mcpbeat Sign in

SEO Growth Agent Skill

> site or brand with no rankings and no authority ("cold start", "starting from zero", "nobody knows us"), deciding what to double down on, a page or cluster that ranks but earns nothing, hunting emerging or newly-coined keywords before competitors arrive, "should we build a cluster or one page", "what do we write next", "we get impressions but no clicks", "our traffic plateaued despite publishing", whether to chase a head term at all, or turning any of it into recurring automation. Also use when asked why an SEO effort stalled despite consistent output. NOT for writing an article, keyword expansion mechanics, or auditing a single tactic for penalty risk.

14k tokens
context cost
the whole folder, loaded on every use
8
files
instructions only
0
copies elsewhere
how many repositories repackaged it
1141
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/AI-Builder-Club/skills --skill seo-growth

The instruction itself

10 sections, as written by the author

/seo-growth — first-party SEO growth method

**Core principle: the right move depends on whether you have data yet, and most bad SEO advice

is given to the wrong one of those two situations.** A site with no rankings and a site with a

breadwinner page need opposite behavior. Advice that ignores which you are in is how efforts

stall despite consistent output.

Distilled from two content properties run in production and the Search Console reads that

produced each rule. This is a first-party method: narrower than a general playbook, and

more trustworthy for it. Every rule traces to the failure or result behind it in

references/why-these-rules.md. Absolute traffic figures are withheld; the ratios and

thresholds are as observed.

The two playbooks

| You are here if… | Playbook | Read |

|---|---|---|

| No rankings, no authority, <100 clicks/mo, possibly no content | A — Cold start | references/cold-start.md |

| GSC has real rows: something ranks, something earns clicks | B — With data | references/with-data.md |

Diagnose per cluster, not per site. A mature site opening a new territory is in Playbook A

for that territory and Playbook B everywhere else — and that combination is the strongest

position in the method.

Playbook A in one line: you cannot win an established term from zero authority, so hunt an

emerging one, ship ONE page, and let day-7 / day-21 decide whether it was real.

Playbook B in one line: your own GSC already knows which page shapes win for you — find the

breadwinner, extract the shape, diagnose whether it is position-limited or snippet-limited,

compound it, and protect it while you do.

Read alongside either playbook

  • references/emerging-terms.md — the hunt. The wedge in both playbooks, and the *only*

thing that works in Playbook A. Where to look, in what order, and the seven gates a candidate

must pass.

  • references/measurement.md — which metric scores which situation, the four query

families that never click, and the GSC traps that produce confident wrong conclusions.

  • references/why-these-rules.md — the failure or result behind each rule.
  • references/operationalize.md — turning either playbook into scheduled loops.

Before advising anything: get three numbers

Pull a 28-day GSC window ending 3 days back (GSC lags 2–3 days):

  • Total clicks — the scale, and which playbook you are in.
  • Breadwinner share — what % of clicks the single best page earns.
  • Best non-brand position — where you rank on something you don't own by name.

If these aren't available, say so and instrument first. Advising without them is guessing, and

it is the most common failure mode in this whole area.

The five laws

  • A low-authority domain can only win a term while competition is thin. So a cold start

doesn't "do SEO" — it hunts emerging terms. Validated on two separate terms, months apart.

  • Score an emerging term on position, a mature one on clicks. Backwards, and the metric

tells you to quit the land-grab exactly as the ground becomes valuable — or leaves a page at

0.53% CTR untouched for a month because its rank looked fine.

  • Impressions are not progress. The trap is a page drawing a very large impression base

relative to the rest of the site at 0.2% CTR. It feels like traction and converts nothing.

Rank work by click opportunity from *human* queries.

  • Concentration is the goal, not the problem. One page earning ~45% of clicks is healthy.

Compound the winner before adding breadth.

  • What you double down on is derived, not chosen. Read your own GSC for which *shapes*

win, then repeat the shape — not just the page.

