ai-cost-estimator
Skill0 starsChanged 3 months agoMITView on GitHub
Estimate LLM API cost for a workload (tokens per request x volume x price). 估算 LLM API 调用成本。
1 other project uses a version of this.
Adopted by
Most recently taken up by
- PlanetMarc/AgentSkillstheir own version, first seen Aug 3, 2026
Other versions1 project uses a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 1
- PlanetMarc/AgentSkills★ 1same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository14 other files it gives its agents.
- ai-code-reviewSkill
- ai-news-digestSkill
- ai-paper-highlightsSkill
- ai-tools-radarSkill
- ai-writing-assistantSkill
- embedding-searchSkill
- fine-tuning-decisionsSkill
- llm-evaluationSkill
- llm-security-checklistSkill
- mcp-server-guideSkill
- model-comparisonSkill
- prompt-engineering-playbookSkill
- rag-pipelineSkill
- structured-output-validationSkill
The file
Files
AI Cost Estimator | LLM 成本估算
Estimate how much an LLM API workload costs per month, before the bill surprises you.
在账单吓到你之前，先估出每月大概花多少钱。
When to use | 适用场景
- 用户要评估「接这个模型要花多少钱」
- 对比方案的成本
Workflow | 工作流程
- 摸清用量：每次请求平均输入/输出 token 数 × 每月请求量。
- 查当前价格：联网搜该模型每百万 token 的输入/输出价（价格常变，别靠记忆）。
- 计算：
输入token × 输入单价 + 输出token × 输出单价× 月请求数。 - 输出对比表 + 估算区间（乐观/悲观）。
- 标注数据截至日期。
Tips | 提示
- 输出 token 通常比输入贵很多，别只算输入。
- 缓存命中、批量折扣等能显著降本，有的话注明。
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.