100 Questions About WorkBuddy

Preface

This is a "100 questions" guide: a way to break a large subject into questions, then read it one question at a time.

WorkBuddy is not mainly about answering questions. It is about taking a task, reading the material, breaking it into steps and delivering a verifiable file into your workspace. The guide follows six stages: setting the right expectations, understanding Ask/Plan/Craft and connectors, avoiding beginner mistakes, building acceptance standards, running real office workflows, and knowing the boundaries.

Quick answers

• Do not use it as a chat box. Give it a real task, real material and an explicit output path.
• Ask to inspect the material first, Plan the steps second, and Craft only after that. Stop at Plan when the task involves overwriting, sending or deleting.
• The workspace is the security boundary. Grant only the folders it needs, and give connectors the minimum permissions.
• Accept the result by opening and editing the file and tracing every conclusion back to the source data, not by trusting "done".
• Automate only after the manual workflow is stable. Sending, publishing and overwriting files require human confirmation.

Compiled from WorkBuddy documentation and public material, structured and lightly completed for this site.

This is an edited edition: some source passages were truncated by OCR, so the body focuses on an actionable question list with concise answers.

1. Fundamentals and positioning

1. Why do many people install WorkBuddy and end up using it as a chat box?

Because they only ask questions and never hand over a complete task. WorkBuddy's value is reading material, breaking work into steps and delivering a verifiable file, not producing a few more chat replies.

2. Which three kinds of people get positive feedback from WorkBuddy in the first week?

Product, operations and project roles; people with clear material and a clear output format; people willing to check results against acceptance criteria.

3. Why are ordinary office tasks a better first step than open-ended research?

Office tasks have clear inputs, outputs and acceptance criteria. Open-ended research has fuzzy boundaries and subjective judgement, so a beginner cannot tell whether the result is right.

4. What exactly is a "verifiable result", and how is it different from a chat reply?

A verifiable result is a document, spreadsheet, web page or deck you can open, edit and check. A chat reply is only text; it is not a deliverable.

5. Why is WorkBuddy's focus less "it can answer" and more "it puts the finished file in your folder"?

Most office effort goes into the path from "I have an idea" to "I have a file I can use". WorkBuddy shortens that path by producing the file directly, instead of making you copy, format and save it yourself.

6. Which tasks are most likely to work on the first try?

Tasks where the material is already in hand, the target result is clear, and little outside judgement is needed: Excel to weekly report, meeting notes to action items, rule-based file renaming.

7. Why does "what can you do for me?" usually produce a bad result?

It is too broad, so you get a feature menu instead of a task. Give it a real task and you can see whether it can read, plan and deliver in your context.

8. How do I tell a real workflow from a casual experiment?

A real workflow states four things: what material is processed, what result is produced, where the result is placed, and who accepts it. If those are unclear, it is an experiment.

9. Why are file-processing tasks a better starting point than open-ended creation?

File tasks carry natural constraints: file names, formats, folders, fields and content range. You can tell whether the result is right by opening the file.

10. When should I use WorkBuddy instead of continuing by hand?

When the task repeats, the steps are stable and the input and output are relatively consistent. The cost of repeated attention is usually higher than the cost of setup.

11. Why do first attempts often fail because the task is too broad, not because the model is weak?

A broad task forces the model to guess what matters. If the folder, output format, forbidden actions and acceptance criteria are unspecified, doing more work often means drifting further.

12. How do I know which repetitive work is worth handing to WorkBuddy?

Two signals: it keeps recurring and feels mechanical; its input and output are already stable. If both are true, it is worth trying.

13. What is the gap between "occasionally use it" and "open it every day"?

A stable, repeatable, verifiable workflow. Without a fixed workflow, it remains an occasional experiment.

14. Which tasks are more likely to fail with WorkBuddy?

High-risk, judgement-heavy tasks; anything involving payments, publishing or deletion; tasks with unclear material boundaries; results that cannot be verified.

15. Why does giving a real task reveal more than asking what it can do?

A real task exposes how it reads material, plans steps, handles exceptions and delivers a file. Asking for features only shows the marketing layer.

2. Core architecture and key mechanics

16. When should I use Ask, Plan and Craft?

Ask inspects the material and current state. Plan defines steps, risks and output. Craft executes and delivers. Use them in that order.

17. Why does jumping from an incomplete Ask straight to Craft damage files?

Without a file list, folder structure and forbidden actions, Craft can overwrite, delete or edit the wrong files. Confirm what exists, what may change and what must not be touched.

18. How do I know a task should stop at Plan instead of starting immediately?

Stop at Plan when the task touches multiple files, overwrites originals, sends externally, has unclear data definitions or carries meaningful risk.

19. When is the Auto model enough, and when should I choose a model myself?

Auto is fine for routine, low-risk tasks with modest formatting demands. Choose a stronger model for critical data, complex judgement, long documents or strict formatting.

