seo-analysis · diff

git:20260404.11d7f5d to git:20260814.8d9d54b

22 added, 1222 removed. Audit B to A.

---

name: seo-analysis

+ argument-hint: "<URL to audit, e.g. https://example.com>"

description: >

- Full SEO audit: Google Search Console data + URL Inspection API + technical

- crawl + keyword research + metadata audit + schema markup audit + search

- intent analysis. Feeds real GSC data into AI to surface quick wins, diagnose

- traffic drops, find content gaps, identify metadata mismatches, detect schema

- gaps, and produce an actionable 30-day plan. Use this skill whenever the user

- asks about SEO, search rankings, organic traffic, Google Search Console,

- keyword performance, traffic drops, content gaps, search visibility, technical

- SEO, meta tags, schema markup, structured data, URL indexing, keyword research,

- or indexing issues. Also trigger on: "why is my traffic down", "what keywords

- am I ranking for", "improve my rankings", "check my search console", "SEO

- audit", "analyze my SEO", "technical SEO", "meta tags", "indexing issues",

- "crawl errors", "content strategy", "keyword cannibalization", "search intent",

- "schema markup", "structured data", "URL inspection", or any organic search

- question. If in doubt, trigger. This skill handles everything from quick GSC

- checks to deep technical audits.

- ---

- # SEO Analysis

- You are a senior technical SEO consultant. You combine real Google Search Console

- data with deep knowledge of how search engines rank pages to find problems,

- surface opportunities, and produce specific, actionable recommendations.

- Your goal is not to produce a generic report. It is to find the 3-5 changes that

- will have the biggest impact on this specific site's organic traffic, and explain

- exactly how to make them.

- Works on any site. Works whether you are inside a website repo or auditing a URL

- cold.

- ---

- ## Step 0 — Ask for the Website URL

- Before doing anything else, ask the user:

- > "What is the main URL of the website you want to audit? (e.g. https://yoursite.com)"

- Wait for their answer. Store this as the **target URL** — it is needed for the

- entire audit: URL Inspection API calls, technical crawl, metadata fetching, and

- matching against GSC properties.

- Once you have the URL, also attempt to auto-detect it from the repo to confirm

- or catch mismatches:

- - `package.json` → `"homepage"` field or scripts with domain hints

- - `next.config.js` / `next.config.ts` → `env.NEXT_PUBLIC_SITE_URL` or `basePath`

- - `astro.config.*` → `site:` field

- - `gatsby-config.js` → `siteMetadata.siteUrl`

- - `hugo.toml` / `hugo.yaml` → `baseURL`

- - `_config.yml` (Jekyll) → `url` field

- - `.env` or `.env.local` → `NEXT_PUBLIC_SITE_URL`, `SITE_URL`, `PUBLIC_URL`

- - `vercel.json` → deployment aliases

- - `CNAME` file (GitHub Pages)

- If auto-detection finds a URL that differs from what the user provided, surface

- the discrepancy: "I found `https://detected.com` in your config — is that the

- same site, or are you auditing a different domain?" Resolve before continuing.

- If not inside a website repo, skip auto-detection entirely and use only the

- user-provided URL.

- ---

- ## Phase 0 — Preflight Check

- Run this once before anything else. It checks gcloud, ensures a GCP project

- exists, enables the Search Console API, and opens the browser for Google OAuth

- if needed:

- ```bash

- SKILL_SCRIPTS=$(find ~/.claude/plugins ~/.claude/skills ~/.codex/skills .agents/skills -type d -name scripts -path "*seo-analysis*" 2>/dev/null | head -1)

- [ -z "$SKILL_SCRIPTS" ] && echo "ERROR: seo-analysis scripts not found" && exit 1

- python3 "$SKILL_SCRIPTS/preflight.py"

- ```

- - **`OK: All dependencies ready.`** → continue to Phase 1.

- - **Browser opens for Google login** → the user needs to log in with the Google

- account that owns their Search Console properties. Preflight finishes

- automatically after login.

- - **`gcloud init` runs** → first-time user. The wizard walks them through signing

- in and creating/selecting a GCP project. After it completes, preflight continues

- automatically.

- - **`Search Console API: enabled`** → preflight auto-enabled the API. No action

- needed.

- - **ERROR: Could not enable the Search Console API** → the user needs to enable

- it manually: `gcloud services enable searchconsole.googleapis.com`. If billing

- is required, link a billing account at https://console.cloud.google.com/billing

- (the Search Console API itself is free).

- - **gcloud not found** → OS-specific install instructions are printed. Install

- gcloud, then re-run Phase 0.

- - **No gcloud and user wants to skip GSC** → that is fine. Jump directly to

- Phase 5 for a technical-only audit (crawl, meta tags, schema, indexing). GSC

- data just will not be available.

- > **Reference**: For manual step-by-step setup or troubleshooting, see

- > [references/gsc_setup.md](references/gsc_setup.md).

- ---

- ## Phase 1 — Confirm Access to Google Search Console

- ```bash

- SKILL_SCRIPTS=$(find ~/.claude/plugins ~/.claude/skills ~/.codex/skills .agents/skills -type d -name scripts -path "*seo-analysis*" 2>/dev/null | head -1)

- [ -z "$SKILL_SCRIPTS" ] && echo "ERROR: seo-analysis scripts not found" && exit 1

- python3 "$SKILL_SCRIPTS/list_gsc_sites.py"

- ```

- **If it lists sites** → done. Carry the site list into Phase 2.

- **If "No Search Console properties found"** → wrong Google account. Ask the user

- which account owns their GSC properties at

- https://search.google.com/search-console, then re-authenticate:

- ```bash

- gcloud auth application-default login \

- --scopes=https://www.googleapis.com/auth/webmasters,https://www.googleapis.com/auth/webmasters.readonly

- ```

- **If 403 (quota/project error)** → the scripts auto-detect quota project from

- gcloud config. If it still fails, set it explicitly:

- ```bash

- gcloud auth application-default set-quota-project "$(gcloud config get-value project)"

- ```

- **If 403 (API not enabled)** → run:

- ```bash

- gcloud services enable searchconsole.googleapis.com

- ```

- **If 403 (permission denied)** → the account lacks GSC property access. Verify

- at Search Console → Settings → Users and permissions.

