“budgets”的搜索结果

DevOps 与站点可靠性工程 (SRE) — 平台 / 基础设施 / 可靠性工程师的认知操作系统, 覆盖软件交付 + 运维全生命周期 (CI/CD 与发布工程 trunk-based + 渐进式发布 canary/blue-green/feature flag + GitOps Argo CD/Flux / 基础设施即代码 Terraform/OpenTofu/Pulumi/Ansible + policy-as-code OPA / 容器与编排 Docker/Kubernetes + Helm/Kustomize + service mesh Istio/Linkerd / 可观测性 Prometheus + Loki + OpenTelemetry + Honeycomb + eBPF + RED/USE / SLO-SLI-error budget 与可靠性工程 Google SRE 学科 + 容量规划 + 优雅降级 / 事件管理与 on-call 事件指挥 + PagerDuty + runbook + 无指责复盘 + MTTR / 云平台与 FinOps AWS/GCP/Azure + 成本优化 + 弹性伸缩 / 平台工程与开发者体验 IDP + Backstage + golden path + Team Topologies / DevSecOps 与供应链安全 shift-left + SBOM + SLSA + sigstore + Vault / 韧性与混沌工程 fault injection + game day + 安全科学 / DORA 指标与工程效能 部署频率 + 变更前置时间 + 变更失败率 + Accelerate 研究 / 数据库与有状态运维 schema 迁移 + 备份容灾) — 不含 通用应用开发 / 纯云销售认证速成 / 'DevOps = 跑 Jenkins 的岗位' 窄化误解 / ITIL 工单文化传统运维 (旧范式仅做边界) / 把手工运维 ClickOps 当稳态 (是 toil, 本 skill 核心反模式) (DevOps & Site Reliability Engineering — the cognitive operating system of platform / infrastructure / reliability practitioners who own the full software delivery + operational lifecycle, covering (a) CI/CD & release engineering (build pipelines, trunk-based development, progressive delivery — canary / blue-green / feature flags, GitOps with Argo CD / Flux), (b) Infrastructure as Code (Terraform / OpenTofu, Pulumi, CloudFormation, Ansible, Crossplane — module design, state management, drift, policy-as-code OPA / Sentinel / Checkov), (c) containers & orchestration (Docker / OCI, Kubernetes — scheduling, networking CNI, storage CSI, operators / CRDs, Helm / Kustomize, service mesh Istio / Linkerd), (d) observability (the three pillars + beyond — metrics Prometheus / VictoriaMetrics, logs Loki / ELK, traces OpenTelemetry / Jaeger / Tempo, high-cardinality observability Honeycomb, eBPF, RED / USE methods, SLO-based alerting), (e) SLO / SLI / error budgets & reliability engineering (Google SRE discipline — service level objectives, error budget policy, toil budgets, capacity planning, load shedding, graceful degradation), (f) incident management & on-call (incident command, PagerDuty / Opsgenie, runbooks, blameless postmortems, MTTR / MTTD, error budget burn), (g) cloud platforms & FinOps (AWS / GCP / Azure well-architected, multi-region, cost optimization, autoscaling), (h) platform engineering & developer experience (internal developer platforms, Backstage, golden paths, self-service, Team Topologies), (i) DevSecOps & supply-chain security (shift-left, SAST / DAST, SBOM, SLSA, sigstore / cosign, secrets management Vault), (j) resilience & chaos engineering (chaos experiments, fault injection, game days, resilience engineering / safety science), (k) DORA metrics & engineering effectiveness (deployment frequency, lead time, change failure rate, MTTR, the Accelerate research), (l) databases & stateful operations (schema migrations, backups / DR, replication); NOT generic software development / app feature coding (是 平行学科, DevOps/SRE 关注 delivery + operability 不是 product feature), NOT pure cloud sales / certification cram without operational depth, NOT 'DevOps = a job title that runs Jenkins' 的窄化误解 (DevOps 是 文化 + 实践, SRE 是 Google 对 reliability 的工程化具体实现), NOT ITIL-heavy 传统运维 工单文化 (是 被 DevOps 取代的旧范式, 仅做边界标注), NOT manual ops / ClickOps as a steady state (是 toil, 本 skill 的核心反模式).) Master OS — automated mastery of DevOps & Site Reliability Engineering — the cognitive operating system of platform / infrastructure / reliability practitioners who own the full software delivery + operational lifecycle, covering (a) CI/CD & release engineering (build pipelines, trunk-based development, progressive delivery — canary / blue-green / feature flags, GitOps with Argo CD / Flux), (b) Infrastructure as Code (Terraform / OpenTofu, Pulumi, CloudFormation, Ansible, Crossplane — module design, state management, drift, policy-as-code OPA / Sentinel / Checkov), (c) containers & orchestration (Docker / OCI, Kubernetes — scheduling, networking CNI, storage CSI, operators / CRDs, He

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Wire the Prisma Next runtime — `db.ts` setup using `postgres<Contract>(...)` from `@prisma-next/postgres/runtime`, `sqlite<Contract>(...)` from `@prisma-next/sqlite/runtime`, or `mongo<Contract>(...)` from `@prisma-next/mongo/runtime`; middleware composition (telemetry from `@prisma-next/middleware-telemetry`; lints and budgets), `DATABASE_URL` config, per-environment branching, switching between Postgres, SQLite, and Mongo façades. Use for db.ts, postgres(), sqlite(), mongo(), middleware, telemetry, lints, budgets, DATABASE_URL, .env, connection pool, poolOptions, dev vs prod config, transactions, db.transaction, read replicas, multi-database, script won't exit, hangs, close connection, db.end, db.close, pool.end, [Symbol.asyncDispose], await using.

