinicians continue CSII in hospitalized patients with type 1 or type 2 diabetes when the institution has clear protocols for evaluating pump candidates and appropriate monitoring and safety procedures. Trigger phrases include: 'Admitted patient on pump, check hospital pump policy' and 'Inpatient with diabetes using pump, verify institutional guidelines'.
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。
Recommends use of hybrid closed-loop pump rather than insulin pump with CGM (without algorithm) or multiple daily injections with CGM for pregnant individuals with type 1 diabetes. Triggered when deciding on insulin delivery methods for pregnant patients with type 1 diabetes.
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。