Raw File System Analysis (FAT32 File Recovery)

This post isn’t about upcoming features, it’s about things you can already do with Profiler. What we’ll see is how to import structures used for file system analysis from C/C++ sources, use them to analyze raw hex data, create a script to do the layout work for us in the future and at the end we’ll see how to create a little utility to recover deleted files. The file system used for this demonstration is FAT32, which is simple enough to avoid making the post too long.

Note: Before starting you might want to update. The 1.0.1 version is out and contains few small fixes. Among them the ‘signed char’ type wasn’t recognized by the CFFStruct internal engine and the FAT32 structures I imported do use it. While ‘signed char’ may seem redundant, it does make sense, since C compilers can be instructed to treat char types as unsigned.

Import file system structures

Importing file system structures from C/C++ sources is easy thanks to the Header Manager tool. In fact, it took me less than 30 minutes to import the structures for the most common file systems from different code bases. Click here to download the archive with all the headers.

Here’s the list of headers I have created:

  • ext – ext2/3/4 imported from FreeBSD
  • ext2 – imported from Linux
  • ext3 – imported from Linux
  • ext4 – imported from Linux
  • fat – imported from FreeBSD
  • hfs – imported from Darwin
  • iso9660 – imported from FreeBSD
  • ntfs – imported from Linux
  • reiserfs – imported from Linux
  • squashfs – imported from Linux
  • udf – imported from FreeBSD

Copy the files to your user headers directory (e.g. “AppData\Roaming\CProfiler\headers”). It’s better to not put them in a sub-directory. Please note that apart from the FAT structures, none of the others have been tried out.

Note: Headers created from Linux sources contain many additional structures, this is due to the includes in the parsed source code. This is a bit ugly: in the future it would be a good idea to add an option to import only structures belonging to files in a certain path hierarchy and those referenced by them.

Since this post is about FAT, we’ll see how to import the structures for this particular file system. But the same steps apply for other file systems as well and not only for them. If you’ve never imported structures before, you might want to take a look at this previous post about dissecting an ELF and read the documentation about C++ types.

We open the Header Manager and configure some basic options like ‘OS’, ‘Language’ and ‘Standard’. In this particular case I imported the structures from FreeBSD, so I just set ‘freebsd’, ‘c’ and ‘c11’. Then we need to add the header paths, which in my case were the following:

Then in the import edit we insert the following code:

Now we can click on ‘Import’.

Import FAT structures

That’s it! We now have all the FAT structures we need in the ‘fat’ header file.

It should also be mentioned that I modified some fields of the direntry structure from the Header Manager, because they were declared as byte arrays, but should actually be shown as short and int values.

Parse the Master Boot Record

Before going on with the FAT analysis, we need to briefly talk about the MBR. FAT partitions are usually found in a larger container, like a partitioned device.

To perform my tests I created a virtual hard-disk in Windows 7 and formatted it with FAT32.


As you might be able to spot, the VHD file begins with a MBR. In order to locate the partitions it is necessary to parse the MBR first. The format of the MBR is very simple and you can look it up on Wikipedia. In this case we’re only interested in the start and size of each partition.

Profiler doesn’t yet support the MBR format, although it might be added in the future. In any case, it’s easy to add the missing feature: I wrote a small hook which parses the MBR and adds the partitions as embedded objects.

Here’s the cfg data:

And here’s the Python script:

And now we can inspect the partitions directly (do not forget to enable the hook from the extensions).

VHD Partitions


Analyze raw file system data

The basics of the FAT format are quite simple to describe. The data begins with the boot sector header and some additional fields for FAT32 over FAT16 and for FAT16 over FAT12. We’re only interested in FAT32, so to simplify the description I will only describe this particular variant. The boot sector header specifies essential information such as sector size, sectors in clusters, number of FATs, size of FAT etc. It also specifies the number of reserved sectors. These reserved sectors start with the boot sector and where they end the FAT begins.

The ‘FAT’ in this case is not just the name of the file system, but the File Allocation Table itself. The size of the FAT, as already mentioned, is specified in the boot sector header. Usually, for data-loss prevention, more than one FAT is present. Normally there are two FATs: the number is specified in the boot sector header. The backup FAT follows the first one and has the same size. The data after the FAT(s) and right until the end of the partition includes directory entries and file data. The cluster right after the FAT(s) usually starts with the Root Directory entry, but even this is specified in the boot sector header.

