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Answers questions about GKE spending using BigQuery billing exports and live cluster metrics. It breaks down costs by project, namespace, or workload and compares requested computing resources with actual usage to investigate cost increases.
Capture token usage from Claude Code session logs, calculate USD costs using the supplied pricing reference, and save structured records to AgentDB. Review totals by model and tier, and provide data for cost reports.
Review AI agent spending by conversation, with each session’s start time, message count, main model, and total cost. Use the results to identify expensive sessions and understand typical costs before planning longer work.
Compare two saved cost-summary JSON snapshots to identify changes in total spending, sessions, and model costs. Set limits on spending increases or token growth to flag cost regressions during pull request checks.
Estimate the cost per million tokens, units of model text, on hardware you own using power consumption, electricity rates, hardware costs, and measured generation speed. Compare utilization levels to find when local operation costs less than a hosted model.
Break down a Claude Code session's costs by assistant message to find expensive responses. Compare input, output, cache writes, cache reads, and models, with message cost percentiles providing context for investigating unusually costly activity.
Review cloud spending across AWS, Azure, GCP, and OCI. Guides resource sizing, discount commitments, storage policies, cost allocation tags, and budget alerts to identify waste and establish ongoing cost management while considering performance and reliability.
Manage cost basis calculation in TRES Finance: check status, view/change strategy (FIFO, LIFO, AVG, MAX_GAINS, MAX_LOSSES, FIFO_IMPAIRMENT), trigger recalculation, review financial issues, manage reevaluations/impairments, set bulk fiat prices, configure spec-ID lot disposal rules, view per-asset cost basis results, and export cost basis reports. Trigger this skill whenever the user asks about cost basis, gains/losses, realized gains, unrealized gains, COGS, inventory lots, tax lots, cost basis strategy, reevaluation, impairment, fiat value override, spec-ID rules, financial issues (negative balance, missing fiat), or anything related to how their crypto assets are costed. Also trigger when the user says "calculate cost basis", "what is my cost basis strategy", "show me financial issues", "create a reevaluation", "run cost basis", "change strategy to LIFO", or similar.
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Knowledge base from the NASA Cost Estimating Handbook (CEH) v4.0. Use for NASA's 12-step, three-part cost estimating process (Project Definition, Cost Methodology, Cost Estimate); the WBS, CADRe, and ONCE data backbone; estimating methodologies (analogy, parametric/CER, engineering build-up, EVM, Delphi); ground rules and assumptions, data normalization and inflation (BY/CY/RY); the point-estimate-to-S-curve cost-risk chain and Unallocated Future Expense (UFE) reserves; Joint Cost and Schedule Confidence Level (JCL) analysis via probabilistic cost-loaded schedules; decision-support analyses (sensitivity, trade studies, make/lease-vs-buy, affordability, CAIV); and economic analysis (discounting, NPV, OMB Circular A-94). The cost-estimating complement to NASA's SE and risk packs. Does NOT reproduce the handbook's tool-specific procedures, worked examples, or appendix-depth methods (it names the appendices), nor cover full DoD/GAO cost guides, schedule construction, or formal risk management beyond the cost interface.
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Summarize Claude Code token usage and estimated spending from the local ECC metrics log. Review costs by model, session, or date, compare spending with budgets, and export records to CSV while avoiding duplicate counting of session totals.
Export recorded AI usage costs and budgets as Prometheus metrics or JSON. Feed external dashboards with totals, model tier breakdowns, session costs, and budget utilization, or send the same data to a webhook endpoint.
Run four cost checks in parallel to assess budget limits, accelerating daily spending, unusual sessions, and projected budget exhaustion. Combine their results into one health status and exit code for automated CI workflows.
Retrieve a single snapshot of recorded AI costs, including total spending, model breakdowns, the highest-cost session, and budget usage. Export JSON for scripts and dashboards, or Markdown for a quick review.
Analyze AWS spending by service, region, and resource tags to find unused resources and oversized configurations. Get specific cost reduction recommendations, estimated savings, and guidance for budgets and ongoing cost monitoring.