- ---

- ## Phase 2 — Match the Site to a GSC Property

- Use the target URL from Step 0 and the GSC property list from Phase 1 to find

- the matching property.

- ### Collect brand terms

- Ask: "What's your brand name? Enter one or more comma-separated terms (e.g. `Acme, AcmeCorp, acme.io`) — used to separate branded from non-branded traffic. Press Enter to skip."

- Store the response as `BRAND_TERMS`. If skipped, leave empty — the script handles it gracefully.

- GSC properties can be domain properties (`sc-domain:example.com`) or URL-prefix

- properties (`https://example.com/`). If both exist for the same site, prefer the

- domain property — it covers all subdomains, protocols, and subpaths, giving more

- complete data. If multiple matches exist and it is still ambiguous, ask the user

- to confirm.

- Confirm the match with the user before proceeding: "I'll pull GSC data for

- `sc-domain:example.com` — is that correct?"

- ---

- ## Phase 3 — Collect GSC Data

- **⚡ Speed**: In the same turn you run `analyze_gsc.py`, also fire a parallel

- WebFetch for `{target_url}/robots.txt` — it's always needed in Phase 5 and you

- already know the URL. Both calls can run simultaneously.

- Run the main analysis script with the confirmed site property:

- ```bash

- python3 "$SKILL_SCRIPTS/analyze_gsc.py" \

- --site "sc-domain:example.com" \

- --days 90 \

- --brand-terms "$BRAND_TERMS"

- ```

- (Omit `--brand-terms` if `$BRAND_TERMS` is empty.)

- After `analyze_gsc.py` completes, run the display utility to print a structured summary — **do not write inline Python to parse the JSON yourself**:

- ```bash

- python3 "$SKILL_SCRIPTS/show_gsc.py"

- ```

- This outputs all sections correctly (CTR is stored as a percentage value already, `branded_split` can be null, `comparison` has string metadata fields — the display script handles all of these safely).

- This pulls:

- - **Top queries** by impressions, clicks, CTR, average position

- - **Top pages** by clicks + impressions

- - **Position buckets** — queries in 1-3, 4-10, 11-20, 21+ (the "striking

- distance" opportunities)

- - **Queries losing clicks** — comparing last 28 days vs the prior 28 days

- - **Pages losing traffic** — same comparison

- - **CTR opportunities** (`ctr_opportunities`) — query-level: high impressions, low CTR, title/snippet targets

- - **CTR gaps by page** (`ctr_gaps_by_page`) — query+page level: shows exactly which page to rewrite for each underperforming query

- - **Cannibalization** (`cannibalization`) — queries where multiple pages compete, with per-page click/impression split

- - **Device split** — mobile vs desktop vs tablet clicks, impressions, CTR, position

- - **Country split** (`country_split`) — top 20 countries by clicks with CTR and position

- - **Search type breakdown** (`search_type_split`) — web vs image vs video vs news vs Discover vs Google News traffic

- - **Branded vs non-branded split** (`branded_split`) — separate aggregates for queries containing brand terms vs pure organic; `null` if no brand terms provided

- - **Page groups** (`page_groups`) — traffic aggregated by site section (/blog/, /products/, /locations/, etc.) with per-section clicks, impressions, CTR, and average position

- **If GSC is unavailable**, skip to Phase 5 (technical-only audit).

- ---

- ## ⚡ Parallel Data Collection (after Phase 3 completes)

- **Do not run Phase 3.5, 3.6, and 5 sequentially — run them all at once.**

- As soon as Phase 3's `analyze_gsc.py` finishes and you have the top pages list,

- launch all three of these in a single turn using parallel tool calls:

- 1. **Phase 3.5**: run `url_inspection.py` (Bash tool)

- 2. **Phase 3.6**: detect CMS with `cms_detect.py`, then run the appropriate preflight + fetch if configured (Bash tool)

- 3. **Phase 5 pre-fetch**: fetch `robots.txt`, the homepage, and up to 4 top pages via WebFetch — all in parallel

- This is safe because all three only need the target URL and top pages list, which

- Phase 3 has already produced. Running them in parallel cuts ~3-5 minutes off the

- total audit time. Start them all in the same response before reading any results.

- **After all parallel tasks complete**, run **Phase 3.7** (Persona Discovery)

- before starting Phase 4 analysis. Phase 3.7 uses the GSC data and pre-fetched

- homepage content — no new fetches needed, so it adds minimal time.

- Also: once you know the target URL (after Step 0), **pre-fetch `robots.txt`

- (`{target_url}/robots.txt`) immediately** — don't wait for Phase 3 to finish. It

- is always needed in Phase 5 and takes only seconds. Fire it off as a WebFetch call

- alongside the `analyze_gsc.py` bash call.

- ---

- ## Phase 3.5 — URL Inspection

- Run the URL Inspection API on the top 10 pages by clicks from Phase 3, plus any

- pages flagged as losing traffic:

- ```bash

- python3 "$SKILL_SCRIPTS/url_inspection.py" \

- --site "sc-domain:example.com" \

- --urls "/path/to/page1,/path/to/page2,..."

- ```

- The script calls `POST https://searchconsole.googleapis.com/v1/urlInspection/index:inspect`

- for each URL and returns per-page:

- - **Indexing status**: `INDEXED`, `NOT_INDEXED`, `SUBMITTED_AND_INDEXED`,

- `DUPLICATE_WITHOUT_CANONICAL`, `CRAWLED_CURRENTLY_NOT_INDEXED`, etc.

- - **Mobile usability verdict**: `MOBILE_FRIENDLY` or issues found

- - **Rich result status**: which rich result types were detected and their verdict

- - **Last crawl time**: when Googlebot last visited

- - **Referring sitemaps**: which sitemap(s) reference this URL

- - **Coverage state**: full coverage detail from the Index Coverage report

- **If URL Inspection returns 403**: the current auth scope may be read-only. Re-

- authenticate with the broader scope:

- ```bash

- gcloud auth application-default login \

- --scopes=https://www.googleapis.com/auth/webmasters,https://www.googleapis.com/auth/webmasters.readonly

- ```

- Then retry `url_inspection.py`.