The FAT itself is just an array of 32-bit indexes pointing to clusters. The first 2 indexes are special: they specify the range of EOF values for indexes. It works like this: a directory entry for a file (directories and files share the same structure) specifies the first cluster of said file, if the file is bigger than one cluster, the FAT is looked up at the index representing the current cluster, this index specifies the next cluster belonging to the file. If the index contains one of the values in the EOF range, the file has no more clusters or perhaps contains a damaged cluster (0xFFFFFFF7). Indexes with a value of zero are marked as free. Cluster index are 2-based: cluster 2 is actually cluster 0 in the data region. This means that if the Root Directory is specified to be located at cluster 2, it is located right after the FATs.

Hence, the size of the FAT depends on the size of the partition, and it must be big enough to accommodate an array large enough to represent every cluster in the data area.

So, let’s perform our raw analysis by adding the boot sector header and the additional FAT32 fields:

Add struct

Note: When adding a structure make sure that it’s packed to 1, otherwise field alignment will be wrong.

Boot sector

Then we highlight the FATs.


And the Root Directory entry.

Root Directory

This last step was just for demonstration, as we’re currently not interested in the Root Directory. Anyway, now we have a basic layout of the FAT to inspect and this is useful.

Let’s now make our analysis applicable to future cases.

Automatically create an analysis layout

Manually analyzing a file is very useful and it’s the first step everyone of us has to do when studying an unfamiliar file format. However, chances are that we have to analyze files with the same format in the future.

That’s why we could write a small Python script to create the analysis layout for us. We’ve already seen how to do this in the post about dissecting an ELF.

Here’s the code:

We can create an action with this code or just run it on the fly with Ctrl+Alt+R.

Recover deleted files

Now that we know where the FAT is located and where the data region begins, we can try to recover deleted files. There’s more than one possible approach to this task (more on that later). What I chose to do is to scan the entire data region for file directory entries and to perform integrity checks on them, in order to establish that they really are what they seem to be.

Let’s take a look at the original direntry structure:

Every directory entry has to be aligned to 0x20. If the file has been deleted the first byte of the deName field will be set to SLOT_DELETED (0xE5). That’s the first thing to check. The directory name should also not contain certain values like 0x00. According to Wikipedia, the following values aren’t allowed:

  • ” * / : < > ? \ |
    Windows/MS-DOS has no shell escape character
  • + , . ; = [ ]
    They are allowed in long file names only.
  • Lower case letters a–z
    Stored as A–Z. Allowed in long file names.
  • Control characters 0–31
  • Value 127 (DEL)

We can use these rules to validate the short file name. Moreover, certain directory entries are used only to store long file names:

We can exclude these entries by making sure that the deAttributes/weAttributes isn’t ATTR_WIN95 (0xF).

Once we have confirmed the integrity of the file name and made sure it’s not a long file name entry, we can validate the deAttributes. It should definitely not contain the flags ATTR_DIRECTORY (0x10) and ATTR_VOLUME (8).

Finally we can make sure that deFileSize isn’t 0 and that deHighClust combined with deStartCluster contains a valid cluster index.

It’s easier to write the code than to talk about it. Here’s a small snippet which looks for deleted files and prints them to the output view:

This script is to be run on the fly with Ctrl+Alt+R. It’s not complete, otherwise I would have added a wait box, since like it’s now the script just blocks the UI for the entire execution. We’ll see later how to put everything together in a meaningful way.

The output of the script is the following:

We can see many false positives in the list. The results would be cleaner if we allowed only ascii characters in the name, but this wouldn’t be correct, because short names do allow values above 127. We could make this an extra option, generally speaking it’s probably better to have some false positives than missing valid entries. Among the false positives we can spot four real entries. What I did on the test disk was to copy many files from the System32 directory of Windows and then to delete four of them, exactly those four found by the script.

The next step is recovering the content of the deleted files. The theory here is that we retrieve the first cluster of the file from the directory entry and then use the FAT to retrieve more entries until the file size is satisfied. The cluster indexes in the FAT won’t contain the next cluster value and will be set to 0. We look for adjacent 0 indexes to find free clusters which may have belonged to the file. Another approach would be to dump the entire file size starting from the first cluster, but that approach is worse, because it doesn’t tolerate even a little bit of fragmentation in the FAT. Of course, heavy fragmentation drastically reduces the chances of a successful recovery.

