「cost」の検索結果

Decide when to order coronary artery calcium (CAC) scoring in a primary-prevention patient and interpret the result per the 2026 ACC/AHA multisociety dyslipidaemia guideline. Covers the Class 1 indication (men ≥40 / women ≥45, borderline / intermediate PREVENT-ASCVD risk, uncertainty about LLT), CAC score interpretation (0, 1–99, 100–299, ≥300, ≥1000), how CAC modifies LDL and non-HDL goals, when to repeat CAC, and pitfalls (incidental CAC on non-cardiac CT, radiation, cost). Use when a clinician asks should I order a CAC score, CAC indications, what does CAC 150 mean, treatment goals with CAC score, repeat CAC frequency, CAC in a smoker, CAC vs CCTA, is CAC useful in intermediate risk. Grounded in the 2026 ACC/AHA multisociety dyslipidaemia guideline.

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Accounts for estrogen-induced elevation of total serum cortisol via increased corticosteroid-binding globulin (CBG) when evaluating adrenal reserve or hydrocortisone replacement adequacy. Triggered when assessing glucocorticoid adequacy in patients receiving estrogen therapy (e.g., oral estrogen replacement) or interpreting cortisol levels in estrogen-exposed individuals.

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Design production-grade multi-agent workflows with clear pattern choice (sequential, parallel, hierarchical), handoff contracts, failure handling, and cost/context controls. Use when architecting a multi-step agent pipeline, choosing between single-agent vs multi-agent approaches, or refactoring an LLM workflow that suffers from context bloat or unreliable handoffs.

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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.

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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.

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Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability.

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Use when the user asks to design a multi-agent system, pick an orchestration pattern (supervisor/swarm/pipeline), generate tool schemas for agents, or evaluate agent execution logs for cost, latency, and failure bottlenecks. Examples: 'design an agent architecture for research automation', 'generate Anthropic tool schemas from these tool descriptions', 'analyze these agent run logs for bottlenecks'. NOT for Claude Code workflow files (use workflow-builder) or single-agent prompt design (use agent-workflow-designer).

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Autonomous DevSecOps & FinOps Guardrails. Orchestrates Gemini 3 Flash to audit Linux Kernel patches, Terraform cost drifts, and K8s compliance.

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Review proposed abstractions, dependencies, and configuration for concrete consumers and maintenance cost when simplifying a design or checking overengineering.

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Use for Fermi estimation, or when a rough number is needed fast: "roughly how many...", "ballpark the cost", "estimate capacity or market size", "is this even feasible?". Decomposes, uses ranges and sanity-checks. Skip when exact data is available or a precise calculation is wanted.

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Use for first principles thinking, or when the user questions an inherited default: "why do we do it this way?", "is this cost really necessary?", "should we copy what competitors do?". Strips analogies and rebuilds from fundamentals. Skip for convention-following checklists.

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Implement a ready rolling-wave slice without locking it down with broad tests yet. Use when the user explicitly wants to code the current docs/rolling-wave/{project}/slices/NNN-slug.md slice, mark it in progress while working, implement small or cohesive work in the main context by default, use one to three gpt-5.6-terra workers only for genuine parallel chunks or large discovery/mechanical work where delegation pays for its handoff cost, pause or hand off chunks marked human-fit when appropriate, record implementation notes and final-test backlog, preserve the original slice contract, and mark completed implementation ready for review.

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Prepare and risk-check the next rolling-wave slice for implementation. Use when the user wants to continue a rolling-wave project, draft one bounded slice in the main context by default, use a fresh planning author only when context or discovery cost warrants it, use a fresh preparation reviewer when semantic risk or uncertainty warrants independent pressure-testing, decompose parallel implementation chunks where appropriate, identify agent, human, either, or hybrid execution fit, write or update docs/rolling-wave/{project}/slices/NNN-slug.md, and mark that slice ready even when other slices are active.

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Review local git changes before opening a PR or merging. First review all local tracked and untracked files; if none exist, review the current branch against a requested base branch or the Git Town parent branch. Use zero to three review subagents only when risk, diff size, language specificity, or uncertainty justifies the token cost, then report high-confidence bugs, regressions, risky assumptions, and missing tests. Use when the user asks for code review, local review, branch sanity check, pre-PR review, or review of work-in-progress changes.

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Audit one or all supported agent harnesses (claude, codex, opencode, pi, omp, antigravity, cursor, grok) for config efficiency, hook overhead, permission friction, plugin and MCP cost, skill metadata portability, dead config, and measured session friction. Use when invoked as `$optimize-harness [harness ...] [focus]`, or when the user asks to optimize or compare agent harness configuration.

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Cloud guardrails for any vendor workload — Google Cloud (GCP, Vertex AI, GKE), AWS (IAM, EKS, Bedrock), Azure (Entra ID, Policy, AKS), Alibaba Cloud (RAM, mainland/international residency). Enforces identity least-privilege, mechanical policy, data boundaries, residency, cost caps, network egress and observability, with official-source validation before any claim. Trigger on any cloud infrastructure design, review, Terraform plan, or LLM/agent deployment; the-architect routes here.

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Use when configuring or optimising GitHub repositories for cost-effective, consistent operations — CI triggers, Actions billing, issue tracking, labelling, branch protection, or release workflow.

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Go / no-go gate before any deployment. Checks failure modes, rollback plan, cost, production bar, and definition of done. Trigger before releasing to any environment that carries real consequences.

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Use when surfacing tech debt as trackable items — interest-vs-principal framing, prioritisation by carrying cost, repayment plan — even if the user just says 'this codebase is a mess'.

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Use when stakeholders pull a decision in different directions — frames each lens, builds a trade-off matrix, surfaces the cost of every choice — even if the user just says 'PO and ops disagree'.

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Use when shaping team structure — functional vs squad, span-of-control, reorg cost, Conway-aware boundaries. Triggers on 'should we reorg', 'how do we split this team'.

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Use when shaping a Livewire component before code — full-page vs partial, parent/child split, event flow, state-vs-props boundary, hydration cost — even on 'add this Livewire component'.

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Black-box evaluation of a shipped LLM feature — adversarial probes for hallucination, prompt-injection, and cost-runaway vs stated expectations. Not RAG/embedding. Triggers 'review my chatbot'.

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Use when deciding insource vs outsource vs acquire — integration-cost analysis, dependency-risk, optionality preservation. Triggers on 'should we build', 'buy vs partner'.

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。