20. Why does swapping the model visibly change the stability of the deliverable?

Models differ in instruction following, format retention, long-text handling and tool use. After a model change, re-run acceptance instead of assuming the result is the same.

21. What layer of judgement do the model and the expert each own?

The model executes, generates and calls tools. The expert owns domain judgement, trade-offs, risk identification and acceptance standards.

22. When is an expert panel worth it, and when is a single expert enough?

A single expert fits a clearly defined domain. A panel fits cross-domain work that benefits from challenge and multiple perspectives.

23. Why does installing more skills not necessarily make you stronger?

More skills mean more selection cost, trigger conflicts and maintenance. Install by frequent workflow, not by count.

24. How do I decide whether a repeated process should become a skill?

If it repeats, the steps are stable, the output is verifiable and you keep re-explaining it, turn it into a skill.

25. Which annoying download, upload and switching steps do connectors remove?

Connectors let WorkBuddy read and write external documents directly, removing export, download, upload, format conversion and platform switching.

26. Why does connecting Tencent Docs change the way you work?

You no longer export the material first. WorkBuddy can read, organize and write back to Tencent Docs, turning the workflow from file shuffling into direct content work.

27. How do I decide whether a task needs a connector or just local files?

Use a connector when the material lives in an external service and needs live read/write. Use the local workspace when the material is already on disk and processed once.

28. Why is the workspace both an efficiency issue and a security boundary?

The workspace limits what WorkBuddy can read and write. A clear boundary improves efficiency and reduces accidental damage; a broad boundary increases deletion, modification and leak risk.

29. Why is giving WorkBuddy the entire desktop usually a bad idea?

The desktop contains private files, account information, unrelated projects and sensitive material. Giving it the whole desktop removes the boundary and makes risk uncontrollable.

30. Does the remote assistant change the entry point or the way work executes when you are away?

Both. The entry point moves from the computer to the phone, and execution changes from "you must sit at the desk" to "you can instruct, monitor and confirm from anywhere".

31. Why must automation be built after the manual workflow works?

Automation multiplies mistakes. First make inputs, outputs, exceptions and confirmation points stable by hand, then schedule it.

32. MCP and skills both look like extensions. What does each solve?

MCP connects external tools and data. Skills codify a workflow and its judgement. Connectors connect a specific platform. They solve different problems.

33. Why do memory, data management and task archiving affect whether you can continue later?

Without records, you must re-explain the material, definitions and progress next time. Memory, archiving and clean data management make work continuous.

3. Beginner pitfalls and anti-patterns

34. Why does an unclean workspace almost always cause trouble later?

Old files, caches, permissions and unrelated folders get mixed together, so WorkBuddy cannot tell what may be changed. An unclear boundary makes accidental edits and deletions a matter of time.

35. What abnormal signals should I watch for during an Ask inspection?

Unexpected file counts or paths, missing data, duplicates, excessive permissions, an unclear output folder, and unreadable formats.

36. Why should "overwrite the original file" in a Plan make me stop immediately?

Overwriting destroys the source and is hard to recover. Back up first, or write to a new file or folder.

37. Why does letting it fill in missing data plant a landmine?

The model may guess or invent values. Missing data should be marked "to confirm" by a human, not filled in to make the deliverable look complete.

38. What danger signals do I miss when the result looks convincing?

Data source, definitions, dates, units, permissions and calculation steps. The more convincing it looks, the more you need to trace it back to the source.

39. Why is a finished weekly report or deck not proof that the conclusion is reliable?

A finished file proves formatting, not correct data, consistent definitions or a valid conclusion. Every conclusion must be traceable to the source.

40. Why are blank cells the easiest thing for WorkBuddy to mishandle?

Blanks can be treated as zero, ignored or filled incorrectly, which skews totals, period comparisons and growth rates. Define the blank-value rule first.

41. When period comparisons and definitions get mixed up, which judgements go wrong first?

Growth, decline, grouping, deduplication and ranking all drift. Without a shared definition, charts and conclusions are not trustworthy.

42. Why does saying "redo it" often make the result worse?

A redo discards the parts you already confirmed and forces the model to guess again. A targeted revision is safer: say what to keep and what to change.

43. How do I request a local revision without destroying the parts that were right?

Name the file, location, parts to keep and parts to change. Ask for a plan or diff first, then approve execution.

44. Why does over-broad connector authorization make it hard to trust the agent later?

The broader the authorization, the more it can read and write, and the higher the risk of mistakes and leaks. Grant the minimum, read-only first.

45. What confirmation gate do people most often skip when instructing remotely?

The pre-send confirmation. Sending, publishing, deleting and paying need a second confirmation, especially from a phone.

