Once a profile is compiled, the work is often not over. Meeting invitation letters need to be written, expert recommendation forms filled out, collaboration intent emails sent, and review expert lists submitted. What these materials have in common is that the content is already there—it is just scattered across different fields in the records and has to be pieced together one document at a time. The real time sink is this stretch: not looking up information, but putting the information you found back into one file after another.
The talent profile is responsible for organizing public information into verifiable fields, while the MuchSci office assistant is responsible for putting those fields into deliverable documents. This article breaks down the seam between the two: how fields map to templates, how a batch of names becomes a finished draft, and how finished drafts are archived.
01 What the second half fills in
▲ Figure 1 | The first stage manages where fields come from; the second manages how documents are produced
The profile provides fields. Basic information, education, employment history, and main research achievements each form their own columns; every record carries a data source label, and anything that does not match is marked as pending verification and not counted toward experts. Its strengths are being searchable, comparable, and verifiable, but it is not itself a document that can be sent out directly.
The office assistant adds form. In the MuchSci office assistant interface, you first select the deliverable type—presentation, spreadsheet ledger, long-form document, or PDF-specific document—then confirm the four elements of topic, length, format, and style, and then generate the file. For the profile, this step solves “in what form the fields land on paper.”
Keeping the two separate actually makes things clearer. In MuchSci, the talent profile and the office assistant are designed as two independent entry points: one manages where information comes from, and the other manages how documents are produced. Once the division of labor is clear, when a problem appears you know which stage to go back to: if the fields are wrong, go back to the records; if the layout is wrong, go back to the template.
02 Where each of the four field types should go
▲ Figure 2 | The blanks in a set of templates correspond exactly to the four field types in the profile
Basic information fields land in the header and salutation. A one-line bio, current position, and research areas usually go into the opening of a letter or the first paragraph of an expert introduction; fields such as gender, date of birth, and place of origin appear depending on what the form requires.
The latter three types are better moved into tables as whole blocks. Education is already four columns—time, school/institution, major, and outcome—so it can be moved into a table as is; employment history is three columns—time, organization, and position—and is used for notes in recommendation forms and lists; main research achievements are split into academic contributions and led projects, and are used in introduction and explanatory paragraphs, with projects involving national-level major engineering works specifically marked.
Only when the mapping is clear does batching make sense. Fields have to match their positions for the blanks to be filled; conversely, if the template requires a field that the profile does not have, that is a missing item and should be explicitly left blank rather than padded with something else.
03 Copying one by hand vs. generating a batch from a template
▲ Figure 3 | What is saved is the repetitive assembly work, not the verification step
The most obvious difference is consistency. When fields are copied by hand into ten letters, formatting distortion, omissions, and misalignment are all easy to occur; when the same template is applied to multiple documents, the format is consistent, and any missing item can be spotted at a glance.
Next is the cost of revision. If a salutation or date needs to be changed temporarily, the manual approach means changing ten places, while the template approach means changing it once. This becomes more obvious the longer the list is.
But it must be stated clearly: batching does not mean exemption from checks. A template can fill in the blanks, but whether the fields filled in are correct still has to be checked by a person. This is especially true for key information such as dates, organizations, and project names; after generation, opening it and flipping through a couple of pages is much easier than having someone point out the error later.
04 Feature interface: from fields to documents
▲ Figure 4 | Select fields, choose type, apply template, manually accept—four steps complete an assembly line
The process has four steps: select the fields to use from the records; first determine the deliverable type—letter, table, presentation, or long-form document; apply the template and generate a batch at once; open it for manual acceptance to check whether the salutation, organization, and dates are correct. The first three steps are preparation and execution; the fourth is the checkpoint.
▲ Figure 5 | Office assistant interface: four deliverable types, two generation methods, and file output after confirming the four elements
On the interface, the four elements are listed separately: topic, length, format, and style. This step is not superfluous; it is the basis for later acceptance—topic determines what to write, length determines how large it should be, format determines what file to deliver, and style determines the tone. There are also two generation methods: when there are no ready materials, use topic-only generation; when materials have piled up, use material-driven generation. Uploading content exported from the profile as material belongs to the latter.
The archiving step is also worth noting: after a batch of letters is generated, it can also be consolidated into a spreadsheet ledger for later registration and retrieval.
05 Boundaries
① This article discusses functional mechanisms and application methods and does not constitute a performance guarantee. Whether the structure, format, and field filling of generated documents meet requirements depends on the template description and the quality of the source materials; the process running to completion does not mean the result is defect-free.
② Structured does not equal assessment. Records are an organization of public information and do not constitute an evaluation of anyone’s abilities, a recommendation, or an endorsement; judgments before materials are sent must be independently verified and are the responsibility of the user.
③ Only public information is used. No personal privacy data is involved; for formal purposes such as review, recruitment, and due diligence, the current regulations and procedures of the relevant organizations shall prevail.