Free-text notes and findings recorded during a content audit. Used to capture observations, issues, and recommendations for individual pages or content assets.
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Bucket
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When planning a transformation, it can be useful to bucket similar content (often grouping content that will be treated similarly to explain the situation to stakeholders).
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Disposition
Particularly useful for product or property pages, this field represents the revenue generated by this product or property (regardless of whether it was directly generated by digital channels or not).
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Org
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Content Type (semantic type of content, such as Product Page or Event) is usually an extremely effective way to group and look for patterns across a digital presence.
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How many links the crawler needed to follow to get to this item.
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[IA] Depth
The date the page content was last modified or updated. Often available in HTTP headers or CMS metadata. Distinct from Date Published, which records when the content was first made public.
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Date the content was originally published. This is frequently a useful factor in deciding what content can be culled.
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This is the treatment a piece of content will get during a transformation.
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Effort
File format (as opposed to content type) is the actual format of the file as delivered by the web server (PDF, HTML, etc). This is especially useful for sites with a large amount of non-HTML.
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File Group
On particularly complex digital presences, there may be so many file formats that seeing all of them in charts or presentations is confusing. File Group groups the file formats, for instance "Data or Spreadsheet" to capture CSV and Excel files.
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Folder1
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Folder1 is the first "folder" in the path, such as "blog" in "test.com/blog/". This is often an effective proxy for site section.
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Site Section
Yes or no, does this piece of content have this specific problem? The actual field name would depend on your situation, such as "Has Wall of Text".
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The depth from the perspective of the main navigational structures, for instance the Breadcrumb Depth.
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Category
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Count of `<img>` elements with missing or empty `alt` attributes.
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Technical
fields.
Count of HTML5 landmark regions (`nav`, `main`, `header`, `footer`, `aside`) or elements with landmark ARIA roles.
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Technical
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The technical content type reported by the web server, which the web browser uses to determine how to display it.
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File Format
The meta description. This is of limited use, aside from simply discovering what pages do not have a meta description (and therefore require one).
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Meta keywords. In most cases, very limited value. More precise meta tags (for instance topics) are usually far more useful if they exist.
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Topic
Is there a near text duplicate of the page? If so, what is the URL for that near duplicate.
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Does the page keep users informed about where they are and what is going on? Look for breadcrumbs, clear page titles, current-section highlighting in navigation, and timely feedback for user actions.
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User
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LLM-written rationale for the Nielsen Heuristic 1 (H1 Visibility) score.
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LLM-assigned Nielsen Heuristic 1 (H1 Visibility) score (0-3). Used as input to the calculated Nielsen H1 Visibility field.
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Final per-heuristic score for Nielsen Heuristic 10 (H10 Help & Docs). Derived from the LLM score with type coercion (and an n/a-aware path for H5/H9).
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LLM-written rationale for the Nielsen Heuristic 10 (H10 Help & Docs) score.
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User
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LLM-assigned Nielsen Heuristic 10 (H10 Help & Docs) score (0-3). Used as input to the calculated Nielsen H10 Help & Docs field.
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Final per-heuristic score for Nielsen Heuristic 2 (H2 Real-World Match). Derived from the LLM score with type coercion (and an n/a-aware path for H5/H9).
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LLM-written rationale for the Nielsen Heuristic 2 (H2 Real-World Match) score.
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User
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LLM-assigned Nielsen Heuristic 2 (H2 Real-World Match) score (0-3). Used as input to the calculated Nielsen H2 Real-World Match field.
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Final per-heuristic score for Nielsen Heuristic 3 (H3 User Control). Derived from the LLM score with type coercion (and an n/a-aware path for H5/H9).
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LLM-written rationale for the Nielsen Heuristic 3 (H3 User Control) score.
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LLM-assigned Nielsen Heuristic 3 (H3 User Control) score (0-3). Used as input to the calculated Nielsen H3 User Control field.
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Final score for H4 Consistency, blending the LLM score 60% with the structural sub 40% via 3:2 weighting in avg(). The repetition trick bypasses Chimera's strict left-to-right evaluation without explicit decimal multiplication.
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LLM-written rationale for the Nielsen Heuristic 4 (H4 Consistency) score.
