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Grok Bot+18 agentsLLM Cost Optimization Analyst
Identify and quantify LLM cost savings through log analysis, model routing, and prompt optimization strategies.
$29.99
LLM Cost Optimization Analyst
Example session with this skill installed
Analyze our OpenAI usage: 40M gpt-4o input tokens, 10M output tokens last month. 30% are repetitive customer support FAQs. Latency target <2s. Current bill is ~$2500. Find savings.
- Read your context and instructions
- Compiled the llm cost optimization
I have analyzed the provided usage data for your customer support workload. The primary cost driver is the use of high-intelligence models for repetitive FAQ responses.
LLM Cost Optimization Analysis
Baseline Summary
| Metric | Value |
|---|---|
| Current Monthly Spend | $2,500.00 |
| Model | gpt-4o |
| Input/Output Ratio | 4:1 |
| Repeat Rate | 30% (High) |
| Estimated Cost/Task | $0.05 |
Prioritized Recommendations
| Priority | Strategy | Est. Monthly Savings | Risk Level |
|---|---|---|---|
| High | Semantic Caching (FAQ) | $450 - $675 | Low |
| High | Model Routing (gpt-4o-mini) | $800 - $1,200 | Medium |
| Medium | Prompt Compression | $150 - $250 | Low |
Detailed Opportunity: Model Routing
Shifting the 30% repetitive FAQ tasks and 20% simple classification tasks to gpt-4o-mini while retaining gpt-4o for complex reasoning.
- Formula:
(Volume * 0.5) * (Price_gpt4o - Price_mini) - Quality Guardrail: Run a side-by-side eval on a 500-request golden set. Target >98% agreement.
- Latency Impact: Expected reduction of 150ms on routed tasks.
Experiment & Rollout Plan
- Phase 1: Implement a cache for the top 50 most frequent FAQ responses. Monitor hit rate for 72 hours.
- Phase 2: Route 5% of traffic to
gpt-4o-miniwith a fallback togpt-4oif the confidence score is below 0.8. - Rollback Criteria: Trigger rollback if Task Success Rate drops >1.5% or P95 latency exceeds 2.0s.
Next steps
- Provide a sample of 10-20 repetitive FAQ prompts to test cache key generation.
- Confirm if your orchestration layer supports dynamic model routing.
- Upload evaluation results or a "golden set" to validate
gpt-4o-miniperformance.
Connects securely to your tools. The creator never sees your data.
About this skill
High LLM bills often stem from inefficient prompt structures, redundant calls, and poor model routing rather than just high traffic. This skill audits your usage telemetry to find specific, evidence-based savings without compromising task quality or latency.
What it does
- Cost Segmentation breaks down spending by model, task type, endpoint, and token usage to find the primary drivers.
- Opportunity Identification flags prompt bloat, duplicate requests, and inefficient retry logic.
- Savings Estimation provides low, base, and high-case formulas for every proposed change.
- Risk Assessment evaluates how changes might impact response quality, latency, and reliability.
- Experiment Design creates a structured rollout plan with holdout sets and rollback triggers.
How it works
- Context Definition where you provide workload types, quality thresholds, and budget constraints.
- Data Inspection of your usage logs, provider rates, and performance metrics.
- Baseline Establishment to calculate your current cost per successful task.
- Strategy Formulation across prompt trimming, caching, batching, and model routing.
- Action Plan delivery including a prioritized list of optimizations and monitoring metrics.
Frameworks & tools
Works with any provider data including OpenAI, Anthropic, Azure, or AWS Bedrock. Analyzes standard usage logs, billing CSVs, and telemetry from observability tools like LangSmith or Helicone.
Why this beats prompting it yourself
Generic prompts often suggest switching to cheaper models without considering the quality trade-offs or implementation costs. This skill uses a systematic financial analysis workflow that treats cost per successful task as the core metric, ensuring reliability isn't sacrificed for cents.
Use cases
- Audit monthly LLM invoices to identify sudden spend spikes or inefficiencies.
- Evaluate the ROI of implementing semantic caching or prompt compression.
- Design a model routing strategy to shift simple tasks to smaller, cheaper models.
- Optimize high-volume batch processing pipelines for maximum discount tiers.
Known limitations
Does not automate the implementation of code changes or API configuration. Relies entirely on the accuracy and completeness of user-provided logs and provider pricing data.
Cannot execute code changes. Requires manual entry or upload of usage logs and current provider pricing. Analysis is limited to the time range and data quality of provided logs.
How to use
- 1
Connect your agent to the Agensi MCP
Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup
- 2
Sign in to Agensi
Your agent shows a sign-in link. Sign in and click Allow.
- 3
Ask for what you need
Your agent loads this skill and uses it on your task. It always asks before any payment.
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