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 esg-data-integrationgit 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/esg-data-integration)<a href="https://agentmods.dev/skills/vivy-yi/finance-skills/esg-data-integration"><img src="https://agentmods.dev/badge/skills/vivy-yi/finance-skills/esg-data-integration/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/esg-data-integration"><img src="https://agentmods.dev/badge/skills/vivy-yi/finance-skills/esg-data-integration.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 | $0.00094 | $0.01915 |
| Opus 5 | $0.00047 | $0.00958 |
| Sonnet 5 | $0.00019 | $0.00383 |
| Haiku 4.5 | $0.00009 | $0.00192 |
Grade A, and why
esg-data-integration 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 11d 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 — 196 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(ESG 指标定义/披露要求/责任部门)。
/esg-data-integration — ESG 数据整合
Examples
→ 示例:用户说"碳排放数据来自三个不同部门,口径不一致,帮我统一一下",系统应调用本技能,统一碳排放数据口径。
→ 示例:用户说"Scope 3 排放核算还没有系统化,帮我设计一个收集流程",系统应调用本技能,设计 Scope 3 数据收集框架。
→ 示例:用户说"ESG 数据每年需要第三方鉴证,帮我准备鉴证所需的数据包",系统应调用本技能,准备 ESG 数据鉴证包。
第一步:识别 ESG 数据来源
ESG 指标分类框架:
环境(E):
□ 碳排放:范围1/2/3 [吨 CO2e]
□ 能源消耗:[MWh]
□ 水资源:[立方米]
□ 废弃物:[吨]
社会(S):
□ 员工数据:总人数/离职率/培训时长
□ 安全数据:工伤率/损失工时
□ 社区投资:[金额 万元]
□ 供应链:关键供应商数量
治理(G):
□ 董事会:独立董事比例/女性董事比例
□ 高管薪酬:CEO 薪酬比 [X]
□ 商业道德:反腐培训完成率 [%]
□ 数据隐私:泄露事件数
数据来源确认:
| 指标类别 | 指标名称 | 数据来源部门 | 系统 | 负责人 | 数据状态 |
|---------|---------|------------|------|--------|---------|
| E-碳排放 | 范围1排放 | 生产部 | [系统] | [姓名] | [✅已收集/⚠️待收集] |
| S-员工 | 离职率 | HR | [系统] | [姓名] | [✅已收集/⚠️待收集] |
第二步:数据收集
数据收集状态:
□ 应收集指标数:[X] 个
□ 已收集:[X] 个
□ 待收集:[X] 个 — 清单:
→ [指标名称] — [未收集原因] — 预计完成 [日期]
□ 收集完成率:[X]%
□ 是否达到报告质量要求:[✅ 是 / ⚠️ 否(完成率需 ≥[X]%)]
数据收集进度跟踪:
| 部门 | 应提交指标数 | 已提交 | 完成率 | 最后跟进 |
|------|------------|--------|--------|----------|
| [部门A] | [X] | [X] | [X]% | [日期] |
| [部门B] | [X] | [X] | [X]% | [日期] |
第三步:数据完整性检查
指标完整性检查:
□ 按 GRI/CSRD 框架要求检查:
| 框架指标 | 要求披露 | 实际披露 | 状态 |
|---------|---------|---------|------|
| GRI 302-1 | 是 | [是/否] | [✅/⚠️/🔴] |
| GRI 305-1 | 是 | [是/否] | [✅/⚠️/🔴] |
□ 时间序列完整性:
→ 上期有但本期缺失的指标:[列表]
→ 连续 [X] 期缺失的指标:[列表] — 须说明原因
数值合理性检查:
□ 同比异常检查:
→ [指标] 本期 [X],上期 [X],变化 [±X]% — [✅ 合理 / ⚠️ 需解释]
→ 变化原因:[描述]
□ 逻辑一致性检查:
→ 范围1+2 ≤ 范围3:[✅ 是 / 🔴 否]
→ 总人数 = 各部门人数之和:[✅ 是 / 🔴 差异 [X]]
→ 女性 + 男性 = 总人数:[✅ 是 / 🔴 差异 [X]]
第四步:数据质量评分
质量评分维度:
| 维度 | 权重 | 评分 | 说明 |
|------|------|------|------|
| 完整性 | [X]% | [X]/5 | 指标覆盖率 |
| 准确性 | [X]% | [X]/5 | 数据核实程度 |
| 一致性 | [X]% | [X]/5 | 跨期/跨系统一致性 |
| 时效性 | [X]% | [X]/5 | 数据更新时间 |
| 可追溯性 | [X]% | [X]/5 | 数据来源文档 |
加权总分 = Σ(评分 × 权重) = [X] / 5
质量等级:
4.0-5.0:✅ 高质量(可直接使用)
3.0-3.9:⚠️ 中等质量(使用前需核实)
< 3.0:🔴 低质量(须优先改进)
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.
- 11d ago First seen · 196 lines · 94 tokens per session scan A 5cd62d63dc66
esg-data-integration is a skill published in the GitHub repository vivy-yi/finance-skills (27 stars, last pushed 2mo ago), licensed MIT. It adds 94 tokens to every session and 1,915 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.
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