“cost”的搜索结果

根据代理工作记录和词元数量，整理每项已完成成果及返工的费用，区分估算金额与实测支出，并生成附有依据的 HTML、JSON 和 CSV 报告。

• 核对每周支出与完成成果
• 比较每项已完成成果的费用
• 调查重复工作与返工费用

根据近期使用费用计算每日支出，预测未来7天、30天、90天和365天的费用；设置预算后，还可查看达到各预算消耗比例所需的天数。

• 规划季度预算
• 将费用预测接入预算看板
• 预算即将耗尽时停止构建

比较实际使用不同AI模型的费用与全部任务统一使用同一模型的估算费用，按时间范围输出节省金额和比例，帮助核查模型选择的成本表现。

• 在费用评审中报告节省效果
• 核查模型选择设置变更后的费用
• 在CI中检查节省比例门槛

Retrieve current Azure retail prices and estimate monthly or annual costs by service, region, and configuration. Compare payment options and calculate Copilot Studio credit usage and estimated costs from expected agent activity.

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• Estimate monthly and annual Azure costs
• Compare prices across regions and configurations
• Compare reservations, savings plans, and spot pricing

Reviews Google Cloud spending and resource usage to identify waste, recommend suitable resource sizes, compare discount options, and establish ongoing cost management using the cost optimization guidance in the Google Cloud Well-Architected Framework.

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• Reviewing Google Cloud spending by team or project
• Finding idle resources and oversized instances
• Comparing CUDs, SUDs, and Spot VMs

Reduce Google Kubernetes Engine costs by adjusting CPU and memory requests, setting resource quotas, and choosing Spot VMs or committed use discounts. Match spending decisions to steady usage, variable demand, and workloads that tolerate interruptions.

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• Reduce excessive CPU and memory requests
• Set resource quotas for teams sharing a cluster
• Move interruption-tolerant batch jobs to Spot VMs

Identify which Convex functions drive spending by combining data read per call with call volume. Use deployment evidence or query structure to explain how costs grow with usage and data size, then suggest the cheapest fix for each driver.

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• Find functions that drive Convex spending
• Assess cost growth as data and traffic increase
• Estimate future cost drivers before traffic arrives

Compare project options by valuing costs and benefits over time. Report net present value, the value remaining after discounted costs, separately for society and the sponsor, alongside funding needs and tests of how assumptions affect results.

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• Comparing project options against doing nothing
• Assessing social value alongside sponsor cash flow
• Testing changes in cost and benefit assumptions

AWS Cost Optimizer workflow skill. Use this skill when the user needs Comprehensive AWS cost analysis and optimization recommendations using AWS CLI and Cost Explorer and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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Warehouse cost reduction, auto-scaling, query optimization, and lifecycle policies for data infrastructure. Activate on: data cost, warehouse credits, cost reduction, auto-scaling, lifecycle policy, cold storage, cost monitoring, resource optimization. NOT for: query performance tuning (use data-warehouse-optimizer), batch job optimization (use batch-processing-optimizer).

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Audit LLM token cost estimates against actual API usage. Activate on 'cost verification', 'token estimate accuracy', 'API cost audit', 'estimation variance'. NOT for pricing lookups, budget planning, or cost optimization strategies.

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Track real-time API cost accrual during LLM execution. Activate on 'cost tracking', 'token usage', 'API costs', 'budget monitoring', 'usage metrics'. NOT for cost estimation, pricing tiers, or billing systems.

