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[omh] Spending money on the user's behalf -- a card checkout, an HTTP 402 or pay-per-request call, a wallet transfer, a booking, a paid data endpoint: show a pre-spend card (payee, total with fees, cap, refund terms, approver) first, and call it done only on an observed receipt. Use when the user says: spend-gate, spend gate, pre-spend, pre-spend card, spend cap, spending cap, spend limit, spending limit.
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Review SaaS and purchasing spend to find growing cost categories, slow approval processes, and overlapping suppliers. Produce categorized reports and consolidation options that account for migration costs, clustered renewals, and the risks of relying on one critical supplier.
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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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从Claude Code的本地使用日志汇总令牌用量和美元预估费用，按模型、会话或日期查看支出，比较预算并导出CSV，避免重复计算会话累计值。
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.
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Plan a paid evaluation with predefined success criteria, comparison groups, decoy items, and stopping rules. Rehearse without spending, then run a three-item paid pilot and use its spending table to inform approval.
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Find LLM workflows in a codebase and label them by the job they perform, so Caveman Cloud can separate spending by workflow. Present a labeling proposal for approval, apply focused code changes, and check an affected execution path.
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Identify individual sessions with unusually high or low spending using the median and MAD, a measure of spread around it. Filter by time period, adjust detection thresholds, and optionally return a failure exit code for automated checks.
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Summarize spending events for each connected peer over the past hour, day, and week, and flag peers above a configured suspension threshold. This consumer-side script depends on upstream event publishing that the source describes as not yet implemented.
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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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Plan advertising budget allocation, bidding, and spending pace using goals, financial constraints, and evidence about additional spending returns. Compare options and produce a plan with amounts, timing, owners, success measures, and triggers for reversing changes.
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Forecast Claude Code spend to the end of the week or month from the daily session trend on the Agent Monitor dashboard — moving average of daily spend × days remaining, added to spend-to-date. Uses /api/analytics daily_sessions, /api/pricing/cost, and /api/sessions for a per-day cost curve. Use when projecting cost or asking "where will my spend land".
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Guide Ramp setup and spending workflows in Paperclip using current official instructions. Account applications, card creation, payments, and financial data sharing require explicit approvals that define the permitted action, amount, and data.
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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.
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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.
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Review estimated spending, token usage, and cache hit ratios across Claude Code, Codex, and Grok using local logs. Filter by time, project, or provider, while keeping currencies separate and identifying models without pricing.
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Read-only Ramp corporate spend: transactions, cards and card limits, users, departments. No spend actions by design. Trigger phrases: ramp, corporate spend, expense transactions, card spend limits.
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A discipline for running paid media that does not light money on fire. Hypothesis writing for paid spend, channel selection, budget allocation, audience targeting, bid strategy, campaign types, what NOT to spend on, attribution reality, and the failure modes that produce expensive lessons. Triggers on paid media strategy, ad budget allocation, channel selection, paid media plan, audit my Google Ads, audit my Meta Ads, scale paid media, kill underperforming campaign, paid media hypothesis, ad spend strategy, attribution reality, performance marketing strategy. Also triggers when a team is asking how to scale paid media, or whether to add a new channel, or how to reallocate spend across channels.
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Marketing budget planning and forecasting — channel allocation, CAC targets, ROI projections, and spend optimization. Use when "marketing budget", "budget planning", "channel allocation", "marketing spend", "CAC forecast", "budget forecast", "how much should I spend", "allocate budget", or any request to plan, forecast, or optimize marketing spend.