However, there’s a gotcha which I wasn’t aware of and it wasn’t mentioned in my references. Let’s take a look at the deleted directory entry of ‘notepad.exe’.

Notepad directory entry

In FAT32 the index of the first cluster is obtained by combining the high-word deHighClust with the low-word deStartCluster in order to obtain a 32-bit index.

The problem is that the high-word has been zeroed. The actual value should be 0x0013. Seems this behavior is common on Microsoft operating systems as mentioned in this thread on Forensic Focus.

This means that only files with a cluster index equal or lower than 0xFFFF will be correctly pointed at. This makes another approach for FAT32 file recovery more appealing: instead of looking for deleted directly entries, one could directly look for cluster indexes with a value of 0 in the FAT and recognize the start of a file by matching signatures. Profiler offers an API to identify file signatures (although limited to the file formats it supports), so we could easily implement this logic. Another advantage of this approach is that it doesn’t require a deleted file directory entry to work, increasing the possibility to recover deleted files. However, even that approach has certain disadvantages:

  1. Files which have no signature (like text files) or are not identified won’t be recovered.
  2. The name of the files won’t be recovered at all, unless they contain it themselves, but that’s unlikely.

Disadvantages notwithstanding I think that if one had to choose between the two approaches the second one holds higher chances of success. So why then did I opt to do otherwise? Because I thought it would be nice to recover file names, even though only partially and delve a bit more in the format of FAT32. The blunt approach could be generalized more and requires less FAT knowledge.

However, the surely best approach is to combine both systems in order to maximize chances of recovery at the cost of duplicates. But this is just a demonstration, so let’s keep it relatively simple and let’s go back to the problem at hand: the incomplete start cluster index.

Recovering files only from lower parts of the disk isn’t really good enough. We could try to recover the high-word of the index from adjacent directory entries of existing files. For instance, let’s take a look at the deleted directory entry:

Deleted entry

As you can see, the directory entry above the deleted one represents a valid file entry and contains an intact high-word we could use to repair our index. Please remember that this technique is just something I came up with and offers no guarantee whatsoever. In fact, it only works under certain conditions:

  1. The cluster containing the deleted entry must also contain a valid file directory entry.
  2. The FAT can’t be heavily fragmented, otherwise the retrieved high-word might not be correct.

Still I think it’s interesting and while it might not always be successful in automatic mode, it can be helpful when trying a manual recovery.

This is how the code to recover partial cluster indexes might look like:

It tries to find a valid file directory entry before and after the deleted entry, remaining in the same cluster. Now we can write a small function to recover the file content.

All the pieces are there, it’s time to bring them together.

Create a recovery tool

With the recently introduced logic provider extensions, it’s possible to create every kind of easy-to-use custom utility. Until now we have seen useful pieces of code, but using them as provided is neither user-friendly nor practical. Wrapping them up in a nice graphical utility is much better.

Home view

What follows is the source code or at least part of it: I have omitted those parts which haven’t significantly changed. You can download the full source code from here.

Here’s the cfg entry:

And the Python code:

When the tool is activated it will ask for the disk file to be selected, then it will show an options dialog.


In our case we can select the option ‘Ascii only names’ to exclude false positives.

The options dialog asks for a directory to save the recovered files. In the future it will be possible to save volatile files in the temporary directory created for the report, but since it’s not yet possible, it’s the responsibility of the user to delete the recovered files if he wants to.

The end results of the recovery operation:


All four deleted files have been successfully recovered.

Three executables are marked as risky because intrinsic risk is enabled and only ‘ntoskrnl.exe’ contains a valid digital certificate.


I’d like to remind you that this utility hasn’t been tested on disks other than on the one I’ve created for the post and, as already mentioned, it doesn’t even implement the best method to recover files from a FAT32, which is to use a signature based approach. It’s possible that in the future we’ll improve the script and include it in an update.

The purpose of this post was to show some of the many things which can be done with Profiler. I used only Profiler for the entire job: from analysis to code development (I even wrote the entire Python code with it). And finally to demonstrate how a utility with commercial value like the one presented could be written in under 300 lines of Python code (counting comments and new-lines).