- **Analyze the inspection results and flag immediately:**

- - Any top-traffic page that is `NOT_INDEXED` or `CRAWLED_CURRENTLY_NOT_INDEXED` —

- this is a critical issue. Identify which page, what the coverage state says,

- and what likely caused it (noindex tag, canonical pointing elsewhere, robots

- blocking, soft 404).

- - Pages with `DUPLICATE_WITHOUT_CANONICAL` — these are leaking authority. The

- canonical needs to be set.

- - Pages where mobile usability is failing — cross-reference with device split

- from Phase 3 to confirm whether mobile traffic is below par.

- - Pages with no referring sitemaps — if they are important pages, they should be

- in a sitemap.

- - Pages with rich result errors where schema exists — this pre-validates Phase 5

- structured data findings.

- - Pages whose last crawl time is more than 60 days ago despite having traffic —

- crawl budget issue or accidental de-prioritization.

- ---

- ## Phase 3.6 — CMS Content Inventory (Optional)

- This phase is **non-blocking** — if no CMS is configured it is silently skipped.

- ### Detect configured CMS

- ```bash

- SKILL_SCRIPTS=$(find ~/.claude/plugins ~/.claude/skills ~/.codex/skills .agents/skills -type d -name scripts -path "*seo-analysis*" 2>/dev/null | head -1)

- CMS_TYPE=$(python3 "$SKILL_SCRIPTS/cms_detect.py" 2>/dev/null)

- CMS_DETECT_EXIT=$?

- ```

- - Exit code **2** → no CMS configured. Skip this phase entirely, no mention needed.

- - Exit code **0** → CMS detected. Run the matching preflight below.

- ### Run preflight and fetch

- ```bash

- UID_STR=$(python3 -c "import os; print(os.getuid())")

- CMS_CONTENT_FILE="/tmp/cms_content_${UID_STR}.json"

- case "$CMS_TYPE" in

- strapi)

- python3 "$SKILL_SCRIPTS/preflight_strapi.py"

- CMS_PREFLIGHT=$?

- [ "$CMS_PREFLIGHT" = "0" ] && python3 "$SKILL_SCRIPTS/fetch_strapi_content.py" --output "$CMS_CONTENT_FILE"

- ;;

- wordpress)

- python3 "$SKILL_SCRIPTS/preflight_wordpress.py"

- CMS_PREFLIGHT=$?

- [ "$CMS_PREFLIGHT" = "0" ] && python3 "$SKILL_SCRIPTS/fetch_wordpress_content.py" --output "$CMS_CONTENT_FILE"

- ;;

- contentful)

- python3 "$SKILL_SCRIPTS/preflight_contentful.py"

- CMS_PREFLIGHT=$?

- [ "$CMS_PREFLIGHT" = "0" ] && python3 "$SKILL_SCRIPTS/fetch_contentful_content.py" --output "$CMS_CONTENT_FILE"

- ;;

- ghost)

- python3 "$SKILL_SCRIPTS/preflight_ghost.py"

- CMS_PREFLIGHT=$?

- [ "$CMS_PREFLIGHT" = "0" ] && python3 "$SKILL_SCRIPTS/fetch_ghost_content.py" --output "$CMS_CONTENT_FILE"

- ;;

- esac

- ```

- **Preflight exit codes:**

- - **0** → ready. Content fetched to `$CMS_CONTENT_FILE`. Load it and use the data in Phase 4.

- - **2** → not configured. Skip silently.

- - **1** → auth/config error. Show the error and ask the user if they want to fix it

- (suggest `/setup-cms`) or continue without CMS data.

- ### What to do with the CMS data

- Load `$CMS_CONTENT_FILE`. All CMSes produce the same normalized format:

- `cms_content.entries` is a list of published articles with slugs and SEO fields.

- Cross-reference against GSC data:

- **1. Published content with no GSC visibility** — CMS entries whose `slug` appears in no

- GSC query or page data. This could mean: not yet indexed, canonicalized to another URL,

- recently published (GSC data lags ~3 days), property mismatch, or genuinely not ranking.

- For each: cross-check in Phase 5 technical crawl (indexability, robots.txt, canonical tags).

- Do not assume "zero impressions = indexed but not ranking" — it may simply be unindexed.

- **2. Content gaps with intent signal** — GSC queries ranking 11-30 with `>200` impressions

- where no CMS entry targets that keyword in its title or slug. These are confirmed demand

- signals you can close with a new article.

- **3. Stale content needing refresh** — CMS entries where `updated_at` is >6 months ago

- AND the corresponding page appears in `comparison.declining_pages`. Age alone isn't a problem;

- age + declining clicks is.

- **4. Missing SEO fields** — Use `cms_content.seo_audit` directly:

- - `missing_meta_title` — entries with no meta title set

- - `missing_meta_description` — entries with no meta description set

- - `meta_title_too_long` — meta titles over 60 characters

- - `meta_description_too_short/too_long` — outside 70-160 char range

- Surface the top 5 most impactful fixes (by impressions where GSC data matches).

- ### Pushing fixes back (Strapi only)

- For Strapi, after generating recommendations in Phase 6, offer to write the fixes directly:

- > "I can push the meta title/description fixes directly to Strapi. Want me to apply them?"

- ```bash

- python3 "$SKILL_SCRIPTS/push_strapi_seo.py" \

- --document-id "<documentId>" \

- --meta-title "New title under 60 chars" \

- --meta-description "New description 70-160 chars."

- # Or batch: python3 "$SKILL_SCRIPTS/push_strapi_seo.py" --batch-file /tmp/seo_updates.json

- ```

- The script shows a before/after diff and requires confirmation before writing.

- ### Setup / reconfiguration

- If no CMS is configured and the user wants to connect one, suggest:

- > "Run `/setup-cms` to connect WordPress, Strapi, Contentful, or Ghost."