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Full-funnel paid advertising operations for B2B SaaS. Use this skill for PPC strategy, Google Ads optimization, LinkedIn Ads, social media advertising, programmatic buying, creative strategy, creative concept diversity for Meta Advantage+ (why six variants of one ad are not a diverse library, and what to brief instead), creator-made ad creative on a spend-share deal, attribution modeling, budget allocation, budget pacing and planned-vs-delivered reconciliation, audience overlap and campaign self-competition audits, suppression lists, ROAS improvement, media spend optimization, and the media no auction sells — newsletter and podcast sponsorships, community and industry-publication placements, paid review-site listings on G2 and Capterra, and pay-per-lead content syndication. It also covers buying on the AI answer surface — ads inside Google AI Overviews and AI Mode that existing Search, Shopping and Performance Max campaigns already serve into with no opt-out, no placement targeting and no segmented reporting, and ChatGPT Ads and Microsoft Copilot as separate opt-in platforms with their own plan-tier reach limits, no negative keywords, and a landing-page crawler gate. It also owns the conversion signal the bidding algorithms learn from — CRM pipeline stages (SQL, opportunity, closed-won) sent back to Google Ads, LinkedIn, Meta and Microsoft Ads through offline conversion import, enhanced conversions for leads, the Google Data Manager API and the LinkedIn Conversions API, with click-ID capture, stage values, dedupe, EEA consent signals and retractions. It also gates connected TV (CTV) and streaming video for B2B — whether a reach medium belongs in the plan, how a vendor ties a TV household to a target account (member data, IP-to-account graph or contextual), the server-side ad insertion (SSAI) fraud questions, Programmatic Guaranteed deals, and a holdout lift test because CTV has no click. It also runs person-led paid social — LinkedIn Thought Leader Ads and Meta partnership ads that put spend behind an employee's or creator's post — on a paid-usage term, an in-post disclosure check and an end-date register, because the platform approval carries no term. Also triggers on: CTV, connected TV, OTT, streaming TV ads, should we run CTV, LinkedIn CTV, B2B CTV, account-based CTV, SSAI fraud, thought leader ads, boost an employee's post, sponsor a creator's post, partnership ads, whitelisting, creator usage rights, PPC, Google Ads, LinkedIn Ads, social ads, programmatic, ad creative, attribution, media budget, paid campaigns, ROAS, CPA, ad spend, newsletter sponsorship, podcast sponsorship, community sponsorship, sponsor a newsletter, content syndication, pay-per-lead, cost per lead vendor, media kit, rate card, insertion order, direct buy, publisher partnership, make-good, G2 paid listing, Capterra ads, review site advertising, sponsorship ROI, budget pacing, we underspent our budget, are our campaigns competing with each other, audience overlap, self-competition, campaign cannibalization, suppression list, exclusion list, ad frequency across channels, how many ad accounts do we have, ads in AI Overviews, AI Mode ads, ChatGPT Ads, OpenAI Ads Manager, Copilot ads, conversational ads, should we advertise on ChatGPT, can we buy our way into the AI answer, our ads are showing in AI Overviews and we cannot report on it, context hints, OAI-AdsBot, our ad landing page fails review, robots.txt is blocking our ads, our paid leads are junk, Google Ads is optimizing for the wrong leads, offline conversion import, OCI, enhanced conversions for leads, send CRM stages back to Google Ads, upload SQLs to LinkedIn, LinkedIn Conversions API, LinkedIn CAPI, Meta conversion leads, CRM to ad platform, gclid, li_fat_id, msclkid, value-based bidding, optimize for pipeline not form fills, low match rate, conversion lag.
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組織を中断し、broker daemon まで含めて完全に停止する。 「完全に止める」「daemon ごと落とす」「org down して」「全部終了」と言われたときに使う。 suspend（状態保存 + ja 管理プロセス / ペイン停止）を完走してから `claude-org-runtime org down` を呼ぶ統合終了。単に「中断」「今日は終わり」なら daemon を残す /org-suspend を使う。
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为拥有钱包或交易执行权限的AI智能体设计并检查安全措施，涵盖外部输入防护、支出限额、发送前验证、停止条件、钱包隔离和私钥管理。
提供LLM API费用管理的设计示例，将按任务复杂度选择模型、记录预算、重试临时错误和缓存共用提示词结合起来，用于检查累计费用与调整调用方式。
通过x402为AI智能体接入付费API和服务支付，设置任务与会话预算、收款方限制，以及由自己管理私钥的钱包。