programmatic-seo
Builds large sets of search-targeted pages from a template and a dataset — the location, comparison, integration, and use-case pages that capture long-tail demand at scale. Use this when there is a repeating query pattern with real volume, when a dataset could answer many similar searches, or to judge whether a programmatic approach is viable before building it.
git clone --depth 1 https://github.com/cbrock84/headcount /tmp/programmatic-seo && cp -r /tmp/programmatic-seo/plugins/demand-generation/skills/programmatic-seo ~/.claude/skills/programmatic-seoSKILL.md
# Programmatic SEO Done well, one template covers thousands of real searches. Done badly, it is a mass of thin pages that damages the whole domain. ## Qualify before building All four must hold: 1. **A real query pattern** with verified volume across many instances — not one popular term and a long tail of zeroes. 2. **Data you actually have**, at quality, for most instances. Missing data produces empty pages, and empty pages are the failure mode. 3. **Genuine per-page value.** If two pages differ only by a swapped noun, they are duplicates however they are generated. 4. **A reason to be better** than what ranks now. Usually completeness, freshness, or data nobody else has. Fail any one and the answer is fewer, better pages. ## Building - **Design the best single page first**, by hand, and confirm it is genuinely useful. Then find what in it is variable. Templating before you know the good page scales a mediocre one. - **Vary the substance, not just the strings.** Each page needs data, comparisons, or context specific to it. - **Set a minimum data threshold.** Below it, the page does not get generated. This single rule prevents most programmatic disasters. - **Internal linking is not optional** — thousands of orphaned pages will not be crawled. Build hub pages and cross-links into the template. - **Roll out in batches.** Publish a few hundred, wait for indexation and performance, then continue. A full launch that goes wrong is hard to unwind. ## Maintaining Stale programmatic pages rot faster than editorial ones because there are so many. Set a refresh cadence tied to the data source, and prune: pages with no impressions after two quarters should be consolidated or removed. Volume is not the goal. ## Never - Generate pages for instances with no data. - Spin text to create the appearance of uniqueness. - Launch without a plan for removing what does not work.
Concentrates marketing and sales effort on a named set of accounts rather than on volume — qualifying whether the model fits your economics at all, building the account list and the buying group inside each, tiering effort against account value, coordinating so the account experiences one campaign rather than several, and measuring account progression instead of leads. Use this to decide whether to run an account-based program, build one, or work out why an existing one produces activity and no pipeline.
Designs orchestrator-and-subagent hierarchies for a repository — splitting agents by exclusive write surface, pairing every producer with an independent auditor, and enforcing the split with a script that runs in CI. Use this whenever the user wants to set up, expand, audit, or fix a multi-agent or subagent structure for a codebase; asks how to divide work between agents; wants agent charters, roles, or a surface map written; or is hitting agents that collide on the same files, review their own work, or drift from their remit. Also use when sizing a roster or deciding whether a new agent is justified.
Governs models and AI systems in production — intended use, evaluation, monitoring, human oversight, documentation, and the decision to deploy or retire. Use this before deploying a model or AI feature, when defining evaluation criteria, when a model's behavior has drifted, when assessing AI risk or regulatory exposure, or when deciding whether an AI system is fit for a consequential decision.
Produces executive-level research — market sizing, competitor mapping, trend analysis, and strategic intelligence — grounded in cited sources with the confidence in each claim made explicit. Use this to analyze a market or industry, map competitors, evaluate a market-entry or build-versus-buy decision, produce a research brief, or assemble evidence for a decision. Also use when comparing options that need a structured, evidence-based verdict rather than an opinion.
Optimizes for AI assistants and AI-generated answers — being retrievable, being cited, and being represented accurately when a model answers on your behalf. Use this when traffic is shifting from links to AI answers, when a brand is misrepresented or absent in AI responses, when planning content for retrieval rather than ranking, or when deciding how AI search changes an existing SEO program.
Improves visibility and conversion in the App Store and Google Play — metadata, keywords, screenshots, ratings, and the listing experience that turns an impression into an install. Use this to audit or optimize an app listing, plan a launch listing, diagnose poor install conversion, or improve store search visibility.
Protects and restores data — backup coverage and scope, retention, immutability against ransomware, and proving restores actually work. Use this to design a backup regime, verify restores, plan retention, protect backups from ransomware, or recover from data loss.
Runs the planning cycle — annual budget, rolling forecast, consolidation of business unit inputs, and the variance analysis that explains actuals against plan. Use this to build or challenge a budget, run a forecast cycle, consolidate departmental submissions, explain why results diverged from plan, or improve forecast accuracy.