Quick reference

| Question | Short answer | Detail |

|---|---|---|

| Cold start, what now? | Instrument, then land ONE emerging term. Not a cluster. | cold-start |

| Cluster or one page? | One page until the term proves itself. Cluster after. | cold-start |

| How do I find emerging terms? | Your own saved stream → repo/release feeds → social listening → diff vs your pages | emerging-terms |

| Is this term worth it? | Seven gates; failing one drops it, and dropping is normal | emerging-terms |

| What do we double down on? | The page at ≥30% of clicks — and the archetype it belongs to | with-data |

| Ranks well, no clicks? | Screen the query mix first. Most such pages are not broken. | with-data, measurement |

| Traffic plateaued despite publishing | The cluster saturated. Open a new front, don't add pages to it. | with-data §7 |

| When do I stop land-grabbing? | Three computable numbers, never a vibe | measurement |

| How do I stop doing this by hand? | Four loop roles, never one loop | operationalize |

Common mistakes

  • Advising before diagnosing. Get the three numbers first.
  • Treating every low-CTR page as broken. Four query families score ~zero clicks by design;

one verified family ranked position 4.7–5.8 for exactly 0 clicks and was working

perfectly.

  • Publishing on a cadence instead of on evidence.
  • Diversifying away from a winner because concentration feels risky.
  • Grounding research on your own pages and your own GSC. A closed loop. It produces internal

reshuffles that cannot move a term you don't yet own — one property lost its head term to

page 2 this way while the answer sat unread in the owner's own bookmarks.

  • Counting a ship as an outcome. A shipped page is a hypothesis until traffic verifies it.

Scope honesty

Derived from B2B content SEO on low-to-mid authority domains. The two-playbook split, the

measurement discipline, and the emerging-term method transfer broadly. The specific archetypes

in with-data.md do not — they are one property's, and yours must be extracted from your own

data. Local, e-commerce, and YMYL were never tested here; say so rather than extrapolating.

What this skill deliberately does not cover

  • Penalty risk / what Google punishes. Read current third-party research before shipping at

any scale — Lily Ray on algorithmic collapses, Kevin Indig on how AI systems select sources,

Rand Fishkin and Amanda Natividad on zero-click. This skill is about *where to aim*, not about

which tactics are dangerous, and the two are different questions.

  • Writing the page. Use whatever content skill or process you already have. The bar this

method assumes: genuine first-party substance, 15+ concrete specifics, and an answer a

competitor cannot regenerate from the same prompt tomorrow.

  • Keyword expansion mechanics. Once you know the territory, any keyword tool will widen it.

Companion skills in this plugin

  • new-loop — build the scheduled loop that runs either playbook on a cadence. See

references/operationalize.md and assets/seo-loop-template.md.

Other skills for the same job

different authors, same section of the catalogue
Internal Comms
by anthropics
vendor ×13

A set of resources to help me write all kinds of internal communications, using the formats that my company likes to use. Claude should use this skill whenever asked to write some sort of internal communications (status reports, leadership updates, 3P updates, company newsletters, FAQs, incident reports, project updates, etc.).

6k tokens
Competitive Ads Extractor
by frostant
×10

Extracts and analyzes competitors' ads from ad libraries (Facebook, LinkedIn, etc.) to understand what messaging, problems, and creative approaches are working. Helps inspire and improve your own ad campaigns.

2k tokens
Lead Research Assistant
by frostant
×8

Identifies high-quality leads for your product or service by analyzing your business, searching for target companies, and providing actionable contact strategies. Perfect for sales, business development, and marketing professionals.

2k tokens
Developer Growth Analysis
by frostant
×6

Analyzes your recent Claude Code chat history to identify coding patterns, development gaps, and areas for improvement, curates relevant learning resources from HackerNews, and automatically sends a personalized growth report to your Slack DMs.

4k tokens
App Store Optimization
by alirezarezvani
×3

Complete App Store Optimization (ASO) toolkit for researching, optimizing, and tracking mobile app performance on Apple App Store and Google Play Store

55k tokens scripts
Deeptools
by christophacham
×3

NGS analysis toolkit. BAM to bigWig conversion, QC (correlation, PCA, fingerprints), heatmaps/profiles (TSS, peaks), for ChIP-seq, RNA-seq, ATAC-seq visualization.

21k tokens scripts
Pymatgen
by christophacham
×3

Materials science toolkit. Crystal structures (CIF, POSCAR), phase diagrams, band structure, DOS, Materials Project integration, format conversion, for computational materials science.

26k tokens scripts
Enhance Prompt
by google-labs-code
vendor ×2

Transforms vague UI ideas into polished, Stitch-optimized prompts. Enhances specificity, adds UI/UX keywords, injects design system context, and structures output for better generation results.

3k tokens

How to use it

Copy the folder

Take ai-builder-club/seo-growth from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.