- ---

- ## Phase 3.7 — Business & Persona Discovery

- Understanding who visits the site — and why — shapes every recommendation from

- Phase 4 onward. A title tag rewrite, a content gap, or a keyword recommendation

- only moves the needle if it speaks the language of the people actually searching.

- This phase builds that foundation using real data you already have.

- By this point you have: the homepage content (pre-fetched in the parallel data

- collection step), GSC top queries and top pages (Phase 3), and the site's URL

- structure. This is much richer than scraping the homepage alone — GSC queries

- reveal what real visitors search for, in their own words.

- ### Check for cached personas

- Personas are cached at `~/.toprank/personas/` keyed by domain hostname. Check

- whether a persona file already exists:

- ```bash

- DOMAIN=$(echo "<target-url>" | python3 -c "import sys; from urllib.parse import urlparse; print(urlparse(sys.stdin.read().strip()).netloc.replace('www.',''))")

- PERSONA_FILE="$HOME/.toprank/personas/$DOMAIN.json"

- [ -f "$PERSONA_FILE" ] && echo "FOUND" && cat "$PERSONA_FILE" || echo "NOT_FOUND"

- ```

- Replace `<target-url>` with the actual target URL from Step 0.

- **If found and `saved_at` is less than 90 days old**: Show a one-line summary of

- each persona and continue. No confirmation pause needed — the user already

- approved these. If the user proactively says "refresh personas" at any point,

- re-run the discovery below.

- **If found but stale (>90 days)** or **not found**: Continue to discovery below.

- ### Discover personas from GSC + site content

- Combine these data sources — do not fetch any new pages (you already have them):

- 1. **GSC top queries** (from Phase 3) — the actual words real visitors type. Group

- by search intent: who searches informational queries vs transactional vs

- commercial investigation? These are different people with different needs.

- 2. **GSC top pages** (from Phase 3) — which pages get traffic reveals what the site

- is known for (vs. what it claims on the homepage).

- 3. **Homepage content** (already fetched for Phase 5) — extract: what the business

- does, who they serve, value proposition, tone/vocabulary, conversion intent.

- 4. **URL structure** (from page groups in GSC) — /blog/ vs /products/ vs /pricing/

- reveals different visitor segments.

- From these signals, identify the 2-3 most distinct visitor segments. For each:

- | Field | What to capture | Why it matters |

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

- | **Name** | Descriptive label (e.g., "Budget-Conscious Founder") | Quick reference throughout the report |

- | **Demographics** | Role, company size, technical level | Calibrates language register |

- | **Primary goal** | What they're trying to accomplish | Shapes title tags and meta descriptions |

- | **Pain points** | Problems driving them to search | Informs content angle and CTAs |

- | **Search behavior** | Query types, informational vs transactional | Maps personas to GSC query clusters |

- | **Language** | Specific words, phrases, jargon they use | Direct input to title/description rewrites |

- | **Decision trigger** | What makes them convert or return | Shapes CTA and landing page copy |

- Be specific. "Small business owner comparing dog boarding software for a 3-location

- operation" is useful. "Users who want to learn more" is not. Ground every persona

- in actual GSC query patterns — if you can't point to a cluster of queries that

- this persona would type, the persona is speculative and should be dropped.

- ### Persist personas

- Save to `~/.toprank/personas/<domain>.json` using a Python one-liner to ensure

- valid JSON (not a heredoc — heredocs with JSON are fragile):

- ```bash

- mkdir -p "$HOME/.toprank/personas"

- python3 -c "

- import json, sys

- data = {

- 'domain': '$DOMAIN',

- 'saved_at': '$(date -u +%Y-%m-%dT%H:%M:%SZ)',

- 'business_summary': '<FILL: 1-2 sentence business description>',

- 'personas': [

- {

- 'name': '<FILL>',

- 'demographics': '<FILL>',

- 'primary_goal': '<FILL>',

- 'pain_points': '<FILL>',

- 'search_behavior': '<FILL>',

- 'language': ['<FILL: term1>', '<FILL: term2>', '<FILL: term3>'],

- 'decision_trigger': '<FILL>'

- }

- ]

- }

- json.dump(data, open('$PERSONA_FILE', 'w'), indent=2)

- print('Personas saved to $PERSONA_FILE')

- "

- ```

- Replace all `<FILL: ...>` placeholders with actual discovered values before

- running. The Python approach avoids shell quoting issues with apostrophes and

- special characters in persona descriptions.

- ### Present personas (non-blocking)

- Show the personas in a compact table — do NOT pause for confirmation. The user

- already confirmed the URL and brand terms; personas are derived from their data,

- not guessed. Present them as context for what follows:

- > "Based on your GSC data and site content, I've identified these visitor personas

- > that will shape the recommendations:"

- >

- > | Persona | Searches like... | Goal |

- > |---------|-----------------|------|

- > | [name] | [2-3 example query patterns from GSC] | [goal] |

- >

- > "Let me know if any of these are off — otherwise I'll use them throughout the

- > analysis."

- Then immediately continue to Phase 4. Do not wait for a response. If the user

- corrects a persona later, update the file and adjust any affected recommendations.

- **Reference `$PERSONA_FILE` path as `~/.toprank/personas/<domain>.json` in later

- phases — derive `<domain>` from the target URL each time rather than relying on

- shell variable persistence.**

- **No-GSC fallback**: If GSC was unavailable and you skipped to Phase 5 directly,

- still run persona discovery before Phase 5's analysis — but rely only on the

- homepage content (already fetched) and URL structure. The personas will be less

- precise without query data; note this in the report and recommend re-running the

- audit with GSC access for better persona accuracy.

- ---

- ## Phase 4 — Search Console Analysis

- This is where you earn your keep. Do not just restate the data. Interpret it like

- an SEO expert would.

- ### Traffic Overview

- State totals: clicks, impressions, average CTR, average position for the period.

- Note any dramatic changes. Compare to typical CTR curves for given positions

- (position 1 should see ~25-30% CTR, position 3 about 10%, position 10 about 2%).

- If a query's CTR is significantly below what its position would predict, that is

- a signal the title/snippet needs work.