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LLM-assigned Nielsen Heuristic 4 (H4 Consistency) score (0-3). Used as input to the calculated Nielsen H4 Consistency field.
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Intermediate helper that aggregates the three structural-signal pattern counts (images without alt, H1 count, landmark count) into a single 0-3 value, used by the H4 Consistency formula. Exists because Chimera expressions cannot multiply directly through case() returns — pre-materializing this helper as a float column sidesteps that limitation.
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Does the page prevent problems from happening in the first place? On pages with forms or user actions, look for clear labels, sensible defaults, required-field marking, and confirmation for destructive actions. Does not apply to purely informational pages.
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LLM-written rationale for the Nielsen Heuristic 5 (H5 Error Prevention) score.
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LLM-assigned Nielsen Heuristic 5 (H5 Error Prevention) score (0-3 or n/a). Used as input to the calculated Nielsen H5 Error Prevention field.
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Final per-heuristic score for Nielsen Heuristic 6 (H6 Recognition). Derived from the LLM score with type coercion (and an n/a-aware path for H5/H9).
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LLM-written rationale for the Nielsen Heuristic 6 (H6 Recognition) score.
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LLM-assigned Nielsen Heuristic 6 (H6 Recognition) score (0-3). Used as input to the calculated Nielsen H6 Recognition field.
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Final per-heuristic score for Nielsen Heuristic 7 (H7 Flexibility). Derived from the LLM score with type coercion (and an n/a-aware path for H5/H9).
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LLM-written rationale for the Nielsen Heuristic 7 (H7 Flexibility) score.
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User
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LLM-assigned Nielsen Heuristic 7 (H7 Flexibility) score (0-3). Used as input to the calculated Nielsen H7 Flexibility field.
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Final per-heuristic score for Nielsen Heuristic 8 (H8 Aesthetic). Derived from the LLM score with type coercion (and an n/a-aware path for H5/H9).
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LLM-written rationale for the Nielsen Heuristic 8 (H8 Aesthetic) score.
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User
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LLM-assigned Nielsen Heuristic 8 (H8 Aesthetic) score (0-3). Used as input to the calculated Nielsen H8 Aesthetic field.
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Final per-heuristic score for Nielsen Heuristic 9 (H9 Error Recovery). Derived from the LLM score with type coercion (and an n/a-aware path for H5/H9).
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LLM-written rationale for the Nielsen Heuristic 9 (H9 Error Recovery) score.
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LLM-assigned Nielsen Heuristic 9 (H9 Error Recovery) score (0-3 or n/a). Used as input to the calculated Nielsen H9 Error Recovery field.
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Average of the 10 Nielsen per-heuristic scores. avg() skips real NULLs, so n/a paths on H5 and H9 (which materialize as NULL on float columns) do not drag the average down.
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The count of pages in a PDF can help us understand whether there are primarily short PDFs (perhaps most easily converted to HTML) or very long PDFs (perhaps for specialist audiences).
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Technical
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Page views are often the first thing to be added from an additional source, after the basic rows of content in the inventory/audit have been determined. Although not always the most useful metric for the value of content, it's often the most immediately tangible proxy.
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[Success Event] Count
How often does the problem happen on the page? This would be a specific issue, so something like "Left Nav Count".
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An example of a problem (on a specific page) you are investigating. This field could be repeated in an analysis, with actual fields like "Table Example" or "Bad Character Encoding Example".
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Reading Level represents the education level required to understand text. Since much content is overly complex, this can be useful to identify where there may be education requirement mismatches.
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Is the information redundant with respect to other content on the site? This is one of the anchors of the highly popular ROT three fields.
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Near Text Duplicate
Who will do the actual transformation. For a large site this would be the team, and for a smaller site it may be the individual.
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Decision
fields.
The section of a site (for instance the news section, or a section for a particular program).
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Category
fields.
Where is the primary source of content for this URL? For instance, what CMS, document management system, or product information system does this content primarily come from?
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The count of events that were successful from this page, such as the count of purchases or the count of downloads.
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What the *desired* field value would be. For instance, there may be an existing content type but in some cases it should be another content type (so you would have a Content Type field as well as a Target Content Type field).
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An ID unique for the piece of content. This should be unique across the entire list.
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