Guide
Nebula Agent: 7 valuable use cases for document work
Discover the most valuable Nebula Agent use cases for researching, analyzing, drafting, editing, and turning project files into finished documents.
The short version
Nebula Agent is most valuable when a task crosses the boundary between asking a question and finishing a real document. It works beside your project files, uses the context you choose, and can research, analyze, draft, edit, or create deliverables across Word documents, spreadsheets, presentations, Markdown, LaTeX, PDFs, and other project sources.
The best Nebula Agent use cases are not isolated requests for more text. They are complete, reviewable workflows: compare a report with its source data, turn a workbook into a decision brief, adapt a paper into a presentation, audit a document before editing it, or trace claims back to the files that support them.
This guide covers seven workflows where that combination of context, action, and review creates the most value.
What is Nebula Agent?
Nebula Agent is the AI agent built into Nebula Writer, a local-first workspace for serious document work. Unlike a standalone chatbot, it sits next to the files in your project. The active document is available as context, and you can add selected text, specific files, folders, PDFs, or Office documents to a request.
That gives the agent three practical advantages:
- Project context: It can work from the source material you name instead of relying on a pasted excerpt.
- Document actions: It can create or edit supported files, not just describe what you should change.
- Reviewable results: You can inspect the affected document, review pending changes, and use action or version history when you need to recover.
The result is an AI document agent designed for the work between a rough source and a finished deliverable.
1. Synthesize information across project files
One of the highest-value uses for Nebula Agent is combining evidence spread across several files. A product decision may depend on interview notes, a PDF research report, a spreadsheet of results, and an earlier Word brief. Reading each source separately is manageable; finding where they agree, conflict, or leave gaps is the harder part.
Give the agent only the relevant sources, identify which one is authoritative for each kind of information, and ask for a structured analysis before requesting edits.
Compare @research/interviews.pdf, @data/pilot-results.xlsx, and @drafts/launch-brief.docx. Treat the workbook as the source of truth for metrics. List the strongest supported findings, contradictions, and missing evidence. Cite the source file, page, sheet, or cell range for each finding. Do not edit any files.
This pattern is useful for literature reviews, due-diligence summaries, customer research, policy analysis, and any project where the answer lives between documents rather than inside one of them.
2. Audit a document before changing it
AI editing is safer when diagnosis and revision are separate steps. Nebula Agent can first inspect a document for problems—unsupported claims, repeated sections, inconsistent headings, weak transitions, spreadsheet errors, or crowded slides—without changing the file.
Audit @client-report.docx for claims that are not supported by @sources/findings.xlsx, repeated conclusions, inconsistent heading styles, and tables that are difficult to scan. Report issues by section and page. Do not edit the document.
After reviewing the audit, authorize one bounded change:
Revise only the Executive Summary using the approved findings. Preserve every number, table, header, footer, and heading style. Show the changes for review.
This two-step workflow is especially valuable for unfamiliar, high-stakes, or inherited documents. You see the agent's interpretation before it acts, and the final scope stays small enough to verify.
3. Draft and revise with source material in view
Nebula Agent can turn notes and evidence into a new section, memo, report, or chapter while keeping the supporting material close at hand. The key is to define the audience and the source of truth rather than asking it simply to “make this better.”
For example:
Use @notes/research-summary.md and @sources/market-study.pdf to draft the Evidence section of this document for a non-technical leadership audience. Keep it under 600 words, distinguish findings from recommendations, and flag any claim that needs a source. Preserve the rest of the document.
You can also select a narrow passage and ask the agent to improve clarity, change tone, shorten it, or align it with the terminology used elsewhere in the project. Because the draft and its references remain in the same workspace, checking the result is faster than moving between a chatbot, browser tabs, and separate editors.
4. Turn spreadsheets into decisions, charts, and reports
A spreadsheet often contains the right numbers but not the explanation a reader needs. Nebula Agent can inspect workbook structure, help define a calculation, create a reproducible analysis, generate a chart, and use the verified result in a report or presentation.
Start by confirming definitions:
Inspect @operations.xlsx and identify the sheets and columns used for on-time delivery, cancellations, and region. Report missing values, formula warnings, and the calculation you recommend. Do not edit or calculate yet.