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Track and report Claude Code token usage, spending, and budgets from a local cost-tracking database. Use when the user asks about costs, spending, usage, tokens, budgets, or cost breakdowns by project, tool, session, or date.

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Plan and track budgets using your AI agent. Works for personal finance, project budgets, or department spending.

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Capture and compare Clawix UI performance budgets for critical flows before optimizing visible or runtime UI behavior.

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GPU budgets: production-level draw calls, overdraw, LOD, DPR budget. Use when implementing, reviewing or testing gpu budgets for React, Vite, Next.js or Perfect_AI projects.

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Defines measurable performance budgets for bundle size, API latency, database queries, and render times. Provides enforcement strategies and monitoring plans. Use for "performance budgets", "performance monitoring", "web vitals", or "optimization targets".

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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。

• 检查LLM应用的API费用
• 按批处理难度选择模型
• 对照预算检查累计费用

将年度营销计划、新产品发布等内容整理成杂志风格的HTML幻灯片，构建叙事、正文和视觉呈现，说明营销活动与增长及商机创造的联系。

• 制作年度营销计划审批材料
• 说明新产品上市计划
• 将活动预算与增长目标关联

Manage payment permissions for AI agents with spending caps, expiration dates, and merchant rules. Use cryptographic signatures to verify authorization, coordinate approval from multiple agents for high-value purchases, and track or revoke payment permissions.

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• Set shopping budgets and permitted merchants for AI agents
• Require multiple agents to approve high-value purchases
• Manage subscription renewals with spending caps

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.

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• Refreshing cost metrics for Grafana or Prometheus
• Sending a one-time cost report to a webhook
• Providing budget utilization data for monitoring

Create, edit, and inspect spreadsheets for reusable budgets, models, and trackers. Check formulas and dependent totals, preserve unrelated data, and deliver an editable workbook or a link to the updated native spreadsheet.

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• Build a monthly headcount budget
• Investigate an incorrect quarterly total
• Fix formulas and highlight changed cells

Plan and review paid advertising across Google Ads, Meta, LinkedIn, and other platforms. Organize audiences, budgets, and bidding, then assess results using customer acquisition costs and sales data while separating evidence, hypotheses, and missing information.

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• Choose advertising platforms and budget allocations
• Review campaign results and decide next steps
• Find wasted spend in search advertising

Manage personal finances in Firefly III through commands. Record expenses, maintain accounts and budgets, configure recurring transactions and categorization rules, and retrieve monthly income, expense, and budget summaries for review or export.

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• Record expenses and check account balances
• Set monthly budget limits
• Configure recurring payments and categorization rules

Check physical units, propagate measurement uncertainty through calculations, and format results with meaningful precision. Local tools also inspect Python code for unit and uncertainty mistakes and assess whether calculated magnitudes are physically plausible.

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• Convert units while carrying measurement uncertainty
• Build an uncertainty budget from calibration data
• Audit Python calculations for unit and uncertainty errors

Plan and review fluid simulations with FluidSim, from solver selection and resource estimates to output analysis and restart checks. Examine numerical accuracy through grid and time-step comparisons, conservation checks, and explicit limits on computing resources.

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• Selecting a solver and reviewing simulation parameters
• Estimating memory, disk space, and runtime needs
• Inspecting spectra and conservation budgets

Test how agent workflows recover from repeatable simulated failures, including tool timeouts, worker restarts, and lost memory. Produces a fault matrix, event timeline, and verdict showing whether state, safety boundaries, and retry budgets were preserved.

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• Test recovery from MCP or tool timeouts
• Verify checkpoint recovery after a worker restart
• Check outcomes when one parallel branch fails

Evaluate whether a fine-tuned model is ready for release by checking training data, general capability loss, comparisons with the base model, and staged production rollout. Produces an evidence-based promotion or rejection report with one prioritized remedy when rejected.

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• Deciding whether to release a fine-tuned model
• Checking whether training reduced general capabilities
• Reassessing a model against updated reference evaluations

Review portfolio company financial reports in Excel, PDF, or CSV, compare key measures with budgets and prior periods, and summarize performance trends, warning signs, and compliance with financial conditions in loan agreements.

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• Reviewing monthly performance against budget
• Preparing financial summaries for board meetings
• Checking financial conditions in loan agreements

Add Adaptive Card templates, structured visual layouts for API responses, to MCP-based plugins in Microsoft 365 Copilot. Configure shared or item-specific layouts, bind response fields, and prepare responsive cards with validation scenarios.

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• Displaying project status and budgets as cards
• Using different layouts for debit and credit entries
• Providing a default card when no template is selected

Design an AI agent around its requirements, or audit an existing agent for architectural problems. Deliver justified decisions, verification plans, and a PDF report with a diagram, covering budgets, permissions, stopping rules, and failure handling.

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• Design an agent from an idea or specification
• Audit an existing agent’s architecture
• Diagnose errors and false completion reports

Analyze supplied ad performance data to identify wasted spend, strong campaigns, and next tests. Compare channels, assess whether test differences are meaningful, and produce budget reallocation recommendations with a prioritized action plan.

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• Identify ads and keywords to pause
• Find campaigns and audiences worth scaling
• Assess whether A/B test results are meaningful

Helps plan and improve marketing campaigns across social media, email, and landing pages. Guides audience selection, messaging, content creation, schedules, budgets, and performance measurement so teams can organize execution and identify improvements from results.

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• Planning a campaign for a product launch
• Coordinating messaging across social media, email, and landing pages
• Setting campaign goals and performance metrics

Plan game development by choosing specialized skills for the target platform, 2D or 3D graphics, and gameplay needs. Get guidance on shared foundations such as game loops, input handling, design patterns, and performance budgets.

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• Planning a browser-based 2D platformer
• Designing a mobile puzzle game
• Organizing development for a multiplayer VR game