Review a bill of materials for cost, risk, and supply exposure — cost rollup, top-10 cost drivers, single-source and EOL risk, MOQ vs forecast mismatch, cost-down candidates, and tariff/logistics sensitivity. Use when asked to review a BOM, find cost-down opportunities, check component sourcing risk, or sanity-check BOM cost against target. Produces a structured BOM review with a cost driver Pareto, risk flags per line, and a prioritised cost-down list.
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Tracks cumulative LLM costs across DAG execution and makes real-time decisions to stay within budget. Downgrades models, skips optional nodes, or stops early when cost exceeds thresholds. Use when managing execution budgets, analyzing cost breakdowns, or optimizing model routing for cost. Activate on "cost budget", "too expensive", "reduce cost", "cost optimization", "model downgrade", "budget exceeded". NOT for LLM model selection logic (use llm-router), pricing comparisons across providers, or billing/invoicing.
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Tracks cumulative LLM costs across DAG execution and makes real-time decisions to stay within budget. Downgrades models, skips optional nodes, or stops early when cost exceeds thresholds. Use when managing execution budgets, analyzing cost breakdowns, or optimizing model routing for cost. Activate on "cost budget", "too expensive", "reduce cost", "cost optimization", "model downgrade", "budget exceeded". NOT for LLM model selection logic (use llm-router), pricing comparisons across providers, or billing/invoicing.
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Estimate AWS costs before deployment, analyze spending trends, and investigate metrics and logs with CloudWatch. The skill guides cost reviews, alert setup, and operational improvements through connected tools, with additional integrations for audits and security assessments.
Monitor daily AWS spending by service and account, compare it with historical baselines, and send Slack alerts with cost differences and likely causes. Known planned increases are excluded, and cloud resources remain unchanged.
Analyze actual Azure spend, bill changes, and AKS or AI costs. WHEN: "Azure cost breakdown", "most expensive resources", "why did my bill increase", "cost spike", "unexpected charge", "Cosmos DB charges", "Foundry model costs", "AKS cluster cost", "namespace cost", "enable AKS cost analysis", "idle AKS capacity". DO NOT USE FOR: forecasts, pricing estimates, rightsizing, commitments, or budgets.
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Analyze AWS costs, find savings, manage budgets, evaluate Savings Plans and Reserved Instances, right-size EC2/Lambda/RDS/EBS with Compute Optimizer, look up service pricing, query CUR with Athena, detect cost anomalies, scope costs to billing views, and monitor Free Tier usage. Triggers on: AWS bill, cost analysis, reduce spend, savings plan, reserved instance, right-size, budget alert, cost optimization, pricing, free tier, cost anomaly, CUR, cost audit, billing view, billing view ARN.
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Analyze actual Azure spend, bill changes, and AKS or AI costs. WHEN: "Azure cost breakdown", "most expensive resources", "why did my bill increase", "cost spike", "unexpected charge", "Cosmos DB charges", "Foundry model costs", "AKS cluster cost", "namespace cost", "enable AKS cost analysis", "idle AKS capacity". DO NOT USE FOR: forecasts, pricing estimates, rightsizing, commitments, or budgets.
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Implement cloud cost optimization strategies for Kubernetes workloads using tools like Kubecost for visibility, right-sizing recommendations, horizontal and vertical pod autoscaling, spot/preemptible instances, and resource quotas. Covers cost allocation, showback reporting, and continuous optimization practices. Use when cloud costs are growing without proportional business value, when resource requests are misaligned with actual usage, when manual scaling leads to over-provisioning, or when implementing showback and chargeback for internal cost accountability.
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Answer natural language questions and perform analysis on GKE cluster and workload costs using BigQuery billing exports, cost allocation data, and live cluster monitoring metrics. Use when querying GKE costs across projects, namespaces, or workloads, analyzing billing reports in BigQuery (`bq`), checking cluster cost budgets (`gcloud billing`), or diagnosing cost drivers like pod requests vs. actual utilization (`kubectl top`). Don't use for applying cost optimization changes, creating rightsizing manifests (VPA/MPA), or selecting ComputeClasses (use gke-cost-optimization instead).
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