blog-topic-research
Stop writing blog posts nobody searches for. This skill builds your editorial backlog from real, verifiable user demand - never from AI vibes. It mines candidates from Google Suggest, People Also Ask, Reddit, Stack Overflow, GitHub issues, vendor forums, and changelogs; captures every signal as a citable URL with verbatim evidence; classifies each topic by post format (how-to-fix, x-vs-y, listicle, migration, release-recap, ...); checks against your existing backlog so you don't cannibalize what you already published; and outputs a writer-ready scaffold with primary sources, problem summary, c
npx skills add jeremylongshore/claude-code-plugins-plus-skills --skill blog-topic-research --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Generates topic candidates for a blog with documented user demand. For every topic, it captures a long-tail query title, assigns a cluster, selects a format, records demand signals with verbatim evidence and URLs, computes a signal score, and lists primary sources, keywords, commentary, problem summary, confirmed fixes, and version context. It requires that each topic include verifiable signals and primary sources, and may append results to a backlog path if requested.
How it works
The skill operates by driving the agent's WebFetch and WebSearch tools to fetch sources from multiple channels (e.g., Google Suggest, People Also Ask, Reddit, Stack Overflow, GitHub issues, vendor forums, changelogs). For each candidate, it records the source URL and the verbatim title text, then fetches the body of high-engagement items to extract error strings, code blocks, and version-qualified phrases. It classifies topics by format (one of how-to-fix, how-to-connect, how-to-automate, x-vs-y, what-is, use-case, listicle, migration, release-recap) and constructs a contract table with fields including topic, cluster, format, demand_signals, signal_score, primary_sources, keywords, commentary, problem_summary, confirmed_fixes, version_context, and question_variants. It enforces hard rules: no topic without a verifiable demand signal; no paraphrased evidence; numbers must come from a source URL. It computes strength tiers for signals and requires a signal_score >= 3 for acceptance. It also performs a cannibalization check using an existing backlog if provided.
When to use it
Trigger when the user says: 'research blog topics', 'find topics with real demand', 'expand the editorial backlog', 'research N long-tail topics', or any variant of growing a content pipeline with verified candidates.
What it can touch
It uses the allowed tools: WebSearch, WebFetch, Bash(python3:*), Read, Write. It records source URLs and verbatim titles as the basis for signals, and may shell out to a Python similarity script for cannibalization when an existing backlog is present. It requires that each topic include primary sources and verifiable signals.
Caveats
No URL, no topic. If the skill cannot cite a verifiable demand signal, the topic is dropped. No paraphrased evidence. All numbers in the topic title must come from a source URL or be omitted. The score threshold for topic acceptance is signal_score >= 3. If no backlog cache is provided, cannibalization uses Jaccard only. The writer re-fetches at least one primary source for currency. If the skill cannot reach N validated topics, it returns what it has and reports the shortfall.
# blog-topic-research Generates topic candidates for a blog with documented user demand. The skill exists to fight hallucinated SEO ideas: every topic it proposes must point to a URL that proves someone is asking about it. ``` research <N> topics [for cluster <C>] [--append-to <path>] ``` - `N` - number of topics to return (default 50; cap 100) - `cluster` - if the blog has cluster taxonomy, restr
What does the blog-topic-research skill do?
Stop writing blog posts nobody searches for. This skill builds your editorial backlog from real, verifiable user demand - never from AI vibes. It mines candidates from Google Suggest, People Also Ask, Reddit, Stack Overflow, GitHub issues, vendor forums, and changelogs; captures every signal as a citable URL with verbatim evidence; classifies each topic by post format (how-to-fix, x-vs-y, listicle, migration, release-recap, ...); checks against your existing backlog so you don't cannibalize what you already published; and outputs a writer-ready scaffold with primary sources, problem summary, c
How do I install it?
Run `npx skills add jeremylongshore/claude-code-plugins-plus-skills --skill blog-topic-research --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From jeremylongshore/claude-code-plugins-plus-skills, a repository with 2,596 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.