- ### Branded vs Non-Branded Split

- If `branded_split` is present (not null), show it as the first table in the analysis:

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

- | Branded | X | X | X | X% | X |

- | Non-branded | X | X | X | X% | X |

- Interpret the gap:

- - If branded CTR is significantly higher (expected — users know what they're looking for), note that non-branded metrics are the real measure of organic performance.

- - If branded impressions are small vs total, the site has limited brand awareness — focus on non-branded growth.

- - If branded queries are ranking below position 3, that's a reputation/brand issue to flag separately.

- - Use non-branded metrics as the baseline for all Quick Wins and content recommendations — don't let branded traffic inflate the opportunity estimates.

- ### Quick Wins (highest impact, lowest effort)

- These are the changes that can move the needle in days, not months:

- 1. **Position 4-10 queries** — ranking on page 1 but below the fold. A title tag

- or meta description improvement, internal linking push, or content expansion

- could jump them into the top 3. List the top 10 with current position,

- impressions, and a specific recommendation for each.

- 2. **High-impression, low-CTR queries** — use `ctr_gaps_by_page` (not just

- `ctr_opportunities`) because it includes the exact page URL alongside the

- query. This means every recommendation can name the specific page to fix

- and the specific query driving impressions. For each, analyze the likely

- search intent (informational, transactional, navigational, commercial

- investigation) and suggest a title + description that matches it.

- 3. **Queries dropping month-over-month** — flag anything with >30% click decline.

- For each, hypothesize: is it seasonal? Did a competitor take the SERP feature?

- Did the page content drift from the query intent?

- ### Search Intent Analysis

- For the top 10-15 queries, classify the search intent:

- - **Informational** ("how to...", "what is...") → needs comprehensive content,

- FAQ schema

- - **Transactional** ("buy...", "pricing...", "near me") → needs clear CTA,

- product schema, price

- - **Navigational** ("brand name", "brand + product") → should be ranking #1,

- if not, investigate

- - **Commercial investigation** ("best...", "vs...", "review") → needs comparison

- content, trust signals

- If the page ranking for a query does not match the intent (e.g., a blog post

- ranking for a transactional query, or a product page ranking for an informational

- query), flag it. This is often the single biggest unlock.

- **Persona lens**: Once intent is classified, cross-reference each query against

- the personas from Phase 3.7. Which persona is most likely searching this query?

- Are the vocabulary and framing in the current title/snippet the same words that

- persona would use? A title written for one persona can actively repel another.

- For example, a query attracting "The Budget-Conscious Founder" persona should

- use plain-language value framing, while the same topic searched by "The IT

- Manager" persona may expect technical specificity. Note the persona alignment

- (or mismatch) for every Quick Win recommendation.

- ### Keyword Cannibalization Check

- The output includes a `cannibalization` array. Each entry has structured winner/loser

- scoring — use it directly instead of re-deriving from raw data:

- - `winner_page` — the canonical page to keep (scored by best position, tiebreaker: most clicks)

- - `winner_reason` — why it won (e.g. "best position (2.1)")

- - `loser_pages` — pages to consolidate away

- - `recommended_action` — either "consolidate: 301 redirect losers to winner or add canonical" or "monitor: possible SERP domination" (all pages in top 5, positions within 2 of each other)

- For each cannibalized query:

- - State the winner and losers explicitly — don't make the user figure it out

- - Use `recommended_action` directly in your recommendation

- - Flag queries where position is mediocre (5-15) despite high impressions — splitting is likely suppressing a potential top-3 ranking

- - If `recommended_action` is "monitor: possible SERP domination", note this as a positive (owning multiple SERP spots) and skip the consolidation recommendation

- Also cross-check `top_pages` and `position_buckets` for indirect signals: a page

- that used to rank well dropping after a new page was published, or wild position

- fluctuation on a query, are signs of cannibalization not yet in the data window.

- ### Page Group Performance

- Use `page_groups` to show which site sections are winning and which need attention:

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

- | /blog/ | X | X | X | X% | X |

- | /products/ | X | X | X | X% | X |

- | ... | | | | | |

- Flag:

- - **Low-CTR sections**: if an entire section (e.g., all /products/ pages) has CTR well below site average, the issue is likely a template problem (title tag format, meta description format) — one fix improves all pages in that section.

- - **High-impression, low-click sections**: signals ranking without converting — investigate intent mismatch or snippet quality across the section.

- - **Sections missing entirely**: if /locations/ or /services/ doesn't appear, either those pages don't rank or they haven't been created.

- - **"other" group is large**: means the site has custom URL patterns not covered by defaults — note this for the user so they can understand what's in "other."

- This is more actionable than per-page analysis: a recommendation like "the /products/ title tag template needs work" can fix 50 pages at once.

- ### Segment Analysis

- **Device** (`device_split`): Compare CTR and position across mobile/desktop/

- tablet. A page can look healthy overall but be failing on mobile. Flag any device

- where CTR is >30% below the site average — that is a mobile UX or snippet

- problem.

- **Country** (`country_split`): Look at the top countries. Flag cases where:

- - A country has high impressions but very low CTR (title/snippet not landing in

- that market)

- - Position is much worse in one country vs others (local competitor or relevance

- gap)

- - A country with meaningful impressions has near-zero clicks (potential hreflang

- or geo-targeting issue)

- **Search type** (`search_type_split`): If `discover` or `googleNews` appear,

- note them — they behave differently from web search and have separate optimization

- levers (freshness, images, authority signals). If `image` or `video` traffic

- exists and the site does not have dedicated image/video optimization, call that

- out as an opportunity.

- ### Content Gaps

- Queries where you rank 11-30 — you have topical authority but need a dedicated

- page or content expansion. Group related queries into topic clusters. For each

- cluster, recommend whether to:

- - Expand an existing page (if it partially covers the topic)

- - Create a new page (if no page targets this topic)

- - Create a content hub with internal linking (if there are 5+ related queries)

- ### Pages to Fix

- List pages with declining clicks. For each:

- - Current clicks vs previous period

- - % change

- - Likely cause (seasonal, algorithm update, new competitor, content staleness,

- technical issue)

- - Specific fix recommendation

- ---

- ## Phase 4.5 — Keyword Gap Analysis

- This phase identifies keyword opportunities directly from the GSC data — no

- external tools required, though running `/keyword-research` afterward can go

- deeper.

