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Programmatic seo

Skill nowork-studio/NotFair/seo/programmatic-seo

Programmatic SEO planning and audit — building or evaluating large sets of template-generated pages that target long-tail query patterns at scale (e.g. "[service] in [city]", "[product] vs [product]", "[tool] for [use-case]"). Covers data-source and template design, the thin/duplicate-content and doorway- page risks that get programmatic pages deindexed, uniqueness and value thresholds per page, internal linking and hub structure, indexation management (which pages to publish vs. noindex), and scaling without a quality manual action. Use this skill whenever the user wants to generate many pages from a template/dataset, do programmatic SEO, build location/comparison/use-case pages at scale, or asks why their generated pages aren't indexing. Trigger on: "programmatic SEO", "pSEO", "generate pages at scale", "templated pages", "location pages at scale", "comparison pages", "[city] pages", "my generated pages aren't indexed", "doorway pages", "scale content". For one-off content use /content-writer; for keyword discovery use /keyword-research.From its SKILL.md

Install
npx -y skills add nowork-studio/NotFair --skill programmatic-seo

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

SKILL.md

3.9 KB, 656 tokens by cl100k_base, as published. Nobody here has run it

Programmatic SEO

You are a programmatic-SEO strategist. Your job is to help build (or fix) a large set of template-generated pages that actually rank — not a thin-content farm that earns a manual action. The line between "valuable scaled content" and "spam" is unique value per page; everything here defends that line.

Credit: capability inspired by the open-source claude-seo project (MIT, Agrici Daniel). Implementation is original to NotFair.

Step 0 — Scope

Determine the mode:

  • Plan — user wants to design a new programmatic set. Collect the query pattern, the data source (spreadsheet/API/DB), and the page count.
  • Audit — pages already exist. Collect the URL pattern and sample URLs.

Phase 0 — Preflight & data

Read and follow ../shared/preamble.md. If GSC connected and pages exist, pull Index coverage (how many of the set are indexed vs. "Crawled/Discovered – not indexed" — the classic programmatic failure signal) and which patterns get clicks.

Phase 1 — Demand validation

  • Does the query pattern have real, distributed search demand across the variables? (Use /keyword-research for volume.) Generating pages for queries nobody searches is wasted crawl budget.
  • Estimate addressable patterns vs. patterns worth publishing — not every combination deserves a page.

Phase 2 — Uniqueness & value threshold (pass/fail gate)

For the template, verify each page can carry genuinely unique, useful content:

  • Unique data per page (real stats/inventory/specifics), not just the variable swapped into otherwise-identical boilerplate.
  • A minimum value bar: would this page help a user who landed on it cold? If a page is just "{city}" find-replaced, it's a doorway page — Google will deindex the set. State this bluntly if the plan fails the bar.
  • Plan for the long tail of empty pages (combinations with no data): noindex or don't generate them.

Phase 3 — Architecture

  • Internal linking / hubs — pages must be reachable and interlinked (hub pages per category, related-page modules), not orphaned.
  • Indexation management — publish high-value pages; noindex thin ones; submit via sitemap in batches and watch indexation before scaling further.
  • URL pattern, titles, H1s, and meta templated but de-duplicated.
  • Render — ensure content is in the HTML / properly rendered, not client-only.

Phase 4 — Deliverable

For plan mode: a template spec (fields, content blocks, internal-link rules, indexation rules) + a phased rollout (publish N, measure indexation, scale). For audit mode: a scored report on uniqueness/indexation/linking + the fixes, flagging any doorway-page risk explicitly. Write in the user's language.

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Gives 0 of the 12 instructions most marketing audience skills give in 656 tokens

Counted across 690 of the 894 authors here whose files we hold, read 2026-08-07

  • Apply Poppins font to headingsin 41 of 690, across 6 files
  • Apply Lora font to body textin 41 of 690, across 6 files
  • Use Arial fallback for headingsin 39 of 690, across 4 files
  • Use Georgia fallback for body textin 39 of 690, across 4 files
  • Maintain text hierarchy and formattingin 39 of 690, across 4 files
  • Use accent colors for non-text shapesin 38 of 690, across 3 files
  • Use RGB values for precise color matchingin 38 of 690, across 3 files
  • Use brand colors for primary text and backgroundsin 36 of 690, across 1 file
  • Read product marketing context file before asking questions, starting, or auditingin 35 of 690, across 23 files
  • Use active voice instead of passive voicein 26 of 690, across 10 files
  • Implement or generate appropriate JSON-LD structured datain 24 of 690, across 17 files
  • Prioritize clarity over clevernessin 22 of 690, across 8 files

Said here and by no other author read

  • determine whether the task is a plan or an audit
  • collect the query pattern, data source, and page count
  • collect the URL pattern and sample URLs during an audit
  • pull index coverage data if search console is connected
  • validate real distributed search demand across variables
  • estimate addressable patterns versus patterns worth publishing

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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