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概要と使いどころ
Use this skill when the user asks about driver ergonomics — back/neck/shoulder injury prevention, seat adjustment + lumbar support, entering and exiting cab safely, load handling techniques, vibration reduction, stretching protocols, and how ergonomics affects workers compensation costs in trucking. Reference OSHA + ATA ergonomics research.
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 when the user asks the economic + operational tradeoffs of broker-mediated freight vs direct shipper relationships — margin difference, capital + operational cost, sales effort, payment terms, relationship investment, when to pursue direct, and the hybrid model. Reference industry research on freight broker margins.
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 use the Pre-Employment Screening Program (PSP) for evaluating driver applicants. Covers what PSP shows, cost, and how to interpret reports.
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。
Decision-ready scouting record for a user-connected provider access campaign. Use when an assigned Provider Access Launch task needs its easiest official route, minimum scopes, cost, account needs, approval path, implementation consumer, and easy-execute verdict established before submission. NOT for client Local Listings distribution.
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。
next/dynamic code splitting and build-graph cost; first stop for build failures. Use before lazy-loading or deferring a heavy component, adding a dynamic import, React.lazy, or ssr: false; on 'window is not defined' or a hydration mismatch; shrinking the bundle; or on 'build failed', OOM, SIGKILL, a slower build, or 'Collecting page data'.
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。
LLM API利用のコスト最適化パターン — タスク複雑度によるモデルルーティング、予算追跡、リトライロジック、プロンプトキャッシュ。
Patterns for scaling read-heavy Convex apps to millions of users. Use when optimizing bandwidth, reducing query costs, fixing slow queries, creating digest tables, replacing reactive subscriptions with one-shot fetches, adding compound indexes, debouncing writes, rate-controlling backfills, or running npx convex insights. Trigger when users mention "scale", "bandwidth", "performance", "optimize", "slow queries", "expensive queries", "digest table", "denormalize", or "thundering herd" in the context of Convex.
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。
Read the Convex deployment's 72h insights (read limits, OCC contention), root-cause each event in code, report evidence-backed perf/cost findings with fixes.
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。
Improve test value and feedback cost when auditing slow, flaky, redundant or brittle tests; choosing tests during implementation; or reviewing test changes before a commit or PR. Keep ordinary implementation and pre-commit use scoped to the affected behavior, not a repository-wide audit.
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。
The science of learning through controlled experimentation. A/B testing isn't about picking winners—it's about building a culture of validated learning and reducing the cost of being wrong. This skill covers experiment design, statistical rigor, feature flagging, analysis, and building experimentation into product development. The best experimenters know that every test, positive or negative, teaches something valuable. Use when "a/b test, experiment, hypothesis, statistical significance, sample size, feature flag, variant, control, treatment, p-value, conversion rate, test winner, split test, experimentation, testing, statistics, feature-flags, hypothesis, growth, optimization, learning, validation" mentioned.
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。
Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing.
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。
Codified expertise for electricity and gas procurement, tariff optimization, demand charge management, renewable PPA evaluation, and multi-facility energy cost management. Informed by energy procurement managers with 15+ years experience at large commercial and industrial consumers. Includes market structure analysis, hedging strategies, load profiling, and sustainability reporting frameworks. Use when procuring energy, optimizing tariffs, managing demand charges, evaluating PPAs, or developing energy strategies.
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。
Tools are how AI agents interact with the world. A well-designed tool is the difference between an agent that works and one that hallucinates, fails silently, or costs 10x more tokens than necessary. This skill covers tool design from schema to error handling.
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 DevSecOps & FinOps Guardrails. Orchestrates Gemini 3 Flash to audit Linux Kernel patches, Terraform cost drifts, and K8s compliance.
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。
Orchestrate autonomous AI development pipelines through your Kanban board (Asana, GitHub Projects, Linear). Manages multi-worker Claude Code dispatch, deterministic quality gates, adversarial review, per-task cost tracking, and crash-proof pipeline execution.
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。
Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability.
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。
OrgX operations execution contract for OpenClaw. Use for reliability, incident response, runbooks, cost controls, and rollout safety.
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。
Compare cross-border e-commerce shipping options, packaging tactics, customs-risk signals, and speed-versus-cost tradeoffs for sellers, operations teams, and marketplace operators. Use when choosing routes, carriers, declaration practices, or packaging plans for parcel shipments without relying on live carrier APIs.
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。
Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability.
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。
AEO — Agent 效果运营平台。自动发现 Agent 效果漂移、诊断根因、运行 AI 数据闭环工具链，给出 AI 效果优化方案。Measure agent performance, attribute outcomes, track quality trends, optimize cost, and monitor SLAs.
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 chatmode or prompt files and recommend optimal AI models based on task complexity, required capabilities, and cost-efficiency
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。
Go-to-market strategy for AI products. Use when positioning AI products, handling "who is responsible when it breaks" objections, pricing variable-cost AI, choosing between copilot/agent/teammate framing, or selling autonomous tools into enterprises.
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 Python + BigQuery pipelines for cost safety, idempotency, and production readiness. Returns a structured report with exact patch locations.
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 Azure infrastructure using natural language, or analyze existing Azure resources to auto-generate architecture diagrams, refine them through conversation, and deploy with Bicep. When to use this skill: - "Create X on Azure", "Set up a RAG architecture" (new design) - "Analyze my current Azure infrastructure", "Draw a diagram for rg-xxx" (existing analysis) - "Foundry is slow", "I want to reduce costs", "Strengthen security" (natural language modification) - Azure resource deployment, Bicep template generation, IaC code generation - Microsoft Foundry, AI Search, OpenAI, Fabric, ADLS Gen2, Databricks, and all Azure services
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。