# Programmatic SEO Human Guide ## What This Is For How to design and run a programmatic SEO program that produces durable traffic instead of penalty-bait. It gives the agent a clearer input/output frame for search and SEO workflows: what context to ask for, what decisions to make, and what usable artifact to return. Use this as a human-readable version of the Programmatic SEO agent skill. It is meant for marketers, operators, founders, and other non-coders who want the workflow without reading agent-specific implementation instructions. ## When To Use This - Use this when you need a repeatable process for programmatic SEO. - Use this when the task needs judgment, examples, constraints, or a clear output format rather than a one-off prompt. - Use this when you want to hand an AI assistant enough context to produce a usable marketing artifact. ## When Not To Use This - Do not use this when you only need a quick factual answer. - Do not use this when the work depends on private data you cannot share with the assistant. - Do not use this as a replacement for legal, compliance, financial, or medical review. ## What You Need Before Starting - The goal or business outcome you want. - The audience, customer segment, or market context. - Any source material the assistant should respect, such as notes, briefs, examples, URLs, or brand guidance. - Constraints such as tone, length, channel, deadline, region, or approval requirements. - A clear definition of what a good final answer should look like. ## Step-By-Step Workflow 1. State the job clearly: "Use the Programmatic SEO guide to help me with..." 2. Add context: audience, goal, offer, channel, source material, and constraints. 3. Ask the assistant to identify missing inputs before producing the final output. 4. Have the assistant follow the skill-specific guidance below. 5. Review the result against the final checklist and ask for revisions where needed. ## Skill-Specific Guidance - `content-strategy` is program-scope: editorial pillars, calendar, governance. Decides whether pSEO fits the program at all. - `pillar-content-architecture` is hub-scope: one topic with 10 to 15 intentional editorial pieces. Editorial in nature. - `content-brief-authoring` is per-piece scope: brief for one editorial artifact. - `content-and-copy` is execution scope: writing individual editorial pieces. - This skill is scaled scope: 100s to 100,000s of pages generated programmatically from structured data sources, each targeting a long-tail query. - The underlying data is shallow (3 to 5 fields, mostly AI-generated, no first-party signal) - The query volume is illusory (long-tail keywords nobody actually searches) - User intent requires narrative or judgment that data cannot supply - Quality control is not budgeted (write a bunch of pages, ignore them) - The program is scaling AI-generated thin pages without unique data - "We generated 50,000 pages and got penalized." Thin content, scaled too fast, no QC discipline. - "We have 10,000 pages but only 200 rank." Internal linking architecture missing; child pages are orphans. ## Decision Points And Nuance The original skill emphasizes: What this skill is for, When pSEO is the right answer, Data source identification, Template design, Schema design, Quality control at scale, Internal linking across the set, Crawl budget management, AEO and GEO for programmatic pages, Refresh and maintenance at scale. Use these questions to steer the work: - What is the intended audience or buyer? - What source material must be preserved? - What should the assistant optimize for: clarity, persuasion, accuracy, speed, creativity, or conversion? - What examples represent the desired quality bar? - What should the assistant avoid? ## Common Mistakes - **Distinctive value per page.** Each generated page must offer something the user could not get by going up to the parent or sideways to a sibling. If the page is just a re-pivot of the parent's data, the page is filler. - "AI engines do not cite our pages." Top-200-word answer was not designed; structured data is thin. - If required data is unavailable - The discipline is not optional and it is not free. Budget for it before generating the first page, or do not start at all. - "AI engines do not cite our pages" - **Prevention.** The required-field threshold in the schema enforces a quality floor. Pages that fail the threshold should not have shipped in the first place. - **Prevention.** The defensibility test (`data-source-identification-patterns.md`) before launch. If competitors could replicate the data source within 6 months at reasonable cost, do not launch the program. - **The discipline.** Pages that fail the schema's required-field threshold do not get a public URL. They sit in the system as drafts until their data populates. ## Copy-And-Paste Prompt ```text Use the Programmatic SEO human guide. My goal: [Describe the business outcome] Audience: [Describe who this is for] Context and source material: [Paste notes, examples, links, or existing copy] Constraints: [Tone, length, channel, timeline, must-include items, must-avoid items] Before producing the final output, ask me for any missing information that would materially improve the result. ``` ## Final Checklist - [ ] The output matches the original goal. - [ ] The audience and context are reflected in the answer. - [ ] Important constraints and source material were preserved. - [ ] The assistant made the relevant decisions explicit. - [ ] The final artifact is ready to use, review, or hand to the next person. ## Source This guide was generated from the rampstackco/claude-skills skill entry for `programmatic-seo`. ## Source Skill Notes These notes preserve the nuance from the original skill. Use them as supporting reference when the workflow above feels too generic. # Programmatic SEO A senior SEO strategist's playbook for designing and running programmatic SEO programs that produce durable traffic instead of penalty-bait. Programmatic SEO has a complicated reputation. Sites like Zillow, Airbnb, TripAdvisor, Indeed, and Yelp have built billion-dollar traffic engines on pSEO. Other sites have built pSEO programs that got hit by Google's helpful-content updates and lost 80% of their traffic in a week. The difference is rarely the technique; it is the underlying data quality and the quality control discipline at scale. This skill is