本文へ移動
cccskills
無料GitHub で公開

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.

インストール方法を見る

含まれるファイル(2)

  • SKILL.md2.6 KB
  • references/sources.md2.6 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

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.

Sources

references/sources.md in this skill lists the outside authorities that settle the questions here — what each one is authoritative for, and what you may do with it. Check them before answering on anything they cover, and cite what you used. Most are free to read and not free to reproduce; the use note on each is binding.

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.

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

Designs and audits who can reach what — authentication, authorization models, privileged access, service credentials, and joiner-mover-leaver process. Use this to design a permissions model, run an access review, reduce standing privilege, handle offboarding, set up SSO or MFA, manage service and machine credentials, or diagnose why permissions have sprawled.

日本語の概要は準備中です。原文の説明を表示しています。

cbrock84/headcount2,0282026年9月18日 更新

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.

日本語の概要は準備中です。原文の説明を表示しています。

cbrock84/headcount2,0282026年9月18日 更新

Gets new users from signup to first real value — signup flow, onboarding, time-to-value, and the early experience that determines whether someone becomes a user or a lapsed account. Use this to design or fix signup and onboarding, diagnose why signups do not convert to active use, reduce time-to-value, or decide what a new user must accomplish first.

日本語の概要は準備中です。原文の説明を表示しています。

cbrock84/headcount2,0282026年9月18日 更新

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.

日本語の概要は準備中です。原文の説明を表示しています。

cbrock84/headcount2,0282026年9月18日 更新

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.

日本語の概要は準備中です。原文の説明を表示しています。

cbrock84/headcount2,0282026年9月18日 更新

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.

日本語の概要は準備中です。原文の説明を表示しています。

cbrock84/headcount2,0282026年9月18日 更新

cbrock84 のスキルをすべて見る

このスキルの問題を報告する