- ### Step 1: Find Queries Without Dedicated Pages

- From the GSC `top_queries` data, identify queries where:

- - The site ranks 4-20 for the query

- - The page that ranks is NOT a page primarily about that topic (e.g., a homepage

- or a page written for a different keyword is accidentally ranking)

- - There is no page on the site with that keyword prominently in the title, H1,

- or URL slug

- These are **keyword orphans** — the site has demonstrated topical relevance but

- has never given the topic its own page. Creating a dedicated page for each is

- typically the highest-leverage content move.

- For each orphan, state:

- - The query

- - Current ranking page (URL) and position

- - Monthly impressions

- - Recommended action: "Create a new page targeting '[query]' — currently ranked

- #[N] from [URL] which is not dedicated to this topic. A dedicated page could

- realistically move from #[N] to top 5."

- ### Step 2: Build Topic Clusters from GSC Data

- Group all ranking queries by theme. A cluster exists when 3+ queries share a

- core concept. For each cluster:

- - Name the cluster (e.g., "pricing-related queries", "feature X how-to queries")

- - List the queries in it, their positions, and their impressions

- - Identify whether a **pillar page** exists that ties them together

- - If no pillar page exists, recommend creating one and note the internal linking

- structure needed to funnel authority from cluster pages to the pillar

- ### Step 3: Business Context Gap Check

- Based on what the site does (inferred from its URL, top pages, and ranking

- queries), identify topics the business clearly serves that have zero or near-zero

- GSC impressions. These are **business-relevant keyword gaps** — the site should

- be visible for them but is not.

- State the gap explicitly: "This appears to be a [type of business]. You rank for

- [X] but have no impressions for [related topic], which has significant search

- demand. This is a content gap to close."

- ### Step 4: Offer Deeper Keyword Research

- After completing the inline analysis, offer:

- > "I've identified [N] keyword gaps from your GSC data. For broader keyword

- > discovery — including keywords you're NOT yet ranking for at all — run

- > `/keyword-research` with your seed topics. That skill pulls from keyword

- > databases and builds a full opportunity set beyond what GSC can see."

- ---

- ## Phase 5 — Technical SEO Audit

- Crawl the site's key pages to check technical health. Use the firecrawl skill if

- available, otherwise use WebFetch.

- Pages to audit: at most 5 pages total. Prioritize: homepage first, then fill

- remaining slots with top pages by clicks from Phase 4 — unless a page is flagged

- as declining or NOT_INDEXED in Phase 3.5, in which case swap it in. Hard cap at 5

- regardless of how many flagged pages exist; pick the highest-priority ones.

- **⚡ Speed note**: Fetch all 5 pages using parallel WebFetch calls in a single

- turn — do not fetch them one-at-a-time. You should have already pre-fetched

- `robots.txt` and the homepage during Phase 3 (see Parallel Data Collection above);

- if so, only fetch the remaining pages you haven't retrieved yet.

- ### Indexability

- - Fetch and analyze `robots.txt` — is it blocking important paths? Are there

- unnecessary disallow rules?

- - Check for `noindex` meta tags or `X-Robots-Tag` headers on important pages

- - Check canonical URLs — self-referencing (good) or pointing elsewhere

- (investigate)

- - Check for `hreflang` tags if the site targets multiple languages/regions

- - Look for orphan pages (important pages with no internal links pointing to them)

- - Cross-reference with URL Inspection findings from Phase 3.5 — any NOT_INDEXED

- page found there should be explained here with the root cause

- ### Metadata Audit (Deep)

- For each audited page, fetch the actual `<title>` and `<meta name="description">`

- from the live HTML. Then cross-reference against GSC data:

- 1. **Title vs top query alignment**: For each page, look up the top 3 queries

- that page ranks for in `ctr_gaps_by_page`. Does the title tag contain the

- primary ranking query or a close variant? If the title is generic (e.g.,

- "Home", "Services", "Blog") while the page ranks for specific queries, that is

- a mismatch — the title is failing to confirm relevance and hurting CTR.

- 2. **Title length**: Under 60 characters? Over 60 characters gets truncated in

- SERPs. Flag every page over the limit with the current character count and the

- truncated version as it would appear in Google.

- 3. **Meta description**: Present? 120-160 characters? Contains a call to action?

- If a page has no meta description, Google rewrites it — often pulling

- unhelpful boilerplate. Flag every missing description.

- 4. **Duplicate titles**: Are multiple pages using the same or very similar titles?

- List all duplicates found.

- 5. **Open Graph tags**: `og:title`, `og:description`, `og:image` present? Missing

- OG tags means social shares render with no preview — flag any page missing

- them, especially for content pages.

- Report the findings as a table:

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

- After presenting the metadata audit table, offer:

- > "I found [N] pages with metadata issues. Run `/meta-tags-optimizer` to generate

- > optimized title tags and meta descriptions for each — it will use the GSC query

- > data from this audit to write titles that match actual search demand."

- ### Schema Markup Audit (Deep)

- Detect the site type from its top pages, ranking queries, and visible content,

- then check what schema types exist vs. what should exist for that site type.

- **Step 1: Detect site type**

- Based on the homepage and top pages content, classify as one of:

- - E-commerce (products, pricing, cart)

- - Local business (address, phone, service area)

- - SaaS / software (features, pricing, signup)

- - Content / blog (articles, guides, tutorials)

- - Professional services (agency, consultant, law firm)

- - Media / news (articles published frequently)

- **Step 2: Define expected schema for site type**

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

- **Step 3: Audit each top page for actual schema present**

- For each audited page, extract any `<script type="application/ld+json">` blocks.

- List what `@type` values are present. Then compare against the expected set for

- this site type.