46. Why does an automation that worked once not stay stable on a schedule?

Credentials expire, paths change, the client goes offline and target-space permissions change. The first successful run is only the beginning.

47. Which batch file-organization tasks most often cause misclassification and misplacement?

Tasks with irregular names, messy folders, vague classification rules, duplicate names or sensitive files. Inspect first, then execute.

48. Why do many unnamed versions make everything messier?

You cannot tell which is final, which is editable and which was sent. Use dates, version numbers and purpose in the name.

49. Why are messy task steps hard to reuse even if the result was produced?

Messy steps cannot be reproduced, accepted or handed over. Write the process as a checklist or skill.

50. When does an expert panel go from helping to making the process messier?

When experts conflict, produce too many opinions and no one decides. Use a panel only when multiple perspectives are genuinely needed.

51. Why can installing too many skills push a task off track?

Skills compete for triggers, conflict with each other and can make the model choose the wrong workflow. Keep a small set of high-frequency, clearly bounded skills.

52. If automation cannot see the target space, where should I look first?

Check authorization and whether the space exists, then whether the client is online, the account is correct and the path has changed.

53. Why must the final pass on sending email or publishing documents be human?

Once sent or published, it cannot be taken back. AI can draft and check, but a human should confirm the final action.

4. Result validation and stability

54. How do I set a completion standard that can actually be accepted?

State the output file, fields, format, data range, completion time and acceptor. It is done when the file opens, can be edited and can be checked.

55. Why is it hard to confirm delivery without an output-path constraint?

No path means no known delivery location. Specify the folder, file name and version so the result can be found and accepted.

56. How do I ask for staged progress reports without a stream of noise?

Ask for four things only: what is done, what remains, the next step and any risks. Skip the play-by-play.

57. How do I check that every conclusion in a report traces back to source data?

Point each conclusion to the original table, row, field and calculation. A conclusion that cannot be traced should be marked "to confirm".

58. Why is turning correlation into causation the most dangerous office illusion?

Correlation means two things move together, not that one causes the other. Writing correlation as causation misleads decisions and is hard for non-specialists to catch.

59. When a file cannot be read, should I inspect the material or change the model first?

Inspect the material first: format, permissions, corruption, encryption, path. If the material is fine, then try another model or parser.

60. How do I use "to confirm" to keep risk visible instead of hiding it in the deliverable?

List uncertain data, definitions, assumptions and gaps separately in a "to confirm" section. Do not fill the deliverable with guesses.

61. Why must I check whether a file can still be edited, not just look at the preview?

A preview only proves it looks right. It must open, formulas must calculate and the format or layers must remain editable to count as a real delivery.

62. How do I know an automation is ready to run largely unattended?

It produces stable results repeatedly, raises alerts on exceptions, has verifiable output and can be rolled back on failure.

63. Why should I re-run acceptance after changing a skill, connector or model?

Changing a component changes how material is read, processed and written. The old acceptance may no longer hold, so run it again.

64. After an upgrade or restart, the workspace looks "gone". Where do I look first?

Check the path, account, permissions and client version, then whether the workspace changed or sync has not finished.

65. When adding a new model, what should I compare before switching everything?

Run the same batch of tasks against the same acceptance criteria in a small trial, focusing on format, accuracy, stability and exception handling.

66. How do I tell whether a failure is the model's fault or the workflow's?

Check the input material, task boundaries, output path and acceptance criteria first. If the workflow is unclear, changing models will not fix it.

67. Why is the output area more trustworthy than "done" in the chat?

"Done" is the model's own claim. A file in the output area can be opened and checked, so it is the real delivery.

68. How do I add a "confirm before sending" layer to connector tasks?

Before sending, list the recipient, subject, body, attachments and scope, and require human confirmation before execution.

69. Why can no automation run when the client is offline?

Local files, client capabilities and the workspace live on your computer. If the client is offline, the task has no execution environment.

70. Which three areas should I strengthen first for more stable results?

Input material, output path and acceptance criteria. Clarifying these three improves stability noticeably.

5. Real office workflows

71. Why is Excel → weekly report → deck the recommended first closed loop?

It has clear inputs, standard outputs and simple acceptance, and it covers reading data, summarizing, writing a document and making a deck.

72. How do I turn messy meeting notes into an accountable action list?

Extract the task, owner, due date, source and status. Mark missing items "to confirm" and output a trackable table.

73. Why is "material to web page" a good test of whether WorkBuddy can really deliver?

It requires combining sources, generating a page, handling structure and style, previewing and publishing — a full test of delivery.

74. How do I make a page with WorkBuddy without overcomplicating it?

Define the page structure, content sources and style first, then generate. Keep an editable source file and a preview URL; do not add a complex backend at the start.

75. How do I use a connector to turn Tencent Docs content into a deliverable?

Grant minimum permissions, read the specified document, organize it into the target format, then write back or export a new document for human acceptance.

