1.9천건 · 관련도순
Turn agent activity and transcript token counts into a cost report organized around completed outcomes. Separate estimates from measured spending, examine waste and rework, and export readable HTML with supporting JSON, CSV, and evidence.
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Estimate future spending from recent usage costs, with projections for 7, 30, 90, and 365 days. When a budget is configured, see how many days remain before spending reaches key budget thresholds.
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Compare actual AI session costs with estimates for using only Haiku, Sonnet, or Opus. The skill reports savings amounts and percentages, helping teams assess model selection costs while leaving differences in answer quality and retries outside the calculation.
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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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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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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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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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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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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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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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CodexBar의 로컬 비용 로그에서 Codex나 Claude의 비용을 모델별로 집계하고, 최근 대표 모델이나 전체 모델의 내역을 텍스트 또는 JSON으로 확인합니다.
Track production session spending over time by grouping costs into configurable time windows. Compare the latest window with earlier spending averages, display the increase, and optionally return a failure exit code when it exceeds a chosen percentage.
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Assess tasks before execution to identify transformations eligible to skip LLM calls. Report the eligible share, recommended models for remaining tasks, and estimated upper costs, then save the routing results for later cost reports.
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Generate a spending report from recorded AI usage, with token counts and USD costs grouped by model, agent, and processing tier. Check budget utilization, remaining funds, and alerts for a selected period.
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Review AWS spending and identify savings through resource sizing, unused resource cleanup, discount commitments, and storage policies. It also guides budget alerts and recurring cost reviews so teams can track spending and plan changes using usage data.
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