messaging-consistency-check

面向基金合规与信息披露领域的口径检查任务Skill，围绕「口径一致性检查助手」场景提供信息抽取、结构化分析与结果输出。

105 stars

Best use case

messaging-consistency-check is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

面向基金合规与信息披露领域的口径检查任务Skill，围绕「口径一致性检查助手」场景提供信息抽取、结构化分析与结果输出。

Teams using messaging-consistency-check should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

You only need a quick one-off answer and do not need a reusable workflow.
You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/messaging-consistency-check/SKILL.md --create-dirs "https://raw.githubusercontent.com/aifinlab/FinClaw/main/skills/fund_task_zlj/messaging-consistency-check/SKILL.md"

Manual Installation

Download SKILL.md from GitHub
Place it in .claude/skills/messaging-consistency-check/SKILL.md inside your project
Restart your AI agent — it will auto-discover the skill

How messaging-consistency-check Compares

Feature / Agent	messaging-consistency-check	Standard Approach
Platform Support	Not specified	Limited / Varies
Context Awareness	High	Baseline
Installation Complexity	Unknown	N/A

Frequently Asked Questions

What does this skill do?

面向基金合规与信息披露领域的口径检查任务Skill，围绕「口径一致性检查助手」场景提供信息抽取、结构化分析与结果输出。

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# 口径一致性检查助手 Skill

## 数据来源

### 1. 输入类型

- 基金公告/定期报告/招募说明书/产品说明材料
- 净值与收益时间序列、持仓与资产配置披露
- 销售/服务记录、客户反馈与问答素材(如适用)
- 合规口径与品牌内容规范(如适用)

### 2. 主要数据要素

- 监管法规条款与公司内控口径
- 披露模板、历史公告与对外口径
- 营销文案/素材与合规审查记录
- 披露时点、频率与留痕要求
- 敏感词与禁止性表述清单

### 3. 质量要求

- 输入信息尽量完整，包含时间区间、基金代码与核心指标
- 若来自 OCR/截图，请尽量校对错字与断行
- 对于未披露的数据需明确标注“缺失/待补充”

---

## 核心能力

- 识别敏感/违规表述与披露缺口
- 给出合规改写建议与风险等级提示

---

## 输出结构

### 1. 基础字段

- skill
- domain
- scene
- input_summary
- key_findings
- data_quality
- limitations

### 2. 场景扩展模块(按需输出)

- compliance
- flags
- revision_suggestions
- risk_level

---

## 使用示例

### 1. 安装依赖

```bash
pip install -r requirements.txt
```

### 2. 运行脚本

```bash
python scripts/main.py --input sample.txt --output-json result.json --output-md report.md
```

### 3. 输出示例

```json
{
  "skill": "口径一致性检查助手",
  "domain": "合规与信息披露",
  "scene": "口径检查",
  "input_summary": {
    "fund_code": "000000",
    "fund_name": "示例基金",
    "period": "2024Q4",
    "data_coverage": "净值/持仓/披露/市场"
  },
  "key_findings": [
    "关键结论1",
    "关键结论2"
  ],
  "data_quality": {
    "has_text": true,
    "text_length": 1200
  },
  "limitations": [
    "仅基于输入信息形成初步判断"
  ]
}
```

---

## 注意事项与限制

- 仅对输入文本进行结构化与初步判断，不替代人工投研或合规结论
- 若缺少关键数据(持仓、基准、时间区间)，结果需明确提示不完整
- 输出建议应结合实际业务口径与监管要求复核

---

## 适用场景

- 业务条线: 合规与信息披露
- 场景/能力: 口径检查
- 典型用户: 研究员、产品经理、渠道与客服、合规审查或内容运营人员

---

## License

- 代码部分遵循 MIT License
- 数据来源与披露口径需遵循对应数据供应商与监管要求