The advantages of using the Profiler SDK are many. Among them:

  • It hugely simplifies the analysis of files. In fact, I used only two external Python functions: one to check the existence of a directory and one to normalize the path string.
  • It helps building a fast robust product.
  • It offers a graphical analysis experience to the user with none or little effort.
  • It gives the user the benefit of all the other features and extension offered by Profiler.

To better explain what is meant by the last point, let’s take the current example. Thanks to the huge amount of formats supported by Profiler, it will be easy for the user to validate the recovered files.

Validate recovered files

In the case of Portable Executables it’s extremely easy because of the presence of digital certificates, checksums and data structures. But even with other files it’s easy, because Profiler may detect errors in the format or unused ranges.

I hope you enjoyed this post!

P.S. You can download the complete source code and related files from here.


  1. File System Forensic Analysis – Brian Carrier
  2. Understanding FAT32 Filesystems – Paul Stoffregen
  3. Official documentation – Microsoft
  4. File Allocation Table – Wikipedia
  5. Master boot record – Wikipedia

Dissecting an ELF with C++ Types

While there are more interesting targets which could be manually analyzed with the new features provided in the Profiler, I decided to write a small post about ELF, also because official support for ELF will be added sooner or later.

Let’s start by importing the types contained in ‘elf.h’. You’ll probably find this header in ‘/usr/include’. Everything we’re interested in is in this file, so we can avoid importing other stuff. I added some predefines in order to avoid includes:

Then I pasted ‘elf.h’ into the Header Manager after the HEADER_START directive and clicked on ‘Import’.

ELF types import

We now have a header (elf) with all the types we need to start the manual analysis.

Since this is just a demonstration I didn’t do a full analysis of the ELF format. I limited the scope to finding the imported symbols and their strings.

ELF analysis

Every ELF starts with a _Elf64_Ehdr header (Elf32_Ehdr for 32-bit files, in this case it’s a 64-bit ELF). The header specifies the offset, number and size of the sections (we’ll just assume the standard 0x40 size here). The ‘name’ field of sections is just an index into a ‘SHT_STRTAB’ section whose index is specified by the header. The contents of a section are specified by its type, so finding the symbol table is pretty straight-forward. In this ELF we have a SHT_DYNSYM section. This section is just an array of _Elf64_Sym structures. Again, their ‘st_name’ field is just an index into another SHT_STRTAB section (the interval in the screenshot named ‘.dynstr’).

As already mentioned in the previous post, we can create a layout programmatically as well:

Moreover, the imported types can be used to do other operations not related to layouts. For instance let’s write few lines of code to print out the symbol names for this ELF:

The output will be:

Rememebr that the advantages of using CFFStructs rely not only in their dynamism or easiness in displaying them graphically, but also security. Contrary to a structure pointer in C, there’s no risk of crash when accessing members in a CFFStruct.

Today some final tests will be performed on the new version and if everything goes well, it will be released tomorrow or the day after. So stay tuned!

C++ Types: Under the Hood

In this post we’re going to explore the SDK part of the Profiler associated to imported structures and also all the C++ internals connected to the layout creation of structures/classes.

At first I thought about subdividing the material into several posts, but at the end it’s probably better to have it all together for future reference.


In the SDK a Layout is the class to be used when we need to create a graphical analysis of raw data. While we can create and handle headers from the UI, it is also possible to do it programmatically.

Creating a layout is straightforward:

The data can be associated to a structure (or array of structures) as well. Please remember that the name of a header is always relative to header sub-directory of the user directory. Saving the layout is not necessary: it’s automatically saved in the project.

Attaching a layout to a hex view is also very easy:

Of course, layouts can be used for operations not related to graphical analysis as well.


Headers are part of the CFF Core and as such the naming convention of the CFFHeader class isn’t camel-case.

A CFFHeader represents an abstract database in which structures/classes and other things are stored. While we won’t use most of its methods, some of them are very useful for common operations.

Let’s say we want to retrieve a specific structure from a header and use it.

The output of this snippet is:

We can specify the following options when retrieving a structure:

These are the same options which are available from the UI when adding a structure to a layout.

When options are not specified, they default to the default structure options of the object. It’s possible to specify the default structure options with this method:

We’ll see later the implications of the various flags.

