llm-cost-optimizer
Use proactively whenever LLM API costs come up -- or should. Triggers include: 'my AI costs are too high', 'optimize token usage', 'which model should I use', 'LLM spend is out of control', 'implement prompt caching', 'we're about to launch an AI feature', 'build me an AI endpoint'. Don't wait for an explicit cost complaint -- if someone is building an AI feature, designing an LLM endpoint, or choosing between models, cost architecture belongs in the conversation. Apply immediately when any of these are true: a system prompt appears that exceeds a few hundred tokens, all requests are hitting the same model, max_tokens is not set, or no per-feature cost logging exists. NOT for RAG pipeline design (use rag-architect). NOT for improving prompt quality or effectiveness (use senior-prompt-engineer).
Overview
Use proactively whenever LLM API costs come up -- or should. Triggers include: 'my AI costs are too high', 'optimize token usage', 'which model should I use', 'LLM spend is out of control', 'implement prompt caching', 'we're about to launch an AI feature', 'build me an AI endpoint'. Don't wait for an explicit cost complaint -- if someone is building an AI feature, designing an LLM endpoint, or choosing between models, cost architecture belongs in the conversation. Apply immediately when any of these are true: a system prompt appears that exceeds a few hundred tokens, all requests are hitting the same model, max_tokens is not set, or no per-feature cost logging exists. NOT for RAG pipeline design (use rag-architect). NOT for improving prompt quality or effectiveness (use senior-prompt-engineer).
Source: https://github.com/alirezarezvani/claude-skills · MIT · © 2025 Alireza Rezvani
System prompt
Preview redactedThe instruction block that defines the agent's role, rules and output format.
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████████████████████████████████████████████████████████████████User prompt template
Fill in the variables and send it to run the skill: {{variables}}
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| Name | Required | Description | Example |
|---|
Full skill configuration
Redacted previewThe standard .aiskill file in JSON and YAML.
{
"format": "aiskill/v1",
"skill": {
"name": "llm-cost-optimizer",
"slug": "llm-cost-optimizer",
"version": "1.0.0",
"description": "Use proactively whenever LLM API costs come up -- or should. Triggers include: 'my AI costs are too high', 'optimize token usage', 'which model should I use', 'LLM spend is out of control', 'implement prompt caching', 'we're about to launch an AI feature', 'build me an AI endpoint'. Don't wait for an explicit cost complaint -- if someone is building an AI feature, designing an LLM endpoint, or choosing between models, cost architecture belongs in the conversation. Apply immediately when any of these are true: a system prompt appears that exceeds a few hundred tokens, all requests are hitting the same model, max_tokens is not set, or no per-feature cost logging exists. NOT for RAG pipeline design (use rag-architect). NOT for improving prompt quality or effectiveness (use senior-prompt-engineer).",
"category": "Development",
"tags": [
"ai-agents",
"llm",
"rag",
"prompt-engineering",
"api"
],
"platforms": [
"chatgpt",
"claude-code",
"cursor"
],
"tested_environment": "Agent Skills convention (SKILL.md) — works with Claude Code, Cursor, ChatGPT/Codex and compatible agents",
"license": "MIT"
},
"prompts": {
"system": "████████████████████████████████████████████████████████████████████████████████████████████████",
"user_template": "██████████"
},
"variables": [],
"install": {
"steps": [
{
"title": "Download & unzip",
"detail": "Download the .zip package from SkillVault and unzip it. The llm-cost-optimizer/ folder contains SKILL.md plus any companion scripts, references and assets."
},
{
"title": "Copy into your agent skills directory",
"detail": "Move the folder into your agent's skills directory — e.g. ~/.claude/skills/ (Claude Code), .codex/skills/, .cursor/skills/ or your project's .claude/skills/ folder — then restart or reload the session."
},
{
"title": "Use it",
"detail": "Describe the task in natural language; the agent loads the skill automatically when the request matches the skill description."
}
]
},
"usage": {
"steps": []
},
"metadata": {
"author": "SkillVault",
"source": "https://skillvault.top/skills/llm-cost-optimizer",
"exported_at": "2026-11-03T07:42:48.002Z"
}
}format: aiskill/v1
skill:
name: llm-cost-optimizer
slug: llm-cost-optimizer
version: 1.0.0
description: >-
Use proactively whenever LLM API costs come up -- or should. Triggers include: 'my AI costs are
too high', 'optimize token usage', 'which model should I use', 'LLM spend is out of control',
'implement prompt caching', 'we're about to launch an AI feature', 'build me an AI endpoint'.