the playbook for getting that distinction right. It assumes you have decided what keyword space to target (see `seo-keyword`) and how the broader content program is shaped (see `content-strategy`). It does not write individual editorial pieces (see `content-and-copy` for that) and does not architect editorial topic hubs (see `pillar-content-architecture` for that). What it does is teach the discipline of generating high-volume pages programmatically from structured data sources without producing thin content, duplicate content, or scale-without-substance pages that get penalized. When to use this skill: deciding whether pSEO is a fit for the program at all (the most important question), designing a new pSEO system, auditing an existing pSEO set that is not ranking or has been hit by an algorithm update, or building quality-control discipline for a pSEO program that grew faster than its quality processes. --- ## What this skill is for This skill spans scaled-content programs from data source through quality control. It composes with five sister and adjacent skills, and the distinction between them is what keeps each one sharp. - `content-strategy` is program-scope: editorial pillars, calendar, governance. Decides whether pSEO fits the program at all. - `pillar-content-architecture` is hub-scope: one topic with 10 to 15 intentional editorial pieces. Editorial in nature. - `content-brief-authoring` is per-piece scope: brief for one editorial artifact. - `content-and-copy` is execution scope: writing individual editorial pieces. - This skill is scaled scope: 100s to 100,000s of pages generated programmatically from structured data sources, each targeting a long-tail query. The clean reading order: `content-strategy` decides whether pSEO is a fit, `seo-keyword` surfaces the long-tail keyword space, this skill designs the pSEO system, `editorial-qa` (forthcoming) provides the QA discipline for sampled quality control across the set. `content-and-copy` and `content-brief-authoring` are not in the loop for pSEO at scale; those skills are for editorial pieces, not data-driven generated pages. The audience: SEO content strategists, content engineers, agencies running pSEO programs, in-house teams considering pSEO as a growth lever. The voice is senior SEO strategist to junior PM or marketer. Specific, opinionated, honest. The reputation problem is not pSEO; it is pSEO without underlying value. --- ## When pSEO is the right answer The keystone question. pSEO works when all of the following are true. **1. Real underlying data.** A genuine structured data source with depth: 10+ fields per record, ideally 20+, with first-party data, expert-curated data, or licensed datasets. Not just scraped data or AI-generated facts dressed as structured records. **2. Long-tail query volume justifies the effort.** The queries the program would target have meaningful aggregate volume even if individual queries are small. Real estate "homes for sale in {neighborhood}" works at scale. "Blue widget reviews 2026" generated for every adjective times widget combination does not, because the queries are not actually searched. **3. User intent is queryable.** The user's question can be answered through structured data presented well. Not through narrative explanation, judgment, or analysis the data cannot supply. **4. Update cadence aligns with query volatility.** Real estate listings update daily and the data refresh aligns. "Best [thing] for [year]" pages get stale annually and need a refresh discipline budgeted in. **5. Quality control is operationally feasible.** The team has the capacity to sample-audit the set, fix failures, and maintain quality as the set grows. Not aspirationally; budgeted in headcount. pSEO does NOT work when: - The underlying data is shallow (3 to 5 fields, mostly AI-generated, no first-party signal) - The query volume is illusory (long-tail keywords nobody actually searches) - User intent requires narrative or judgment that data cannot supply - Quality control is not budgeted (write a bunch of pages, ignore them) - The program is scaling AI-generated thin pages without unique data The honest framing. Most teams that ask "should we do pSEO?" should hear "probably not, unless the underlying data is unique or first-party expertise makes the pages actually useful." The reputation problem is not the technique; it is the technique applied without underlying value. Detail in [`references/when-pseo-works-decision.md`](references/when-pseo-works-decision.md). --- ## Data source identification The data source is the pSEO program. Common sources with different defensibility profiles. **First-party data.** Customer transactions, content database, user-generated content. Defensible because nobody else has it. Examples: Glassdoor's employee reviews, Yelp's user ratings, TripAdvisor's traveler reviews. **Licensed datasets.** Industry databases, regulatory data, government datasets, licensed third-party feeds. Defensible by license terms and integration depth. Examples: Zillow's MLS partnerships, real estate brokerage feeds, sports statistics licenses. **Aggregated public data.** Scraped, cleaned, enriched. Judgment call on legality (often gray area depending on robots.txt, terms of service, jurisdictional rules). Defensibility depends on the cleaning and enrichment work. Easy to copy if the cleaning is shallow. **Expert-curated content.** The dataset is built by hiring experts to populate it. Slow, high-quality, defensible. Examples: Wirecutter's product testing, expert-reviewed medical content, curated editorial databases. **Synthesized data.** Combining multiple sources into a unique view. "Neighborhoods times schools times prices" combining three datasets into a comparison view. Defensibility comes from the synthesis logic and the ongoing maintenance of multi-source pipelines. The "moat" question. Would a competitor be able to replicate the data source? If yes, the pSEO program has no defensibility; anyone can copy. If no, the data source becomes a moat that compounds. Zillow's MLS partnerships, Glassdoor's employee reviews, Crunchbase's funding data, are moats. "Scraped Wikipedia plus AI rewrite" is not. Detail in [`references/data-source-identification-patterns.md`](references/data-source-identification-patterns.md). --- ## Template design The template is the structure that data fills. Design principles.