Results for “budget-optimization”

Plan, launch, and optimize paid ad campaigns across Meta, Google, and LinkedIn with audience targeting, budget allocation, and creative testing.

Design and implement SLIs, SLOs, and error budgets for production services. Covers SLI selection, SLO target-setting, error budget calculation, burn-rate alerting, and error budget policy. Use when asked about "SLO", "SLI", "error budget", "reliability targets", "burn rate", "uptime SLA", or when designing alerting for a production service. Do NOT use for: application-level performance optimization — use `web-performance` for frontend.

Systematic analysis of Google Ads and Meta advertising spend to identify wasted budget. Produces actionable exclusion lists with thematic categorization and statistical validation. Use when onboarding new accounts, performing monthly hygiene, or preparing for budget optimization.

Optimize Kubernetes cluster costs by right-sizing resources, configuring autoscalers, using spot instances, and monitoring spend.

Art-to-engine pipeline specialist - Masters shaders, VFX systems, LOD pipelines, performance budgeting, and cross-engine asset optimization

Use when evaluating costs or looking for ways to reduce expenses in your domain. This skill provides a framework for identifying, analyzing, and reducing costs across all operations, infrastructure, development, and business activities.

Optimizes code for performance - speed, memory, bundle size

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.

Reallocate ad budget across campaigns and channels using ROAS and marginal return analysis

Optimize GKE costs by rightsizing workloads, configuring Spot VMs, selecting machine types, and using Committed Use Discounts.

`analysis-agent`/`task-agent`/`review-agent`: use when latency, throughput, bundle, memory, CPU, query, rendering, or resource cost needs a budget; skip without performance risk.

Plan and review paid-media budgets, bidding, pacing, marginal return, forecasts, CPA, ROAS, MER, LTV:CAC, constraints, and allocation across supported platforms.

Optimize context usage for the session by reducing bloat, improving token efficiency, and focusing on relevant areas. Use when context is growing large, responses are slow, or switching between unrelated tasks. Covers context analysis, compacting, targeted pruning, MCP-first strategies, and focused scoping by area.

Context window monitoring and budget management. Keeps orchestrator at 15-30% context usage while subagents get full 200k tokens. Provides warnings at thresholds, context-aware summarization triggers, and wave-level budget planning.

Create and manage budgets with variance analysis and departmental allocation. Use when the user requests budget planning or provides relevant inputs for this workflow.

The instruction to profile before optimizing survives because intuition about

Audits Claude Code context window consumption across agents, skills, MCP servers, and rules. Identifies bloat, redundant components, and produces prioritized token-savings recommendations.

Design pricing, packaging, and monetization strategy for a software or AI product. Use when the team needs plan structure, free-tier logic, usage vs seat pricing tradeoffs, paywall timing, or monetization guardrails tied to product value and costs.

Compress bloated project or global memory while preserving decisions, rationale, revisit triggers, provenance, and active user preferences. Load when memory exceeds budget, global memory is too large, session logs are repetitive, or before appending to an over-budget memory file.

Optimize a complete candidate context package by deduplicating, filtering, ordering, and allocating its token budget. Use when retrieved or assembled material is noisy or exceeds the useful context budget; use context-ranking for scoring chunks and context-compression for shrinking selected content.

Reduces AI API costs by 30-75% by classifying task complexity, checking prompt quality, and routing tasks to the most cost-efficient model tier before execution.

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.

当感到生活混乱、压力大、注意力分散，希望系统性地优化心智资源分配以提升创造力和生活体验时

Optimize machine learning model hyperparameters using grid search, random search, Bayesian optimization, and Hyperband to maximize model performance within a compute budget. Use when the user requests hyperparameter tuning or provides relevant inputs for this workflow.

Paid acquisition strategy, budget allocation, and avoiding common advertising mistakes across platforms

Almost every cloud estate is over-provisioned.

Transform the agent into a performance engineer. Apply methodologies for measuring, profiling, and optimizing code (caching, algorithm complexity, resource usage).

Use this when the team needs to improve latency, throughput, render performance, query speed, worker throughput, or resource usage across frontend, backend, database, or background systems.

Orchestrate token-heavy research, coding, and testing by delegating bounded tasks to cheaper subagents while reserving Claude Fable for architecture, synthesis, and final review.

Brainstorm 3-5 monetization strategies with audience fit, risks, and validation experiments for a product or feature.