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 kpi-data-trackinggit 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/kpi-data-tracking)<a href="https://agentmods.dev/skills/vivy-yi/finance-skills/kpi-data-tracking"><img src="https://agentmods.dev/badge/skills/vivy-yi/finance-skills/kpi-data-tracking/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/kpi-data-tracking"><img src="https://agentmods.dev/badge/skills/vivy-yi/finance-skills/kpi-data-tracking.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 | $0.00099 | $0.01780 |
| Opus 5 | $0.00049 | $0.00890 |
| Sonnet 5 | $0.00020 | $0.00356 |
| Haiku 4.5 | $0.00010 | $0.00178 |
Grade A, and why
kpi-data-tracking 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 9d 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 — 166 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（KPI 定义/达标标准/目标值）。
/kpi-data-tracking — KPI 数据追踪与报告
Examples
→ 示例：用户说"帮我看一下本月各业务线的 KPI 完成进度，到今天为止"，系统应调用本技能，从各系统拉取数据并输出进度追踪表。
→ 示例：用户说"月度销售 KPI 数据要每周更新一次，给管理层看仪表盘用"，系统应调用本技能，建立自动化的 KPI 数据追踪机制。
→ 示例：用户说"财务共享中心上线了，想把报销 KPI 也纳入系统自动抓取"，系统应调用本技能，配置从 SAP Concur 拉取 KPI 数据。
第一步：获取 KPI 目标值
从 [预算系统/战略系统] 获取 KPI 目标值：
KPI 名称 | 目标值 | 单位 | 目标来源 | 备注
---------|--------|------|----------|----
[KPI1] | [X] | [%/万/个] | [年度预算/战略目标] | [说明]
[KPI2] | [X] | [%/万/个] | [季度分解] | [说明]
第二步：提取 KPI 实际值
从 [BI 系统/ERP/CRM/财务系统] 获取实际值：
KPI 名称 | 实际值 | 单位 | 数据来源 | 数据时间 | 数据状态
---------|--------|------|----------|----------|----
[KPI1] | [X] | [%/万/个] | [系统名] | [时间] | [✅已确认/⚠️待核实]
[KPI2] | [X] | [%/万/个] | [系统名] | [时间] | [✅已确认/⚠️待核实]
数据提取查询：
□ 查询条件：追踪期间 [YYYY-MM]，KPI 范围 [全部/指定列表]
□ 数据时间戳：[YYYY-MM-DD HH:MM]
□ 数据颗粒度：[月度/季度累计]
第三步：数据核对
系统间交叉核对：
□ BI vs ERP 数值一致性：[✅ 一致 / ⚠️ 差异 X%（差异值 [X]）]
□ 财务数据 vs 审计数据：[✅ 一致 / ⚠️ 差异待确认]
□ 本期 vs 上期合理性：[✅ 正常 / ⚠️ 异常波动 >X%]
□ 数据质量评估：
→ 完整度：[X]% — 缺失 [KPI 名称] 的原因
→ 及时性：[✅ 及时 / ⚠️ 延迟 X 天]
→ 一致性：[✅ 高 / ⚠️ 中 / 🔴 低]
异常数据处理：
□ 异常项：[KPI 名称] — 实际值 [X]，偏离预期 [±X%]
→ 原因：[数据录入错误/口径调整/真实业务变化]
→ 处理：[已修正/待确认/保持原值]
第四步：达成率计算
KPI 达成率 = 实际值 / 目标值 × 100%
KPI 名称 | 目标值 | 实际值 | 达成率 | 差距 | 状态
---------|--------|--------|--------|------|----
[KPI1] | [X] | [X] | [X]% | [±X] | [✅达标/⚠️接近/🔴未达标]
[KPI2] | [X] | [X] | [X]% | [±X] | [✅达标/⚠️接近/🔴未达标]
状态定义：
✅ 达标：达成率 ≥ [X]%（如 95%）
⚠️ 接近：达成率 ≥ [X]%（如 85%）但 < 达标线
🔴 未达标：达成率 < [X]%
趋势计算：
□ 环比变化（vs 上期）：
→ 提升：[X] 个 KPI — [KPI 名称] (+[X]%)
→ 下降：[X] 个 KPI — [KPI 名称] (-[X]%)
□ 同比变化（vs 去年同期）：
→ 提升：[X] 个 KPI — [KPI 名称] (+[X]%)
→ 下降：[X] 个 KPI — [KPI 名称] (-[X]%)
第五步：生成 KPI 追踪报告
═══════════════════════════════════════
KPI 追踪报告
报告期间：[YYYY-MM]
生成时间：[YYYY-MM-DD HH:MM]
═══════════════════════════════════════
【KPI 状态汇总】
状态 | 数量 | 占比
-------|------|------
✅ 达标 | [X] | [X]%
⚠️ 接近 | [X] | [X]%
🔴 未达标 | [X] | [X]%
合计 | [X] | 100%
【KPI 明细】
| # | KPI 名称 | 目标 | 实际 | 达成率 | 环比 | 同比 | 状态 |
|---|---------|------|------|--------|------|------|------|
| 1 | [KPI1] | [X] | [X] | [X]% | [±X] | [±X] | ✅ |
| 2 | [KPI2] | [X] | [X] | [X]% | [±X] | [±X] | ⚠️ |
| 3 | [KPI3] | [X] | [X] | [X]% | [±X] | [±X] | 🔴 |
【未达标 KPI 分析】
| # | KPI 名称 | 缺口 | 主因 | 建议措施 |
|---|---------|------|------|----------|
| 1 | [KPI3] | [X] | [原因] | [措施] |
【数据质量】
□ 数据完整度：[X]% — [✅达标/⚠️需关注]
□ 系统一致性：[✅通过/⚠️差异已处理]
═══════════════════════════════════════
置信度：[✅ 高 / ⚠️ 中 / 🔴 低]
═══════════════════════════════════════
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.
- 9d ago First seen · 166 lines · 99 tokens per session scan A c5dbafdbefc0
kpi-data-tracking is a skill published in the GitHub repository vivy-yi/finance-skills (29 stars, last pushed 3mo ago), licensed MIT. It adds 99 tokens to every session and 1,780 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 2026-09-03.
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