Production Data Aggregation Automation? This Skill Is Amazing
To be honest, back when I worked in the production management department, the biggest headache was compiling production results at the end of each month. The BI system would export one set of data, the labor rate and unit price side would provide another, and assembly fees had to be pulled from an API. I had to manually merge these three data sources into one master list, which took several hours each time and was prone to errors. Then I discovered this production result aggregation automation skill, and it felt like I had found a lifesaver.
What does this skill do? In simple terms, it automatically combines data from three sources—BI results, labor rate unit prices, and the assembly fee API—into a single Excel file called 생산관리_마스터리스트.xlsx. All you need to do is run a Python script, and it handles everything else.
Installation and Configuration: Done in Three Steps
Installing this skill is incredibly simple—you can do it with npx:
npx skills add https://github.com/ha-giwan1203/GIWANOS_AgentOS --skill production-reportAfter installation, you need to ensure a few file paths are correct: the BI results file should be placed under 05_생산실적/BI실적/, the labor rate unit price file goes in 02_급여단가/임률단가/03_대원테크/, and you also need access to two API endpoints (one for assembly fees and one for production line data). If the paths are incorrect, the script will throw an error immediately.
How Smooth Is It to Run?
The command to run it is just one line:
PYTHONUTF8=1 python build_master.pyThe first time I ran it, I was pretty nervous, worried something might go wrong. But a few minutes later, the output file was generated. When I opened it, the number of rows matched the BI results perfectly, the unit price column had no empty values, and the API data summed up correctly. At that moment, I felt this operation really saved me a ton of hassle.
That said, this skill isn't something you can use blindly without thinking. There are several failure conditions you need to watch out for: for example, if the BI results file can't be opened, the unit price file is missing, or the API doesn't respond, the run will fail. There are also several interruption conditions, such as changes in the BI column structure, unit prices showing 0, or API field names being altered. In these cases, it will stop immediately rather than stubbornly generating incorrect data. I find this particularly thoughtful—better to not produce output than to produce wrong output.
Personal Tips from Usage
After using it a few times, I've summarized some lessons: before running, you must confirm that the BI results file is up to date. This is because, in the skill's prerequisite steps, the BI results file needs to be updated first through another skill (production-result-upload). If the file is outdated, the generated master list will also contain stale data, and all your effort will be wasted.
Additionally, once the output file is generated, never edit it manually, because the next time you rerun the script, it will be overwritten directly. If you want to keep a specific version, remember to copy it to another location first.
Who Is This Skill Suitable For?
If you work in production management, production planning and scheduling, or data analysis, and you frequently deal with similar result aggregation tasks, then this skill is practically tailor-made for you. It automatically integrates three data sources, eliminating a huge amount of manual work, and ensures data quality through a validation mechanism. The only requirement is that you have some basic Python knowledge—just enough to run a script.
In short, this skill transformed me from someone who spent half a day every week building spreadsheets into someone who just sips coffee while watching the script finish. Although the name is in Korean, the operational logic is very straightforward—a quick look at the documentation and you'll know how to use it. If you have similar needs, I'd suggest giving it a try.
By the way, the author is 하지완, and the repository is in GIWANOS_AgentOS. If you have any questions, you can check the source code—it's written quite clearly.
