Six Common Pitfalls When Using WorkBuddy – A 4‑Month Review

1. Importing third‑party Skills can introduce security risks

After purchasing the 99 CNY professional version, the author eagerly added numerous Skills from unknown sources. A scan of one such Skill triggered a high‑severity warning, highlighting that these third‑party Skills can act like computer viruses with file‑read/write and command‑execution permissions, potentially causing severe damage.

2. Vague prompts let the Agent act unpredictably

The author assumed the Agent could understand brief instructions. When asked to "organize this directory and remove useless items," the Agent deleted backup drafts, demonstrating that without explicit, detailed prompts the Agent may perform unintended file operations. The lesson: always specify actions such as "analyze directory structure only, do not move, delete, or rename files until I confirm."

3. Overestimating large‑file processing leads to CPU saturation

Feeding a several‑hundred‑page document to WorkBuddy for summarization caused the CPU to hit 100 % and the computer to freeze. The author notes that the Agent is limited by context‑window size and hardware resources; massive batches must be split and processed incrementally.

4. The 99 CNY Pro version still consumes credits quickly

Running an Agent for multi‑round tasks consumes model calls and tool invocations, which depletes the allocated credits rapidly. The author advises monitoring credit usage and not leaving complex tasks unattended, as credits represent real monetary cost.

5. Expecting 100 % automation is unrealistic

WorkBuddy is an efficiency amplifier, not a full replacement for human judgment. Mid‑task misunderstandings and output errors are common, so users must retain control, verify direction, and correct instructions rather than delegating everything and stepping away.

6. Changing the underlying model can drastically alter results

Different models exhibit varying execution styles and autonomy. Swapping the model for a given Skill can produce completely different outcomes, so the author recommends testing several mainstream models on critical workflows and sticking with the most stable one.

Overall, WorkBuddy offers an intriguing local AI Agent experience for developers, but its power must be tempered with security awareness, precise prompting, resource‑aware task design, credit monitoring, realistic automation expectations, and careful model selection.