「spend」の検索結果

Optimizing FinOps and cloud cost: IaC-based estimation, right-sizing, RI/SP recommendations, anomaly detection, budget alerts, AI/GPU workload economics. Use to forecast or cut cloud spend.

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AI x Web3 Architect: Autonomous agents that hold wallets: smart accounts, spend limits, intents, agent payments.

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When the user wants help with paid advertising campaigns on Google Ads, Meta (Facebook/Instagram), LinkedIn, Twitter/X, or other ad platforms. Also use when the user mentions 'PPC,' 'paid media,' 'ROAS,' 'CPA,' 'ad campaign,' 'retargeting,' 'audience targeting,' 'Google Ads,' 'Facebook ads,' 'LinkedIn ads,' 'ad budget,' 'cost per click,' 'ad spend,' or 'should I run ads.' Use this for campaign strategy, audience targeting, bidding, and optimization. For bulk ad creative generation and iteration, see ad-creative. For landing page optimization, see cro.

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Nexscope Ozon product report search: filters Ozon products by multi-dimensional metrics including sales, revenue, sales/revenue growth rate, cart conversion rate, order conversion rate, price, rating, review count, QA count, variant count, page views, gross margin, return/cancellation rate, ad spend share, weight/volume, listing time, brand, seller, fulfillment method, and labels. Returns each product's SKU, title, price (RUB), sales, revenue, lost revenue, conversion rate, rating, reviews, brand, seller, fulfillment method, listing days/months, and complete product report fields. Use for Ozon product selection, competitor product analysis, best-seller mining, price/conversion band filtering. Trigger when the user mentions Ozon product report, Ozon product selection, Ozon product filtering, Ozon product analysis, Ozon best-selling products, Ozon competitor product analysis, Ozon product report, Ozon product screener, filter Ozon products by sales, Ozon best-seller mining, Nexscope Ozon product report. Also trigger when the intent is to filter Ozon products by multiple metrics and view product-level reports, even without explicitly mentioning Nexscope.

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Analyze Trigger.dev tasks, schedules, and runs for cost optimization opportunities. Use when asked to reduce spend, optimize costs, audit usage, right-size machines, or review task efficiency. Combines static source analysis with live run analysis via the Trigger.dev MCP tools (list_runs, get_run_details, get_current_worker).

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Manage vendor relationships — vendor selection scorecard, contract review checklist, renewal tracking, and vendor consolidation audit. Use when asked to "evaluate this vendor", "review this contract", "track vendor renewals", or "reduce our SaaS spend".

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Audit cloud infrastructure costs and produce a concrete optimization plan with specific changes and estimated savings. Use when asked to "how much is this costing", "reduce cloud spend", "cost optimization", "are we overpaying", "cloud bill", or "budget for this infra".

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Audit cloud spend — identify waste, rightsizing opportunities, and reservation gaps. Use when asked "our cloud bill is too high", "find cloud waste", or "where can we rightsize".

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Design cost reduction strategies — model tiering, prompt compression, caching, batch inference. Use when asked to "reduce our AI costs", "set up model tiering", or "cut LLM spend".

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Provides FinOps practices for cloud cost optimization, budget management, and resource rightsizing. Use when analyzing cloud spend, optimizing costs, or when user mentions 'finops', 'cloud cost', 'rightsizing', 'budget', 'cost allocation', 'reserved instance', 'spot instance', 'showback', 'chargeback'.

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Analysis of aggregate economic phenomena -- GDP measurement, inflation dynamics, unemployment theory, monetary policy (central banks, interest rates, money supply), fiscal policy (government spending, taxation, debt), and the business cycle. Covers Keynesian, monetarist, and new classical perspectives. Use when analyzing national or global economic conditions, policy debates, economic growth, recessions, or the interaction between monetary and fiscal authorities.

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The household as an economic unit with income, expenses, savings, and debt. Covers the envelope method, fixed vs variable expenses, the 50-30-20 baseline, emergency reserves, the true cost of ownership, opportunity cost in household decisions, and the distinction between a budget and a spending plan. Use when building a household budget, diagnosing why a household is always over, planning a major purchase, or teaching financial literacy at the household level.