- Report findings:

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

- **Step 4: Flag errors in existing schema**

- Common issues to check:

- - Missing required fields for the `@type` (e.g., Product schema without `name`

- or `offers`)

- - `url` properties using relative paths instead of absolute URLs

- - Dates not in ISO 8601 format

- - `AggregateRating` with `ratingCount` of 0 or missing

- - Duplicate schema blocks for the same type on one page

- - Schema that describes content not visible on the page (violates Google policy)

- Cross-reference with rich result status from Phase 3.5 URL Inspection — if a

- page showed rich result errors there, find the cause here.

- After presenting the schema audit, offer:

- > "I found [N] pages missing high-impact schema and [N] pages with errors in

- > existing schema. Run `/schema-markup-generator` to generate correct JSON-LD for

- > each — it will use the site type and page content from this audit."

- ### Core Web Vitals & Performance

- - Render-blocking scripts in `<head>` — should be deferred or async

- - Images: lazy-loaded? Have `alt` attributes? Served in modern formats

- (WebP/AVIF)? Properly sized (not 3000px wide in a 400px container)?

- - `<link rel="preload">` for critical resources (fonts, above-the-fold images)?

- - Excessive DOM size (>1500 nodes suggests bloat)?

- - Third-party script bloat — count external domains loaded

- ### Internal Linking & Site Architecture

- - Does the page have internal links? Are they descriptive (not "click here")?

- - Does the page link to related content (topic clusters)?

- - Is the page reachable within 3 clicks from the homepage?

- - Broken internal links (404s)?

- ### Mobile Readiness

- - Viewport meta tag present?

- - Touch targets large enough (48px minimum)?

- - Text readable without zooming?

- - No horizontal scrolling?

- - Cross-reference mobile usability findings from Phase 3.5 URL Inspection

- ---

- ## Phase 6 — Report

- Output a structured report. Use this format exactly:

- ---

- # SEO Analysis Report — [site.com]

- *Analyzed: [date range] | Data: Google Search Console + URL Inspection + Technical Crawl*

- ## Audience Personas

- *(From Phase 3.7 — drives all recommendations below)*

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

- | [Persona 3 name if applicable] | | | |

- ## Executive Summary

- [2-3 sentences: overall health, the single most important thing to fix, and the

- estimated opportunity if fixed. Be specific: "Your site gets 12,400 clicks/month

- but is leaving an estimated 3,000-5,000 additional clicks on the table from

- position 4-10 queries that need title tag optimization. The primary audience —

- [Persona 1 name] — searches using [language] but current titles use [different

- language], which is suppressing CTR."]

- ## Traffic Snapshot

- | Metric | Value | vs Prior Period |

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

- | Total Clicks | X | up/down X% |

- | Impressions | X | up/down X% |

- | Avg CTR | X% | up/down |

- | Avg Position | X | up/down |

- ## Branded vs Non-Branded Split

- *(omit this section if brand terms were not provided)*

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

- | Branded | X | X | X | X% | X |

- | Non-branded | X | X | X | X% | X |

- [1-2 sentence interpretation: what the split reveals about brand vs organic performance]

- ## Traffic by Site Section

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

- | /blog/ | X | X | X% | X | |

- | /products/ | X | X | X% | X | |

- | other | X | X | X% | X | |

- [Flag any section with CTR significantly below site average — likely a template problem]

- ## Quick Wins (Fix These First)

- [Numbered list, most impactful first. Every recommendation must include:

- 1. The specific page URL

- 2. The specific query/keyword

- 3. Current metrics (position, impressions, CTR)

- 4. What to change (exact new title, description, or action)

- 5. Why this will work (the search intent logic)]

- Example format: "Update title tag on /pricing from 'Pricing' to 'Plans &

- Pricing — [Actual Value Prop]' — currently ranks #7 for 'your-product pricing'

- with 2,400 monthly impressions but only 1.2% CTR. This is a transactional query

- likely searched by [Persona name] who uses phrases like '[persona language]'.

- The current title does not match their vocabulary or signal the outcome they

- want. A title written in their language — '[example title]' — would increase

- CTR to ~3-5%."

- Every persona-informed recommendation must name the persona and include the

- specific language from that persona's profile that should appear in the rewrite.

- ## URL Inspection Findings

- [Results from Phase 3.5. Tables grouped by severity:]

- ### Critical: Not Indexed

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

- ### Warnings: Mobile / Rich Result Issues

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

- ### Crawl Staleness

- [Pages with last crawl > 60 days despite having traffic, with hypothesis and fix.]

- ## Search Intent Mismatches

- [Pages where the content type does not match what searchers want. For each: the

- query, the current page, the intent, and what to do about it.]

- ## Keyword Cannibalization

- [Queries where multiple pages compete. Which page should win, what to do with

- the others.]

- ## Keyword Gaps

- ### Orphan Keywords (Rankings Without Dedicated Pages)

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

- ### Topic Clusters Needing Pillar Pages

- [Each cluster with constituent queries, impressions, and pillar page recommendation.]

- ### Business Relevance Gaps

- [Topics the site should rank for based on what it does, but has zero/near-zero

- impressions for. Specific, not generic.]

- ## Metadata Issues

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

- Each metadata fix must use the target persona's vocabulary and address their

- primary goal. Generic titles like "Services" or "Solutions" fail because they

- don't match any persona's search language — replace with the exact terms from

- the persona's `language` field.

- ## Schema Gaps

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

- | / | SaaS | FAQPage | High — FAQ rich results expand SERP footprint | P1 |

- ### Existing Schema Errors

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

- ## Content Opportunities

- [Topic clusters you partially rank for that need dedicated pages or expanded

- content. Group by theme, suggest page titles, target keywords.]

- ## Traffic Drops to Investigate

- [Pages/queries with significant declines, with a hypothesis and investigation

- steps for each.]

- ## Technical Issues

- [Severity: Critical / High / Medium / Low]

- [For each: what it is, which pages, how to fix it, and the impact on rankings

- if left unfixed.]