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Knowledge base from the GAO Cost Estimating and Assessment Guide (GAO-20-195G). Use for program cost estimating: the four characteristics of a reliable estimate (comprehensive, well documented, accurate, credible) and the 18 best practices; the 12-step cost estimating process (purpose, plan, technical baseline, WBS, ground rules and assumptions, data, point estimate, sensitivity, risk/uncertainty, document, present, update); estimating methods (analogy, parametric, engineering build-up, learning curves); Monte Carlo risk/uncertainty analysis, confidence levels and contingency; auditing and validating an estimate against the characteristics; earned value management (EIA-748, BCWS/BCWP/ACWP, CPI/SPI/TCPI, EAC, PMB, IBR); and specialized techniques (software cost estimating, learning curves, Analysis of Alternatives, WBS templates, the Green Book internal-control framework). This is the cost-estimating companion to the GAO Schedule and Technology Readiness Assessment guides. Does not cover schedule estimating/risk in depth (see the GAO Schedule Assessment Guide), agency-specific cost models, or the full text of referenced standards (EIA-748, MIL-STD-881D, the Green Book) — these are named, not reproduced.

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Use when working with Aws Cost Optimization — aWS native cost optimization using Compute Optimizer, Trusted Advisor cost checks, Savings Plan analysis, Cost Explorer, and resource rightsizing. Covers EC2 rightsizing, idle resource detection, Savings Plan and Reserved Instance recommendations, and cost anomaly detection. Use when optimizing AWS infrastructure costs with native AWS tools.

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Investigate LLM spend in PostHog — total cost over time, cost by model, provider, user, trace, or custom dimension, token and cache-hit economics, and cost regressions. Use when the user asks "how much are we spending on LLMs?", "which model / user / feature is most expensive?", "why did cost spike?", wants to build a cost dashboard or alert, or pastes a trace URL and asks about its cost.

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Cost modeling and ROI analysis for specialized LLM development. Use when deciding whether to train a custom model, estimating total cost, or calculating break-even vs frontier APIs. Covers training costs, inference costs, and time-to-ROI projections.

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Warehouse cost reduction, auto-scaling, query optimization, and lifecycle policies for data infrastructure. Activate on: data cost, warehouse credits, cost reduction, auto-scaling, lifecycle policy, cold storage, cost monitoring, resource optimization. NOT for: query performance tuning (use data-warehouse-optimizer), batch job optimization (use batch-processing-optimizer).

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Track real-time API cost accrual during LLM execution. Activate on 'cost tracking', 'token usage', 'API costs', 'budget monitoring', 'usage metrics'. NOT for cost estimation, pricing tiers, or billing systems.

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Audit LLM token cost estimates against actual API usage. Activate on 'cost verification', 'token estimate accuracy', 'API cost audit', 'estimation variance'. NOT for pricing lookups, budget planning, or cost optimization strategies.

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Locate BotProfile::Cost in CS2 server.dll / libserver.so via IDA Pro MCP and emit a fresh, minimal-unique signature or offset for the local gamedata entry "BotProfile::Cost" (symbol BotProfile_Cost). This is a VIRTUAL function - resolve the BotProfile::Cost vtable via RTTI and identify the slot, then emit the slot. Verify by xrefs from expected call sites. vtable slots commonly differ between platforms - never assume identical indices. Trigger: BotProfile_Cost, BotProfile::Cost

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从CodexBar本地费用日志中，按模型汇总Codex或Claude的使用费用，以文本或JSON查看最近的代表模型、全部模型或指定模型的费用。

• 查看最近代表模型的费用
• 比较全部模型的使用费用
• 汇总已导出的费用日志

根据AI智能体的使用记录，按模型、智能体和处理层级汇总令牌用量及美元费用，查看指定期间的预算使用率、剩余额度和预警。

• 查看今天的AI使用费用
• 比较各模型和智能体的费用
• 检查预算使用率与预警

按可调整的时间段汇总生产环境会话费用，将最新支出与历史平均值比较，显示差额和增幅。超过设定增幅时，通过退出码报告异常。

• 查看生产环境每日费用
• 比较近期支出与历史平均值
• 在持续集成中加入费用激增警报

在执行前对任务分流，报告被判定可跳过LLM调用的任务比例，为其余任务提供推荐模型与费用上限估算，并保存结果供后续费用报告使用。

• 批量处理相似任务前检查
• 审视简单代码转换的LLM费用
• 确认Agent Booster适用比例