meta-tags
Skill0 starsChanged 2 months agoMITView on GitHub
Click-optimized meta titles, descriptions, slugs, and OG tags — five variants each, written as search-result ad copy.
In this repository15 other files it gives its agents.
- alignment-auditSkill
- auditSkill
- citable-passageSkill
- content-briefSkill
- entity-coverageSkill
- humanizerSkill
- internal-linksSkill
- navboost-auditSkill
- optimizeSkill
- researchSkill
- schema-genSkill
- scoreSkill
- seo-fundamentalsSkill
- topic-clustersSkill
- write-seoSkill
The file
We split this skill into the rules it gives an agent
2 rules. About 0 are common, 2 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈2
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 36
Before/after table when optimizing an existing page, with the reason each change should lift CTR.
- Distinctive: line 38
Never use example brands, dates, or figures that are not from the page or the user.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
/meta-tags — Meta element optimizer
Target: $ARGUMENTS
Why titles and descriptions matter beyond "metadata": they are inputs to click-based re-ranking (${CLAUDE_PLUGIN_ROOT}/skills/seo-fundamentals/references/navboost.md). Write them as ad copy with intent match; the first screen of the page must deliver what the title promises. Lengths and rules: ${CLAUDE_PLUGIN_ROOT}/skills/seo-fundamentals/references/scoring-spec.md.
Process
- Current state (URL given):
python3 ${CLAUDE_PLUGIN_ROOT}/scripts/fetch_page.py "<url>" --json→ current title, description, OG, canonical, H1. Measure lengths; note duplicates, missing keyword/entity, weak copy. Pull the query's GSC CTR vs position if available. - Keyword and entity — confirm the primary entity and keyword, 2–3 secondary terms, intent, whether an AI Overview appears for the query (it depresses CTR regardless of copy).
- Titles — 5 variants, 50–60 characters, primary entity front-loaded, brand at the end if space allows:
- A — Entity-first: "
<Entity>:<benefit>" - B — Sub-query targeted: answers a likely fan-out question
- C — Negative-space differentiator: "
<Topic>without<common pitfall>" - D — Freshness-bracketed: "[
<current month year>]" only when the content is genuinely updated - E — Number + specific promise: "
<N><things><that do X>" Classical fallbacks: "How to<action><without obstacle>", "<Topic>guide:<what the reader gets>".
- A — Entity-first: "
- Descriptions — 5 variants, 150–160 characters, keyword included, value proposition + CTA, intent match; formulas: direct value prop · question matching intent · specific fact from the page (real, from the content) · benefit + risk reversal · curiosity.
- Slug — 3–5 words, lowercase-hyphenated, keyword included, stop words removed (do not change the slug of an indexed page without a 301 plan).
- Open Graph / Twitter —
og:title(can be more descriptive),og:description,og:type,og:image1200×630,twitter:cardsummary_large_image,og:locale.
Output (per page)
- Title table: # · title · length · entity front-loaded ✓/✗ · variant · recommended.
- Description table: # · description · length · keyword ✓ · CTA ✓ · recommended.
- Slug recommendation.
- OG/Twitter block as HTML
<meta>tags. - Before/after table when optimizing an existing page, with the reason each change should lift CTR.
Never use example brands, dates, or figures that are not from the page or the user.
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