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Data Completeness Check
作者
mirowangl-ops
· GitHub ↗
· v1.0.0
· MIT-0
103
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当前安装
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版本数
在 OpenClaw 中安装
/install xiaozhua-data-completeness-check
功能描述
数据完整性判断机制。通过前晚基准值 vs 早上值对比，判断 Power BI 数据是否回灌完毕。适用于任何有延迟回灌的数据源。避免发送不完整数据。
使用说明 (SKILL.md)
数据完整性判断
方法：双时间点对比
步骤
- 前一天晚上 23:30 — 抓取关键指标基准值（如 Paid Subs）
- 第二天早上 9:00 — 再抓一次同一指标
- 对比判断：
- 两次值不同 → 数据有更新，可能完整
- 两次值相同 → 数据没跑完，继续等
- 达到经验参考值 → 大概率完整
经验参考值（O+ 业务）
- Paid Subs 工作日 ~14,000
- Paid Subs 周末/节假日 ~10,000
- Revenue 工作日 ~$20,000-25,000
决策规则
- 达标 + 与昨晚不同 → ✅ 数据完整，直接发简报
- 达标但与昨晚相同 → ⚠️ 可能没跑完，等 30 分钟重试
- 未达标 → ❌ 先发 Lei 确认，不直接发群
通用化
此方法适用于任何有 T+1 延迟的数据源：
- 选一个敏感指标作为"哨兵"
- 建立该指标的经验参考范围
- 用双时间点对比判断是否回灌完毕
安全使用建议
This skill is high-level and coherent, but check these practical points before enabling it: 1) Decide and document how metrics will be fetched (Power BI API, DB query, exported file) and ensure any credentials used are minimal-scope and provided separately from this skill. 2) If you allow the agent to call connectors autonomously, confirm which tokens/accounts it will use and limit their scope. 3) Define and record where the sampled values and logs will be stored and who can view them. 4) Test the procedure on non-sensitive data to validate thresholds and retry logic (30-minute wait) to avoid false positives/negatives. 5) If you need the skill to perform automated fetches, consider adding explicit instructions about the connector and required permissions so users understand what access will be needed.
功能分析
Type: OpenClaw Skill
Name: xiaozhua-data-completeness-check
Version: 1.0.0
The skill bundle consists of logical instructions for an AI agent to perform data completeness checks by comparing metrics at different time intervals. It contains no executable code, network requests, or suspicious instructions, and its behavior is entirely aligned with the stated purpose of validating Power BI data integrity in SKILL.md.
能力评估
Purpose & Capability
The name/description (data completeness check for Power BI / T+1 sources) matches the instructions (capture a sentinel metric at 23:30 and 09:00 and compare). Nothing requested (no env vars, no binaries, no installs) is disproportionate to that stated purpose.
Instruction Scope
The SKILL.md is high-level and prescribes a manual/automated sampling workflow (capture metric at two times, compare, use thresholds). It does not specify HOW to fetch metrics (e.g., Power BI API, DB query, or exported file) or where results are recorded. This ambiguity is not inherently malicious but means implementers must decide which connector/credentials to use.
Install Mechanism
No install spec and no code files are present (instruction-only), so nothing is written to disk and no third-party packages are pulled. This is the lowest-risk install surface.
Credentials
The skill declares no required environment variables, secrets, or config paths. The described task could require Power BI or database credentials in practice, but those are not requested by the skill itself—so the declared access is proportionate.
Persistence & Privilege
always is false and the skill does not request persistent presence or modify other skills or agent-wide settings. Autonomous invocation is allowed by platform default but is not excessive given the simple nature of the instructions.
如何使用
- 确保已安装 OpenClaw（本地或 Docker 部署）
- 在对话框中输入安装命令：
/install xiaozhua-data-completeness-check - 安装完成后，直接呼叫该 Skill 的名称或使用
/xiaozhua-data-completeness-check触发 - 根据 Skill 的参数说明提供必要输入，即可获得结构化输出
版本历史
v1.0.0
- Introduced a data completeness checking mechanism based on dual time-point comparison (late night vs. morning values).
- Outlined decision rules to verify if Power BI data has been fully updated before sending reports, preventing incomplete data delivery.
- Provided practical reference thresholds for key metrics (e.g., Paid Subs, Revenue) on weekdays and weekends/holidays.
- Made the approach generalizable for any delayed (T+1) data sources by selecting a key "sentinel" metric and establishing experience-based reference ranges.
元数据
常见问题
Data Completeness Check 是什么？
数据完整性判断机制。通过前晚基准值 vs 早上值对比，判断 Power BI 数据是否回灌完毕。适用于任何有延迟回灌的数据源。避免发送不完整数据。 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 103 次。
如何安装 Data Completeness Check？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install xiaozhua-data-completeness-check」即可一键安装，无需额外配置。
Data Completeness Check 是免费的吗？
是的，Data Completeness Check 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。
Data Completeness Check 支持哪些平台？
Data Completeness Check 跨平台运行，可在任意部署了 OpenClaw / Claude Code 的环境中使用（cross-platform）。
谁开发了 Data Completeness Check？
由 mirowangl-ops（@mirowangl-ops）开发并维护，当前版本 v1.0.0。
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