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账号被限流/异常/担心操作触发风控/需要恢复时用：先自检from:@用户名搜不到即限流→区分临时标签(等24h不动账号)与正式标签(写申诉邮件+48h不操作)→复盘触发原因预防复发。Trigger："推文搜不到了""被限流了""账号异常""触发风控""临时标签""怎么申诉""shadowbanned""account suspended""appeal suspension""tweets not showing up"。不适用：正常发内容没出问题(→内容创作skill)、新号开业装修养号(→x-account-setup)、没粉丝怎么起量(→x-cold-start-playbook)。

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。

Power management and performance optimization for Zephyr RTOS. Covers system power states (Idle, Suspend, Off), device-level power management, residency hooks, and code/data relocation for speed efficiency. Trigger when optimizing battery life, reducing latency, or managing memory constraints.

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Set up Hierarchical Agent Memory (HAM) — scoped CLAUDE.md files per directory that reduce token spend. Trigger on "go ham", "set up HAM", "ham commands", "ham help", "ham route", "ham remove", "ham update", "ham status", "ham benchmark", "ham baseline start", "ham baseline stop", "ham metrics clear", "HAM savings", "HAM stats", "HAM dashboard", "HAM sandwich", "HAM insights", "HAM carbon", or "ham sync".

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Intelligent financial management skill for Claude Code that provides comprehensive PocketSmith API integration with AI-powered analysis, transaction categorization, rule management, tax intelligence, and scenario planning. Use when working with PocketSmith data for (1) Transaction categorization and rule management, (2) Financial analysis and reporting, (3) Australian tax compliance (ATO) and deduction tracking, (4) Scenario analysis and forecasting, (5) PocketSmith setup health checks, (6) Budget optimization and spending insights.

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Real-time cost tracking, budget enforcement, and ROI measurement for AI agent operations. Track token usage, predict costs, enforce budget caps ($50-70/month typical), optimize model selection, cache results, measure cost-to-value. Use when tracking AI costs, preventing budget overruns, optimizing spend, measuring ROI, or ensuring cost-effective AI operations.

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。

Daily ad performance check-in across platforms. Pulls live metrics from Meta, Google, and LinkedIn via deterministic scripts, compares against benchmarks and previous period, flags overspend/underperformers/policy issues, and outputs a quick daily summary with action items. Use when "daily ad review", "how are my ads doing today", "ad check-in", "morning ad report", "daily ad summary", "check ad performance", "ad dashboard", "daily ads", or any request for a recurring or quick-glance ad performance review.

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Use before committing, opening a PR, or reporting a task done — when you want ground-truth confirmation there are no conflict markers, live-looking credentials, debug leftovers, untracked TODOs, or oversized files, without spending a model turn re-reading every file yourself.

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Hunt business logic flaws — race conditions, workflow state bypass, parameter pollution affecting logic, coupon/discount abuse, refund logic, multi-step process bypass, integer overflow, negative quantities, double-spend. Use when looking for non-CVE bugs that require understanding the app's intended workflow.

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Query OpenRouter API costs, credits, and usage breakdown by model, provider, and date for the last 30 days. Triggers on API spending, check costs, openrouter usage, how much have I spent.

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Report ground-truth Claude token consumption and estimated cost by parsing JSONL session files directly. Use when checking API spend, auditing token usage by project/session/model, generating daily/weekly/monthly cost reports, or diagnosing ccusage undercounting. Includes subagent files that ccusage ignores.

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Monitor, cap, and recover from context accumulation in agentic systems. Covers per-cycle cost tracking, context window auditing, budget caps with enforcement policies, emergency pruning when approaching limits, and progressive disclosure integration to minimize token spend on routing. Use when running long-lived agent loops (heartbeats, polling, autonomous workflows), when context windows are growing unpredictably between cycles, when API costs spike beyond expected baselines, when designing new agentic workflows that need cost guardrails from the start, or when post-mortem analysis reveals a cost incident caused by context accumulation.

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Forecast infrastructure and application metrics using Prophet or statsmodels for capacity planning, cost optimization, and proactive scaling. Visualize predictions in Grafana and set up alerts for projected resource exhaustion. Use when forecasting infrastructure capacity needs for CPU, memory, or disk, planning hardware procurement for next quarter, predicting cost trends to optimize cloud spending, or setting up proactive scaling policies based on predicted load.

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。