WorkBuddy: Don't Start with Research Reports, Build a Sourced Post-Market Daily First

Today I saw an article about WorkBuddy enabling normalized office automation. It starts with a workstation computer automatically switching PPTs late at night while the person scrolls their phone in a small bar. It's summarized into three scenarios: creating illusions of overtime, acting as a model patchwork monster, and doing table-moving grunt work. I quickly verified this and found it quite accurate, and also quite dangerous. What financial analysts fear most is that after moving tables, no one can explain where a column of numbers came from, if the dates are correct, or who changed it. I've been using WorkBuddy for about a month. I previously wrote that it shouldn't go head-to-head with research report generation agents, but this week my conviction is stronger: treat it as an auditable structured workbench, not a robot that automatically writes conclusions.

Beginners can start by building a minimal viable pipeline. My interface looks like this, though versions may vary slightly. Open WorkBuddy, click "New Project," and name it "Post-Market Data Daily Report." Inside, you'll see file areas, task areas, and template areas. First, drop three Excel files and one PDF into the file area. Don't rush to generate the daily report. Click "Organize Materials" and select "Rename and Record Source." The expected result is that each file gets an extra line showing the source filename and upload time. This step seems obsessive, but later error-checking relies entirely on it.

Next, find spreadsheet processing skills in the "Skill Hub." You can understand WorkBuddy's Skill Hub as a relay station calling different models and tools. But don't click "One-Click Generate." I usually split it into three tasks: parsing fields, merging fields, and applying templates. When parsing fields, map each table's column names to standard fields via Field Mapping, telling WorkBuddy which column is the real code. For example, Table A calls it "Securities Code," Table B calls it "ticker," both mapping to code. Being lazy here leads to misalignment during merging. I stepped on this landmine last week handling contract attachments; one wrong field made the whole table look correct.

For the template step, click "Template Management," upload the Excel template used for morning calls, and check "Output Strictly Matches Template." The key is that it can only output columns present in the template. Fix the columns for Source, Date, and Caliber Description; do not delete them. For validation, click "Validate" and set simple rules: date must equal the latest trading day, amount columns cannot be empty, and source columns cannot be unknown. The first time I ran it, there were 4 date anomalies, all due to old PDFs being misread. Later I learned my lesson: convert PDFs to Excel first via PDF to Excel, import only confirmed table regions, and then let WorkBuddy merge them.

I also simply divide permissions and collaboration. Only the data owner can modify the source file directory; analysts can comment; compliance has read-only access. I export WorkBuddy results to Enterprise WeChat Docs, setting document permissions to "commentable, non-overwritable." Template versions are named by date; anyone changing the template must write the reason in the change log. The log must answer source questions. When someone asks where a row of data came from, you don't need to recall; just click the source file.

Daily operations involve three fixed tasks. Check failed tasks for 15 minutes post-market, because WorkBuddy is prone to errors when handling mixed data sources, especially when Excel column names are inconsistent or PDF table lines are poorly recognized. Clean the field dictionary every Friday, grouping closing price, close, and close under close. Pull accuracy rates monthly. By my rough statistics, morning call preparation went from 90 minutes down to 56 minutes, saving about 34 minutes, roughly 37.5%. My environment has relatively stable data sources, so this might not apply to everyone.

The biggest pitfall to avoid is moving light scenarios from articles into core systems. Personal desktop-level automation is fine if you redo it after an error; but for post-market clearing, compliance audits, and external research reports, errors mean taking the blame. I've been trying East Money and Akshare recently. Just starting out, I won't let WorkBuddy scrape web pages and write conclusions directly. My approach is to clean data into tidy base tables in Excel first, then bring them into WorkBuddy for merging and templating. I've also used Huibo Investment Research PDFs for less than a week; attachment fields often mix up, so I still break down the steps.

If you're using it for the first time, I suggest not making a 10,000-word report today. Take three source tables and one daily report template, run a 10-minute sample first, and see if sources can be traced, dates can be validated, and templates can be locked. Expand only after it works. I use it for daily reports mainly because it allows source tracing, turning repetitive moving into a verifiable process. If the process isn't stable, fast conclusions are untraceable.