QC16 TimeMissing logo

QC16 TimeMissing

Organization
cas-bigdatalab
QC16_TimeMissing

数据时序完整性检查工具。读取时间序列数据,设定数据记录频率(如每2分钟、30分钟或60分钟), 以此频率为基准遍历数据的时间范围,检查数据时序是否完整,发现缺失与多余数据点,最后输出为相同格式的文件。 当用户提到时序完整性检查、时间序列缺失检查、时间点完整性、数据遗漏检验等需求时使用此skill。 即使用户没有说出"时序完整性",只要任务涉及检查时间序列数据是否有遗漏或多余的时间点,就应该使用此skill。

Overview

Publishercas-bigdatalab
Repositorypiflow
Skill nameQC16_TimeMissing
Stars
541
Forks
172
Bundled files
4
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by cas-bigdatalab on GitHub. Read the source before you install it.

Installation

Install the QC16 TimeMissing AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/cas-bigdatalab/piflow/tree/master/workspace/skills/QC16_TimeMissing
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/cas-bigdatalab/piflow.git /tmp/piflow
mkdir -p .claude/skills
cp -r /tmp/piflow/workspace/skills/QC16_TimeMissing .claude/skills/QC16_TimeMissing
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable QC16 TimeMissing in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads QC16 TimeMissing on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so QC16 TimeMissing is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

QC16_TimeMissing 数据时序完整性检查Skill

功能概述

本skill用于检查时间序列数据的时序完整性。具体来说:

  1. 根据设定的时间频率(如每30分钟)生成预期的时间戳序列
  2. 遍历数据的实际时间范围,识别缺失的时间点(数据遗漏)
  3. 识别多余/不匹配的时间点(不在预期频率上的数据)
  4. 标记异常数据并输出

触发条件

当用户请求以下任务时,应使用此skill:

  • 时序完整性检查
  • 时间序列缺失检查
  • 时间点完整性检验
  • 数据遗漏检验
  • 检查时间序列是否有缺失时间点
  • 检查时间序列是否有非预期时间点

处理逻辑

  1. 读取输入的时间序列数据
  2. 设定数据记录频率(如30分钟)
  3. 计算数据时间范围内的预期时间点数量
  4. 生成预期时间戳序列
  5. 通过对比识别缺失时间点
  6. 通过对比识别多余/不匹配时间点
  7. 为异常数据添加质控标识
  8. 输出处理后的数据文件(原始数据 + 异常数据)

支持的文件格式

  • CSV (.csv)
  • TSV (.tsv)
  • Excel (.xls, .xlsx)
  • SPSS (.sav)

使用方法

步骤1:获取用户输入输出路径及参数

向用户确认:

  1. 输入文件路径
  2. 原始数据输出路径
  3. 异常数据输出路径
  4. 数据记录频率(分钟,如30表示30分钟)
  5. 时间字段名
  6. 时间格式
  7. 质控标识字段名(默认:QC0000)

步骤2:执行时序完整性检查脚本

在 scripts/ 目录下找到 QC16_TimeMissing.py,使用Python执行:

bash
python scripts/QC16_TimeMissing.py \
    --input_path <输入文件> \
    --origin_output_path <原始数据输出文件> \
    --error_output_path <异常数据输出文件> \
    --data_frequency <数据频率(分钟)> \
    --time_field <时间字段> \
    --time_format "<时间格式>" \
    --mark_field_name <质控字段名>

步骤3:返回结果

告知用户处理完成。

参数说明

参数必填说明
--input_path是输入文件路径
--origin_output_path是原始数据输出路径
--error_output_path是异常数据输出路径(缺失/多余时间点)
--data_frequency是数据记录频率(分钟,如30表示每30分钟)
--time_field是时间字段名
--time_format是时间格式(如:%Y-%m-%d %H:%M:%S)
--mark_field_name否质控标识字段名,默认QC0000

时间格式示例

格式示例
%Y-%m-%d2024-01-15
%Y-%m-%d %H:%M:%S2024-01-15 14:30:00
%Y/%m/%d2024/01/15

数据频率示例

频率值含义
2每2分钟
30每30分钟
60每60分钟

输出示例

Start reading input data...
[OK] Using encoding GB2312 successfully read CSV file
Successfully read file: /path/to/input.csv, data shape: (100, 5)
Min timestamp in data: 2024-01-15 00:00:00
Max timestamp in data: 2024-01-15 23:30:00

--- Missing time period check report ---
Found missing time periods, total 5 time points:

--- Extra/mismatched time period check report ---
No extra or mismatched time points found with expected frequency.

Start writing original data...
Successfully wrote file: /path/to/origin_output.csv
Start writing error data...
Successfully wrote file: /path/to/error_output.csv
Processing completed!

质控标识说明

  • Missing time point - 缺失时间点(数据遗漏)
  • Extra time point - 多余时间点(非预期时间点)

注意事项

  1. 时间字段必须是可解析的日期时间格式
  2. 数据频率必须为正整数(分钟)
  3. 异常数据会输出到单独的文件,包含时间字段和质控标识字段
  4. 原始数据保持不变,仅输出异常数据明细
  5. 时间格式需要与实际数据中的格式完全匹配

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the QC16 TimeMissing AI skill do?

数据时序完整性检查工具。读取时间序列数据,设定数据记录频率(如每2分钟、30分钟或60分钟), 以此频率为基准遍历数据的时间范围,检查数据时序是否完整,发现缺失与多余数据点,最后输出为相同格式的文件。 当用户提到时序完整性检查、时间序列缺失检查、时间点完整性、数据遗漏检验等需求时使用此skill。 即使用户没有说出"时序完整性",只要任务涉及检查时间序列数据是否有遗漏或多余的时间点,就应该使用此skill。

Why use QC16 TimeMissing on TypingMind?

Because you install it once and use it with any model. QC16 TimeMissing is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install QC16 TimeMissing in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cas-bigdatalab/piflow/tree/master/workspace/skills/QC16_TimeMissing. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use QC16 TimeMissing?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with QC16 TimeMissing?

As many as you like. As long as a model supports skills, you can use QC16 TimeMissing with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the QC16 TimeMissing AI skill free?

It is published on GitHub by cas-bigdatalab. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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