When I said that a CFFHeader represents an abstract database, I meant that it is not really bound to a specific format internally. All it cares about is that data is retrieved or set. The standard format used by headers is SQLite and you’ll need to use that format when creating layouts associated to structures. However, when using structures from Python it can be handy to avoid an associated header file. When the number of structures is very limited and you don’t need write or other complex operations, structures can be stored into an XML string. In fact, the internal format of structures is XML. Let’s take a look at one:

We can inspect the format of a structure stored in a header from the Header Manager in the Explore tab by double clicking on it. But we can also avoid creating a header altogether and output the schema of parsed structures directly when importing them from C++. Just check ‘Test mode’ and as ‘Output’ select ‘schemas’.

Output schemas

Let’s import a simple structure such as:

The output will be:

To use this structure from Python we can write the following code:

As you can see it’s very simple. I’ll use this method for the examples in the rest of the post, because they’re just examples and there’s no point in creating a header file for them.


As a rule of thumb if a structure contains a pointer (or a vtable pointer) it is always a good idea to specify the desired size. When the size is omitted both in the explicit options and in the default structure options, the size will be set to the default pointer size of an object, which apart for PEObjects and MachObjects will always be 32bits.


When endianness is not specified it will be set to the default of the object. While internally it’s already possible to have individual fields with different endianness, an extra XML field attribute to specify it will be added in the future.


The first thing to say is that there’s a difference between an array of top level structures and an array of fields. Creating a top level array of structures is easy:

The support of arrays is somewhat limited. Multidimensional arrays are only partially supported, in the sense that they will be converted to a single dimension. For instance:

Or in XML:

Will be convrted to:

Also notice that to access an array element in a CFFStruct the syntax to use is not “a[15]” but “a.15”, e.g.:


The only thing to mention about Sub-structures is that complex sub-types are always dumped separately, e.g.:


In Python:

The output:

Being a separate type, we can also use ‘A::Sub’ without its parent.

A new thing we’ve just seen is the presence of multiple structures in a single XML header. I’ve pasted the whole Python code once again just for clarity, in the next examples I won’t repeat it, since the Python code never changes, only the header string does.


Unions just like sub-structures are fully supported. The only thing to keep in mind is that when we have a top level union, meaning not contained in another structure, such as:

Then to access its members it is necessary to add a ‘u.’ prefix. The reason for this is that CFFStructs support unions only as members, so the union above will result in a CFFStruct with a union member called ‘u’.

Anonymous types

Anonymous types are only partially supported in the sense that they are given a name when imported. A type such as the following:

Results in the following xml:

As you can see a ‘_Type_’ + number naming convention has been used to rename anonymous types. The first character (‘_’) in the name represents the default anonymous prefix. This prefix is customizable. If a typedef is found for an anonymous type, then the new name for that type will created by using the anonymous prefix + the typedef name.


Bit-fields are fully supported.


The unnamed field at the end represents the unused bits given the field size, in this case we have an ‘int’ type and we’ve used only 5 bits of it.

There are significant differences in how compilers handle bit-fields. Visual C++ behaves differently than GCC/Clang. Some of the differences are summarized in this message by Richard W.M. Jones.

Another important difference I noticed is how bit fields are coalesced when the type changes, e.g.:

Without going now into how they are coalesced, the thing to remember is that the Profiler handles all these cases, but you need to specify the compiler to obtain the correct result.


Namespaces are fully supported.

Results in:

Moreover, just as in C++ we can use namespaces to encapsulate #include directives.

This will cause all the types declared in ‘Something’ to be prefixed by the namespace (‘N::’). This can be very handy when we want to include types with the same name into the same header file.


Inheritance is fully supported.



Same with multiple inheritance:



The presence of virtual table pointers in structures which require them is fully supported. Let’s take for instance:



Let’s see an example with multiple inheritance:


When virtual tables are involved it is very important to specify the compiler, because things can vary a great deal between VC++ and GCC/Clang.

Virtual Inheritance

Virtual inheritance is fully supported. Virtual inheritance is a C++ feature to be used in scenarios which involve multiple inheritance with a common base class.

Let’s take the complex case of:

Output (Visual C++):

Output (GCC):

As you can see the layout differs from Visual C++ to GCC. Another thing to notice is that members of virtual base classes are appended at the end. There’s a very good presentation by Igor Skochinsky on C++ decompilation you can watch for more information.

Field alignment

Field alignment is an important factor. Structures which are not subject to packing constraints are aligned up to their biggest native member. It’s more complex than this, because sub-structures influence parent structures but not vice versa. Suffice it to say that there are some internal gotchas, but the Profiler should handle all cases correctly.


