Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add vivy-yi/finance-skills --skill cost-center-performancegit clone --depth 1 https://github.com/vivy-yi/finance-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/vivy-yi/finance-skills/cost-center-performance)<a href="https://agentmods.dev/skills/vivy-yi/finance-skills/cost-center-performance"><img src="https://agentmods.dev/badge/skills/vivy-yi/finance-skills/cost-center-performance/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/vivy-yi/finance-skills/cost-center-performance"><img src="https://agentmods.dev/badge/skills/vivy-yi/finance-skills/cost-center-performance.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00106 | $0.02669 |
| Opus 5 | $0.00053 | $0.01334 |
| Sonnet 5 | $0.00021 | $0.00534 |
| Haiku 4.5 | $0.00011 | $0.00267 |
Grade A, and why
cost-center-performance scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 275 lines — stays where its author put it; the contents beside it link to each section on GitHub.
加载上下文
首次使用时： 读取 ../../CLAUDE.md 获取场景级配置（成本中心定义/产出计量/绩效标准）。
/cost-center-performance — 成本中心绩效分析
Examples
→ 示例：用户说"帮我分析一下各成本中心的绩效，看看谁超支了"，系统应调用本技能，按成本中心执行绩效分析。
→ 示例：用户说"制造费用最近上涨明显，帮我找找是哪些科目带动的"，系统应调用本技能，执行制造费用变动归因分析。
→ 示例：用户说"新产线上线后成本超了 15%，帮我分析一下原因"，系统应调用本技能，评估新产线的成本超支原因。
第一步：获取成本中心数据
从 [ERP]/[COST]/[MES] 获取各成本中心数据：
□ 成本中心清单：[CC01]/[CC02]/[CC03]/[CC04]...
□ 成本中心类型：生产成本中心/费用成本中心
□ 获取字段：
预算成本 | 实际成本 | 产出数量 | 差异
第二步：生产成本中心分析
生产成本中心绩效：
□ 成本中心：[CC01] — [生产部 A 线]
□ 产品：[产品 A]
| 指标 | 预算 | 实际 | 差异 | 差异率
----------|----------|----------|----------|--------
总成本 | [XXX万] | [XXX万] | [±XXX万] | [±X%]
产出数量 | [X] 件 | [X] 件 | [±X] 件 | [±X%]
单位成本 | [X] 元/件| [X] 元/件| [±X] 元/件| [±X%]
□ 成本差异：[±XXX万]
□ 产量差异：[±X] 件
□ 单位成本差异：[±X] 元/件（[±X%]）
成本分解：
□ 直接材料成本：[XXX万]（[X%]）
□ 直接人工成本：[XXX万]（[X%]）
□ 制造费用：[XXX万]（[X%]）
□ 标准单位成本：[X] 元/件
□ 实际单位成本：[X] 元/件
□ 差异：[±X] 元/件
效率指标：
□ 产能利用率：[X%]（目标 [X%]）
□ 良率：[X%]（目标 [X%]）
□ 人均产出：[X] 件/人（目标 [X] 件/人）
□ 机器利用率：[X%]（目标 [X%]）
绩效评价：
绩效等级：[A/B/C/D]
□ 成本控制：A（优于目标）/ B（符合目标）/ C（轻微超支）/ D（严重超支）
□ 产出效率：A（超产）/ B（达标）/ C（轻微不足）/ D（严重不足）
□ 质量水平：A（良率高）/ B（达标）/ C（轻微下降）/ D（质量问题）
综合评价：[优秀/良好/合格/不合格]
第三步：费用成本中心分析
费用成本中心绩效：
□ 成本中心：[CC10] — [销售一部]
| 指标 | 预算 | 实际 | 差异 | 差异率
------------|----------|----------|----------|--------
费用总计 | [XXX万] | [XXX万] | [±XXX万] | [±X%]
人员数量 | [X] 人 | [X] 人 | [±X] 人 | [±X%]
人均费用 | [X] 万/人| [X] 万/人| [±X] 万/人| [±X%]
费用结构：
□ 人员成本：[XXX万]（[X%]）
□ 差旅费：[XXX万]（[X%]）
□ 招待费：[XXX万]（[X%]）
□ 市场推广：[XXX万]（[X%]）
□ 其他：[XXX万]（[X%]）
效率指标：
□ 人均创收：[X] 万/人（目标 [X] 万/人）
□ 费用创收比：[X]%（目标 [X]%）
□ 客户覆盖率：[X] 家/人（目标 [X] 家/人）
绩效评价：
绩效等级：[A/B/C/D]
□ 费用控制：A（优于目标）/ B（符合目标）/ C（轻微超支）/ D（严重超支）
□ 人均产出：A（高于目标）/ B（达标）/ C（轻微不足）/ D（严重不足）
综合评价：[优秀/良好/合格/不合格]
第四步：跨成本中心对比
成本中心横向对比：
| 成本中心 | 产出 | 实际成本 | 标准成本 | 差异 | 单位成本 | 绩效 |
|---------|--------|----------|----------|--------|----------|------|
[CC01] | [X]件 | [XXX万] | [XXX万] | [±XXX万] | [X]元/件 | [A] |
[CC02] | [X]件 | [XXX万] | [XXX万] | [±XXX万] | [X]元/件 | [B] |
[CC03] | [X]件 | [XXX万] | [XXX万] | [±XXX万] | [X]元/件 | [C] |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 275 lines · 106 tokens per session scan A a68fb05d0c85
cost-center-performance is a skill published in the GitHub repository vivy-yi/finance-skills (29 stars, last pushed 2mo ago), licensed MIT. It adds 106 tokens to every session and 2,669 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
wealth-structure
A framework for thinking about wealth through ownership, responsibility, leverage, and avoiding risks that could end your progress.
alpha-vantage
Access real-time and historical stock market data, forex rates, cryptocurrency prices, commodities, economic indicators, and 50+ technical indicators via the Alpha Vantage API. Use when fetching stock prices (OHLCV), company fundamentals (income statement, balance sheet, cash flow), earnings, options data, market…
hedgefundmonitor
Query the OFR (Office of Financial Research) Hedge Fund Monitor API for hedge fund data including SEC Form PF aggregated statistics, CFTC Traders in Financial Futures, FICC Sponsored Repo volumes, and FRB SCOOS dealer financing terms. Access time series data on hedge fund size, leverage, counterparties, liquidity…
edgartools
Python library for accessing, analyzing, and extracting data from SEC EDGAR filings. Use when working with SEC filings, financial statements (income statement, balance sheet, cash flow), XBRL financial data, insider trading (Form 4), institutional holdings (13F), company financials, annual/quarterly reports (10-K…
asc-ppp-pricing
Set territory-specific pricing for subscriptions and in-app purchases using current asc setup, pricing summary, price import, and price schedule commands. Use when adjusting prices by country or implementing localized PPP strategies.
fred-economic-data
Query FRED (Federal Reserve Economic Data) API for 800,000+ economic time series from 100+ sources. Access GDP, unemployment, inflation, interest rates, exchange rates, housing, and regional data. Use for macroeconomic analysis, financial research, policy studies, economic forecasting, and academic research requiring…