esg-data-integration

esg-data-integration is a skill for Claude Code from vivy-yi/finance-skills. It costs 94 tokens per session (1,915 once invoked), scanned A, original, MIT.

A workflow for collecting environmental, social, and governance data from multiple departments and systems, checking its completeness and accuracy, and producing a data-quality report. ESG covers topics such as emissions, employees, safety, board oversight, ethics, and privacy.

In plain words
What is it for?
Use it to identify data sources and owners, track collection progress, standardize measures such as carbon emissions, check data against reporting frameworks, and prepare information for external assurance.
Why use it?
It helps resolve missing data and inconsistent definitions when different departments supply information for an ESG report or review.

Skill for Claude Code

Written for Claude Code: argument-hint in frontmatter. Also seen: mentions CLAUDE.md.

Good fit Use it to identify data sources and owners, track collection progress, standardize measures such as carbon emissions, check data against reporting frameworks, and prepare information for external assurance.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/vivy-yi/finance-skills/esg-data-integration
Install

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.

Any agent
npx skills add vivy-yi/finance-skills --skill esg-data-integration
Clone the repo
git clone --depth 1 https://github.com/vivy-yi/finance-skills

Made for: Claude Code.

Wrote 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.

agentmods badge for esg-data-integration

README.md
[![agentmods](https://agentmods.dev/badge/skills/vivy-yi/finance-skills/esg-data-integration/github.svg)](https://agentmods.dev/skills/vivy-yi/finance-skills/esg-data-integration)
Your own site
<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.

agentmods 80×15 button for esg-data-integration

Your own site · 80×15
<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>
Per session 94 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,915 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 11d ago against content hash 5cd62d63dc66, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

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.

finance-skills/skills/esg-reporting/skills/esg-data-integration/SKILL.md · 196 lines

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)

Read the full file on GitHub · 196 lines

Changes

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.

  1. 11d ago First seen · 196 lines · 94 tokens per session scan A 5cd62d63dc66

Subscribe to this mod's changes

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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