When a packing constraint is applied, fields are aligned to either the field size or the packing whichever is less. A packing constraint of 1 is essential if we want to read raw data without any kind of padding between fields. For instance, PE structures in WinNT.h are all pragma packed to 1, so we must specify the same packing when using them.


And for the end a little treat: C++ templates. Let’s take for instance:


We can specify template parameters following the C++ syntax:


So, even nested templates are supported. 😉

C++ Types: Introduction

As announced previously, the upcoming 0.9.7 version of the Profiler represents a milestone in the development road map. We’re excited to present to you an awesome set of new features. In fact, the ground to cover is so vast that one post is not nearly enough. Throughout this week I’ll write some posts to cover the basics and this will allow for enough time to beta test the new version before reaching a release candidate.

Let’s start with an awesome image:


Does it look like a Clang based tool to parse C++ sources and extract type information? If yes, then that’s exactly it!

To sum it up very briefly, the Profiler is now able to extract C++ types such as classes and structures and use these types both in the UI and in Python.

Add structure dialog

Of course, there’s much more to it. The layout of C++ types is a complex matter and doesn’t just involve supporting simple data structures. This post is just an introduction, the next ones will focus on topics such as: endianness, pointers, arrays, sub-structures, unions, bit-fields, inheritance, virtual tables, virtual inheritance, anonymous types, alignment, packing and templates. Yes, you read correctly: templates. 🙂

And apart from the implications of C++ types themselves, there’s the SDK part of the Profiler which will also require some dedicated posts. In this introduction I’m going to show a very simple flow and one of the many possible use cases.

You probably have noticed that the code in the screenshot above belongs to WinNT.h. Let’s see how to import the types in this header quickly. Usually we could parse all the headers of a framework with a few clicks, but while Clang is ideal to parse both Linux and OS X sources, it has difficulty with some Visual C++ extensions which are completely invalid C++ code. So rather than importing the whole Windows SDK we just limit ourselves to a part of WinNT.h.

I have added some predefines for Windows types (we could also include WinDef.h):

Then I just copied the header into the import tool. Usually this isn’t necessary, because we can set up the include directories from the UI and then just use #include directives, but since we need to modify the header to remove invalid C++ extensions, it makes sense to paste it.

The beginning of the code:

Did you notice the HEADER_START macro?

This tells our parser that the C++ types following this directive will be dumped into the header “WinNT.cphdr”. This file is relative to the header directory, a sub-directory of the user data directory. A HEADER_END directive does also exist, it equals to invoking the start directive with an empty string. To give you a better idea how these directives work take a look at this snippet:

If you specify the “#” string in the start directive, the types which follow will be dumped to the ‘this’ header. This is a special header which lives in the current project, so that you can pass the Profiler project to a colleague and it will already contain the necessary types without having to send extra files.

Back to the importing process, we click on ‘Import’ and that’s it. If Clang encounters C++ errors, we can fix them thanks to the diagnostic information:

Diagnostic information

We can explore the created header file from the ‘Explore’ tab.

Explore header

Now let’s use the header to analyze a PE file inside of a Zip archive.

Add structure to layout

Please notice that I’m adding the types with a packing of 1: PE structures are pragma packed to 1.

What you see applied to the hex view, is a layout. In a layout you can insert structures or intervals (a segment of data with a description and a color).

A layout can even be created programmatically and be attached to a hex view as we’ll see in some other post. The implementation of layouts in the Profiler is quite cool, because they are standalone objects. Layouts are not really bound to a hex view: a view just chooses to be attached to a layout. This means that you can share a single layout among different hex views and changes will reflect in all attached views.

Multi-view layout

And while I didn’t mention it, the table view below on the left is the layout inspector. Its purpose is to let you inspect the structures associated to a layout at a particular position. Since layouts allow for overlapping structures, the inspector shows all structures associated in the current range.

Multi-structure inspection

But what if you go somewhere else and return to the hex view? The layout will be gone. Of course, you could press Ctrl+Alt+L and re-attach the layout to the view. There are other two options: navigate back or create a bookmark!


The created bookmark when activated will jump to the right entry and associate the layout for us. Remember that changing the name of a layout invalidates the bookmark.

That’s all for now. And we’ve only scraped the surface… 🙂