76. Why is "email summary" a better first loop than "auto-send email"?

A summary is read-only and low-risk. Auto-sending cannot be undone if it goes wrong. Start with summarizing, then add a send confirmation.

77. How do I make batch renaming fast without losing files?

Inspect the files, generate an old-to-new name map, confirm conflicts and exceptions, then rename and keep a rollback list.

78. How do I inspect and archive a messy folder instead of moving everything at once?

List the files and classification rules, produce an archive plan, confirm it, then move files while keeping the original folder or a backup.

79. Which three "I'm away but it must be done now" scenarios suit the remote assistant?

Quick file edits, remote information lookup, and confirming or publishing while away. Sending and deleting still require a second confirmation.

80. Why is "automatically produce the weekly report on Friday" a good first automation?

Its input, output and timing are fixed and its acceptance criteria are clear — ideal for a recurring office workflow.

81. How do I connect Google Calendar and Drive without getting stuck at the start?

Grant minimum permissions, start read-only, validate the connection with a small task, then expand to writes.

82. Why should "I have to rethink the prompt every time" tasks become fixed workflows or Skills?

Repeated rethinking means the process is stable but the expression cost is high. A Skill reduces re-explaining and result variance.

83. What small projects are best for trying no-code local apps?

Small tools such as a budget tracker, checklist, internal lookup or personal dashboard. Inputs and outputs are simple, acceptance is easy and the data risk is low.

84. Where does AI-driven mode help, and where does it lose control?

It helps with exploration, organization and repetition. It loses control around high-risk decisions, external sending, deletion and payments, where human confirmation must remain.

85. When does it really save time, and when does it just replace manual busyness with watching AI?

It saves time on material organization, format conversion and repeated summaries. It only adds stress when requirements are vague, the work keeps redoing itself and acceptance is unclear.

86. How do I choose the next workflow to become a WorkBuddy loop?

Pick a high-frequency workflow with stable inputs, verifiable outputs and a controllable cost of failure. Prove one loop, then copy it to similar workflows.

6. Boundaries, alternatives and future judgement

87. Why should complex backends and production development not be handed over just because it "can also make pages and code"?

Production systems need architecture, permissions, security, monitoring and long-term maintenance. Generating pages and code is not the same as carrying production responsibility.

88. Which tasks should stay in WorkBuddy, and which should move to a more specialized approach?

Office documents, organization, summaries and lightweight pages can stay. Production systems, core code, security-sensitive work and high-availability services need a specialized team and tools.

89. When is manual handling actually more direct and stable?

One-off tasks, judgement-heavy tasks, high-risk tasks and tasks that need immediate feedback are often faster by hand.

90. Why shouldn't every document and folder be handed to WorkBuddy at once?

Sensitive data, overly broad folders and unrelated files expand the risk surface. Give only the material and scope the task needs.

91. Why does boundary management matter more as connectors and platforms multiply?

More integrations mean more systems it can read and write, and more complex permissions, data flows and accidental-action risk. An unclear boundary multiplies the incident surface.

92. When local workspace, remote assistant and platform integrations combine, what management cost really grows?

Permission management, data flow, version consistency, responsibility and exception handling all grow — not just the number of entry points.

93. As models multiply, how do I avoid treating "change the model" as the only solution?

Fix the task structure, input material, output path and acceptance criteria first. The model is one layer; changing it will not fix an unclear workflow.

94. As skills, MCP and connectors expand, which authorization boundary should ordinary users protect first?

Least privilege: only the necessary folders and data, read-only first, and confirmation for sending and writing.

95. Why is the gap usually task structure and acceptance, not the prompt?

A prompt affects one expression. Task structure and acceptance decide whether the result can be reproduced and delivered.

96. When does WorkBuddy move from a one-off experiment to a fixed workbench?

When you have a stable workflow, fixed output and clear acceptance, and it consistently saves time.

97. Should I hand external sending, formal publishing and direct file changes to it?

Let it prepare drafts, check content and generate files. Irreversible actions such as sending, publishing and overwriting originals require human confirmation.

98. Why does a long feature list not mean I should use everything from day one?

More features mean more learning and selection cost. Protect the core workflow first and add skills, connectors and automation as needed.

99. If I only learn part of it first, which basics should I protect?

Workspace, Ask/Plan/Craft, output paths, acceptance criteria and least-privilege authorization. These five are the foundation for everything else.

100. What misuse and misjudgement should these 100 questions help me avoid?

Treating it as a chat box, making tasks too broad, skipping acceptance, over-authorizing, automating before the manual run works, and mistaking a finished file for a correct conclusion.

References

WorkBuddy documentation · Tencent Cloud Developer Community (cloud.tencent.com) · Tencent Cloud English technical site (tencentcloud.com) · Community reviews and integration docs