SleekRank for rock identification pages
Maintain a sheet of around 700 rock types across igneous, sedimentary, and metamorphic classes. SleekRank publishes one page per type at /rock-id/{slug}/ with mineral composition, texture, formation environment, and OG card driven by the row.
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Rock ID lives between mineralogy and field geology
A hiker picks up a rock and wants to know what they're holding. A geology student studies for an identification exam. A garden landscaper sources stone for a wall. All three queries hit the same kind of page: a focused ID page with the rock type, the mineral composition, the texture, the formation environment, and a few specimen photos. Around 700 rock types covers the audience.
SleekRank reads the rock-type sheet as the schema. Columns for slug, name, class, parent_rock, composition, texture, formation_environment, color_range, specific_gravity, uses, and image_url feed the base page at /rock-id/{slug}/. List mappings render mineral composition and uses, a selector mapping fills the spec table, and a meta mapping wires the OG card.
Cluster blocks at the bottom surface other rocks in the same class (other igneous rocks for granite, other sedimentary rocks for limestone) and other rocks with overlapping composition (other quartz-rich rocks). Both are list mappings against the same sheet, returning six related rocks per page deterministically.
Workflow
From rock-type row to ID page
Build the base rock-type page
Compile the rock-type sheet
Wire selector and list mappings
Cluster by class and composition
Data in, pages out
One row per rock type, one ID page
| slug | name | class | texture | formation_environment |
|---|---|---|---|---|
| granite | Granite | igneous | phaneritic | deep intrusion |
| limestone | Limestone | sedimentary | clastic / biogenic | shallow marine |
| basalt | Basalt | igneous | aphanitic | lava flow |
| sandstone | Sandstone | sedimentary | clastic | river / desert / shore |
| marble | Marble | metamorphic | granoblastic | regional / contact |
/rock-id/{slug}/
- /rock-id/granite/
- /rock-id/limestone/
- /rock-id/basalt/
- /rock-id/sandstone/
- /rock-id/marble/
Comparison
Hand-published rock guides vs SleekRank
Per-rock-type WordPress posts
- Each rock type written manually, with composition listed inline as prose
- Texture and formation terminology drifts across entries
- Cross-references between parent rocks and metamorphic equivalents are manual
- Specimen photos and credits typed per post, often missing
- Schema and OG card configured per post, broken on most
- Practical ceiling around 50 rock types before quality decays
SleekRank
-
One row per rock type fills
/rock-id/{slug}/automatically - List mappings render mineral composition and common uses
- Selector mappings fill the spec table with class, texture, and environment
- Parent rock and metamorphic equivalent columns drive transformation clusters
-
Meta mapping wires
og:imagefrom the same row - Around 700 rock types become 700 indexable URLs from one template
Features
What SleekRank gives you for Rock identification pages
Mineral composition lists
Composition stored as a JSON array column with mineral name and percentage. The list mapping renders one li per mineral under a Mineral Composition heading. Cross-references link each mineral to its species page, so the rock corpus and the mineral corpus interlock cleanly.
Formation environment and texture
Selector mappings fill texture, formation environment, and tectonic context cells from columns. The reader sees not only what the rock is but where it forms, which is the field geologist's first question after the rock name.
Parent rock and transformation links
Parent rock and metamorphic equivalent columns drive a transformation block on each page. Granite links to gneiss (its high-grade metamorphic equivalent), limestone links to marble, shale links to slate. The geological story of the rock unfolds across linked pages.
Use cases
Who runs rock ID libraries on SleekRank
Geology departments and labs
Run a teaching rock library off the same sheet that backs the lab specimen collection. Composition values and texture descriptions stay consistent, and undergraduates always read the same fields across the corpus.
Landscape stone and quarry sites
Pair the ID library with the quarry inventory. Each rock-type page links to suppliers who carry the stone; each product page links back to the rock-type page for spec reference.
Field guide and hiking sites
Build regional rock libraries tied to trail guides. Each trail page lists the rock types a hiker is likely to see; each rock-type page lists trails where it is exposed. Both flow from one sheet with parallel arrays.
The bigger picture
Why rock ID rewards a structured per-type library
Rock identification queries split between hobbyists picking up a rock on a hike and students preparing for a lab exam. Both audiences benefit from the same kind of page: a focused entry that names the rock, lists the minerals that compose it, describes the texture, and explains how the rock forms. Building 700 such entries by hand is impossible at any reasonable timescale, and the values drift between entries as authors change.
A data-driven library inverts the work: the editor curates the rock-type sheet, the developer maintains the template, and the corpus emerges as a consistent reference library across all 700 entries. The other reason rock ID works as a generated corpus is the interlocking with mineralogy. Every rock is composed of minerals, and a mineral library and a rock library together form a tightly cross-linked reference.
The composition column on the rock page carries mineral slugs; the occurrence column on the mineral page carries rock slugs. Both link graphs are generated, not hand-maintained, so they stay consistent as the corpus grows. The hiker who lands on the granite page can follow the links to quartz, feldspar, and mica; the student who lands on the quartz page can follow the links to granite, sandstone, and quartzite.
The same sheets feed all of it.
Questions
Common questions about SleekRank for Rock identification pages
The IUGS rock classification scheme is the canonical source for igneous rocks; the Pettijohn scheme for sedimentary; the metamorphic facies scheme for metamorphic. Apply one classification system per class as the column vocabulary, and let the sheet enforce naming consistency. The template stays the same; the column values stay controlled.
Add a composite_components JSON array column for rocks that are mixtures of others. A list mapping renders the components on the page. Migmatite, for instance, has a melanosome and a leucosome; both render as separate entries in the composite list.
Yes. A use_class column with values like building, ornamental, or industrial drives conditional mappings. Building-stone pages show a workability and durability block; field-rock pages hide it. One template, several conditional blocks, the same sheet.
Add a tectonic_setting column (subduction, mid-ocean ridge, continental rift, passive margin). A list mapping renders other rocks formed in the same setting at the bottom of the page. A geologist browsing through a setting reads the page suite end-to-end without leaving the corpus.
Add an aliases JSON array column listing regional or historic names. A selector mapping renders the aliases under a Also Known As heading. Search engines pick up the regional terms as alternate names, so a query for trap rock lands on the basalt page and a query for caliche lands on the calcrete page.
Yes. The composition JSON array column carries mineral slugs; a list mapping renders each as a link to the matching /mineral/{slug}/ page (assuming the mineral library is on the same site). The two corpora interlock without an editor maintaining the link graph.
An image_credit column per row carries the photographer or source. A selector mapping renders credit under the photo. USGS public-domain photos credit USGS; community-contributed photos credit the photographer. The same template handles both.
The static pages each show their composition. A separate Vite-built search widget reads the same JSON file and filters by mineral, texture, or formation environment. The widget links to the static pages, so search and corpus share one source of truth.
Pricing
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