Don't wait for an explicit cost complaint -- if someone is building an AI feature, designing an
LLM endpoint, or choosing between models, cost architecture belongs in the conversation. Apply
immediately when any of these are true: a system prompt appears that exceeds a few hundred
tokens, all requests are hitting the same model, max_tokens is not set, or no per-feature cost
logging exists. NOT for RAG pipeline design (use rag-architect). NOT for improving prompt
quality or effectiveness (use senior-prompt-engineer).
category: Development
tags:
- ai-agents
- llm
- rag
- prompt-engineering
- api
platforms:
- chatgpt
- claude-code
- cursor
tested_environment: >-
Agent Skills convention (SKILL.md) — works with Claude Code, Cursor, ChatGPT/Codex and
compatible agents
license: MIT
prompts:
system: ████████████████████████████████████████████████████████████████████████████████████████████████
user_template: ██████████
variables: []
install:
steps:
- title: Download & unzip
detail: >-
Download the .zip package from SkillVault and unzip it. The llm-cost-optimizer/ folder
contains SKILL.md plus any companion scripts, references and assets.
- title: Copy into your agent skills directory
detail: >-
Move the folder into your agent's skills directory — e.g. ~/.claude/skills/ (Claude Code),
.codex/skills/, .cursor/skills/ or your project's .claude/skills/ folder — then restart or
reload the session.
- title: Use it
detail: >-
Describe the task in natural language; the agent loads the skill automatically when the
request matches the skill description.
usage:
steps: []
metadata:
author: SkillVault
source: https://skillvault.top/skills/llm-cost-optimizer
exported_at: "2026-11-03T07:42:48.002Z"
Installation guide
- 1
Download & unzip
Download the .zip package from SkillVault and unzip it. The llm-cost-optimizer/ folder contains SKILL.md plus any companion scripts, references and assets.
- 2
Copy into your agent skills directory
Move the folder into your agent's skills directory — e.g. ~/.claude/skills/ (Claude Code), .codex/skills/, .cursor/skills/ or your project's .claude/skills/ folder — then restart or reload the session.
- 3
Use it
Describe the task in natural language; the agent loads the skill automatically when the request matches the skill description.
How to use
Platform compatibility
ChatGPT GPTs
OpenAI
Paste the system prompt into the GPT "Instructions" field and keep the user template as your starter message.
Official docsClaude Code
Anthropic
Save the exported .aiskill JSON as a skill file in your project or paste the system prompt into CLAUDE.md / a slash-command markdown file.
Official docsCursor
Anysphere
Copy the system prompt into .cursor/rules/*.mdc (or Project Rules in Settings) and reference the user template in your agent prompts.
Official docsRelated skills
prompt-governance
Use when managing prompts in production at scale: versioning prompts, running A/B tests on prompts, building prompt registries, preventing prompt regressions, or creating eval pipelines for production AI features. Triggers: 'manage prompts in production', 'prompt versioning', 'prompt regression', 'prompt A/B test', 'prompt registry', 'eval pipeline'. NOT for writing or improving individual prompts (use senior-prompt-engineer). NOT for RAG pipeline design (use rag-architect). NOT for LLM cost reduction (use llm-cost-optimizer).
rag-architect
Use when the user asks to design a RAG pipeline, choose a chunking strategy or embedding model, pick a vector database, or evaluate retrieval quality (precision@k, recall@k, NDCG). Examples: 'design a RAG system for our docs', 'what chunk size should I use for this corpus', 'evaluate my retriever against ground truth'. NOT for general LLM cost tuning (use llm-cost-optimizer) or agent loops over retrieval (use agenthub).
senior-ml-engineer
ML engineering skill for productionizing models, building MLOps pipelines, and integrating LLMs. Covers model deployment, feature stores, drift monitoring, RAG systems, and cost optimization. Use when the user asks about deploying ML models to production, setting up MLOps infrastructure (MLflow, Kubeflow, Kubernetes, Docker), monitoring model performance or drift, building RAG pipelines, or integrating LLM APIs with retry logic and cost controls. Focused on production and operational concerns rather than model research or initial training.