Then state the rule and output explicitly:
Treat an order as on time when Delivered Date is on or before Promised Date, and exclude cancelled orders. Create analysis/on-time-delivery.csv and a 1600×900 trend chart. Save the calculation code under analysis/ and leave the workbook unchanged.
Once the numbers are verified, the same project can produce an editable DOCX brief or PPTX update. This is more useful than a chat-only summary because the source, method, chart, and final narrative can all remain available for review.
5. Transform one document format into another
Important work rarely ends in the format where it began. A LaTeX paper becomes a conference talk. A quarterly workbook becomes an executive report. A working presentation becomes a durable decision memo.
Nebula Agent can use one or more existing files to create a new deliverable while leaving the sources intact:
Create exports/research-talk.pptx from @paper.tex, @references.bib, and the compiled paper. Make six slides for an applied-ML audience: question, prior limitation, method, strongest result, limitations, and next step. Add concise speaker notes with source sections and citation keys. Do not change the LaTeX project.
The most reliable transformation requests specify the audience, output path, structure, facts to preserve, and source files that must remain unchanged. The result should then be inspected in its native view—page by page, slide by slide, or sheet by sheet.
6. Research with a deliverable in mind
Research becomes more valuable when it feeds a concrete output. Nebula Agent can help organize project sources, identify evidence gaps, develop an outline, and—when the available account tools support it—use web or academic search to find additional material.
Instead of asking for a broad topic summary, connect research to the document you need:
Review the current literature notes and identify the three claims in @drafts/proposal.docx that need stronger evidence. Propose an academic search plan for each claim. Return candidate sources with title, author, year, DOI or stable URL, and the claim each source may support. Do not edit the proposal.
Always open the original source and verify authors, dates, quotations, findings, and citation details before using them. The agent can make discovery and organization faster, but source verification remains part of the job.
7. Repair complex documents without losing the bigger picture
Structured documents fail in ways that ordinary prose does not. LaTeX can stop compiling, spreadsheet formulas can point to the wrong range, and a slide can overflow after a small text change. Nebula Agent can inspect the error in context, propose the smallest fix, apply it, and report whether the problem was resolved.
For a LaTeX project, try:
Explain the first actionable compile diagnostic in @paper.tex and propose the smallest safe fix. Preserve labels, citation keys, commands, and document structure. Do not edit the source until I approve the plan.
The same principle applies to spreadsheet warnings and presentation layout audits: diagnose first, preserve surrounding structure, and verify the rendered or calculated result after the change. This is where an agent inside the document workspace has a practical advantage over an answer that ends in chat.
How to get better results from Nebula Agent
Strong requests identify the source, output, audience, and constraints. A compact template is:
Use [source files] as the source of truth. Create or change [specific output] for [audience]. Preserve [facts, formatting, formulas, or files]. Save the result to [path]. Report uncertainties and show changes for review.
For consequential work, add three safeguards:
- Ask for analysis or a plan before permitting edits.
- Create a new output file instead of overwriting the source.
- Work in one section, sheet, or slide batch at a time.
More context is not automatically better. Remove irrelevant attachments, name the authoritative source, and state how disagreements between files should be handled.
Review every result
Nebula Agent is designed to keep the writer in control, but review is still essential. Before relying on an output:
- Compare material claims and numbers with the named sources.
- Check formulas, filters, units, and calculated values.
- Inspect page breaks, tables, charts, slide overflow, and visual hierarchy.
- Verify quotations, citations, links, and bibliographic details yourself.
- Accept or discard pending changes deliberately, then save and reopen the file.
For legal, medical, financial, regulated, or otherwise high-stakes work, use the appropriate expert review. An inspectable workflow improves the handoff; it does not replace professional judgment.
When should you use Nebula Agent?
Use Nebula Agent when the task depends on project context, spans multiple steps or formats, or should produce a file you can review. A direct editor command may be faster for a tiny mechanical change, and a simple question may not need a multi-step plan.
The clearest signal is this: if completing the work means reading sources, making decisions, changing a document, and checking the result, Nebula Agent can keep that entire workflow together.