Agent Skills › QuixiAI/Hexis › cost-report

cost-report

GitHub

查询 API 使用账本，按提供商、模型和时间段生成详细的成本报告。支持预算阈值监控和支出预测，帮助评估 LLM、嵌入及工具调用的费用效率。

skills/installed/cost-report/SKILL.md QuixiAI/Hexis

Trigger Scenarios

用户询问花费或展示使用情况心跳检测接近预算阈值时评估模型或提供商切换的成本效益

Install

npx skills add QuixiAI/Hexis --skill cost-report -g -y

More Options

Non-standard path

npx skills add https://github.com/QuixiAI/Hexis/tree/main/skills/installed/cost-report -g -y

Use without installing

npx skills use QuixiAI/Hexis@cost-report

指定 Agent (Claude Code)

npx skills add QuixiAI/Hexis --skill cost-report -a claude-code -g -y

安装 repo 全部 skill

npx skills add QuixiAI/Hexis --all -g -y

预览 repo 内 skill

npx skills add QuixiAI/Hexis --list

SKILL.md

Frontmatter

{
    "name": "cost-report",
    "aliases": [
        "cost",
        "costs",
        "spend",
        "spent",
        "usage",
        "tokens",
        "budget",
        "bill",
        "billing"
    ],
    "category": "analytics",
    "contexts": [
        "heartbeat",
        "chat"
    ],
    "requires": {
        "tools": [
            "query_usage"
        ]
    },
    "bound_tools": [
        "query_usage"
    ],
    "description": "Query and report on API usage costs across LLM, embedding, and tool providers"
}

Usage Cost Reporting

Query the API usage ledger and produce clear, actionable cost reports broken down by provider, model, and time period.

When to Use

When the user asks "how much have I spent" or "show me my usage"
During heartbeats to check if spending is approaching a configured budget threshold
When evaluating whether to switch models or providers based on cost efficiency
As part of a daily or weekly briefing that includes operational metrics
After a heavy ingestion or research session to assess the cost impact

Step-by-Step Methodology

Determine the time window: Default to the current billing period (calendar month) unless the user specifies a range. Common windows: today, this week, this month, last 30 days, custom range.
Query the ledger: Use query_usage with the appropriate date range. The tool returns rows from the api_usage table with provider, model, token counts (input/output), and computed cost.
Aggregate by dimension: Break down the raw data into useful summaries:
- By provider: Total cost per provider (OpenAI, Anthropic, local inference, etc.)
- By model: Cost per model to identify which models consume the most budget
- By category: Separate LLM inference costs from embedding costs from tool API costs
- By day: Daily trend to spot spikes or patterns
Compute key metrics: Calculate:
- Total spend for the period
- Average daily cost
- Cost per conversation (if trackable)
- Projected monthly cost at current burn rate
Compare to budget: If a budget threshold is configured, compare current spend and projection against it. Flag if on track to exceed.
Present the report: Format as a clean table or summary. Lead with the headline number (total spend), then break down by the most useful dimension.

Quality Guidelines

Always show actual numbers, not vague descriptions. "You have spent $4.32 this month" is useful; "you have moderate usage" is not.
Round costs to two decimal places for readability.
When projecting monthly cost, note that it is an extrapolation and actual spend may vary.
If the usage ledger is empty or the query returns no data, say so plainly rather than reporting zeros that could be misleading.
In heartbeat context, only generate a cost report if spend is approaching a threshold or if a goal explicitly asks for cost monitoring. Do not waste energy on routine cost checks every heartbeat.
Protect cost data as operational metadata. Do not expose it to external services.