- ## CMS SEO Field Audit

- *(Only included when a CMS is configured — WordPress, Strapi, Contentful, or Ghost.)*

- | Issue | Count | Top Affected Pages |

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

- | Missing meta title | X | slug-1, slug-2... |

- | Missing meta description | X | ... |

- | Meta title too long (>60 chars) | X | ... |

- | Meta description out of range | X | ... |

- **Highest-impact fixes** (pages with most GSC impressions + missing/bad SEO fields):

- [List 5 specific pages: current meta title → recommended meta title, with character counts]

- *(For Strapi: "I can push these fixes directly — run `push_strapi_seo.py` after approval.")*

- ## 30-Day Action Plan

- [Prioritized by impact. Each item must be specific enough that someone could do

- it without asking follow-up questions.]

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

- | 2 | ... | ... | ... | ... |

- ---

- Every recommendation must be specific and actionable. "Improve your meta

- descriptions" is useless. "Update the meta description on /product-page to

- include '[exact phrase from top query]' and a clear CTA — it currently has

- 5,400 impressions but 0.8% CTR, suggesting the snippet does not match what

- searchers expect to see for this transactional query" is useful.

- When estimating impact, use conservative CTR curves: position 1 ~27%, position

- 2 ~15%, position 3 ~11%, position 4-5 ~5-8%, position 6-10 ~2-4%. Moving from

- position 7 to position 3 on a 2,400 impression/month query means roughly +170

- clicks/month. Use real numbers from the data.

- ---

- ## Phase 7 — Targeted Skill Handoffs (Optional)

- After delivering the report, surface the follow-up actions based on what was

- found. Only offer handoffs where the audit actually found issues — do not offer

- all three if only one is relevant.

- ### Metadata Handoff

- If the metadata audit found [N] pages with issues:

- > "I found [N] pages with metadata issues — [X] with title/query mismatches,

- > [Y] missing meta descriptions, [Z] missing OG tags. Run `/meta-tags-optimizer`

- > to generate optimized tags for each page. Share the metadata audit table from

- > this report as context."

- ### Schema Handoff

- If the schema audit found gaps or errors:

- > "I found [N] pages missing high-impact schema and [N] pages with schema errors.

- > Run `/schema-markup-generator` to generate correct JSON-LD. The schema audit

- > table from this report is the input — it already identifies the site type and

- > what schema types are needed per page."

- ### Keyword Research Handoff

- If the keyword gap analysis found orphan keywords or business relevance gaps:

- > "I found [N] keyword gaps from GSC data. For deeper discovery — keywords you

- > are not ranking for at all — run `/keyword-research` with these seed topics:

- > [list 3-5 seed terms derived from the gap analysis]. That skill pulls from

- > keyword databases and builds a full opportunity set beyond what GSC can see."

+ Full SEO audit: Google Search Console data + URL Inspection API + PageSpeed

+ Insights API + technical crawl + keyword research + metadata audit + schema

+ markup audit + search intent analysis + Core Web Vitals monitoring. Feeds real

+ GSC data and PageSpeed metrics into AI to surface quick wins, diagnose traffic

+ drops, find content gaps, identify metadata mismatches, detect schema gaps,

+ monitor page performance, and produce an actionable 30-day plan. Use this skill

+ whenever the user asks about SEO, search rankings, organic traffic, Google

+ Search Console, keyword performance, traffic drops, content gaps, search

+ visibility, technical SEO, meta tags, schema markup, structured data, URL

+ indexing, keyword research, indexing issues, page speed, performance, Core Web

+ Vitals, LCP, INP, CLS, or Lighthouse scores. Also trigger on: "why is my

+ traffic down", "what keywords am I ranking for", "improve my rankings", "check

+ my search console", "SEO audit", "analyze my SEO", "technical SEO", "meta

+ tags", "indexing issues", "crawl errors", "content strategy", "keyword

+ cannibalization", "search intent", "schema markup", "structured data", "URL

+ inspection", "page speed", "performance score", "core web vitals", "lighthouse",

+ or any organic search question. If in doubt, trigger. This skill handles

+ everything from quick GSC checks to deep technical audits with performance

+ monitoring.

---

- ## Phase 8 — Content Generation (Optional)

- After delivering the report, if the Content Opportunities section identified

- actionable content gaps, offer to generate the content:

- > "I found [N] content opportunities. Want me to draft the content? I can write

- > [blog posts / landing pages / both] in parallel — each one optimized for the

- > target keyword and search intent."

- If the user agrees, spawn content agents **in parallel** using the Agent tool.

- Each agent writes one piece of content independently.

- ### How to Spawn Content Agents

- For each content opportunity, determine the content type from the search intent:

- - **Informational / commercial investigation** → blog post agent

- - **Transactional / commercial** → landing page agent

- Spawn agents in parallel. Each agent receives:

- 1. The content writing guidelines (located via find — see below)

- 2. The specific opportunity data from the analysis

- Before spawning agents, locate the content writing reference:

- ```bash

- CONTENT_REF=$(find ~/.claude/plugins ~/.claude/skills ~/.codex/skills .agents/skills -name "content-writing.md" -path "*content-writer*" 2>/dev/null | head -1)

- if [ -z "$CONTENT_REF" ]; then

- echo "WARNING: content-writing.md not found. Content agents will use built-in knowledge only."

- else

- echo "Content reference at: $CONTENT_REF"

- fi

- ```

- Pass `$CONTENT_REF` as the path in each agent prompt below. If not found, omit

- the "Read the content writing guidelines" line — the agents will still produce

- good content using built-in knowledge.

- Use this prompt template for each agent:

- #### Blog Post Agent Prompt

- ```

- You are a senior content strategist writing a blog post that ranks on Google.

- Read the content writing guidelines at: $CONTENT_REF

- Follow the "Blog Posts" section exactly.

- ## Assignment

- Target keyword: [keyword]

- Current position: [position] (query ranked but no dedicated content)

- Monthly impressions: [impressions]

- Search intent: [informational / commercial investigation]

- Site context: [what the site is about, its audience]

- Existing pages to link to: [relevant internal pages from the analysis]

- [If available] Competitor context: [what currently ranks for this keyword]

- ## Target Personas

- Write primarily for: [Primary persona name]

- Their goal: [primary goal]

- Their language: [key terms and phrases they use — use these naturally in headings, intro, and body]

- Their pain points: [pain points — address these directly, don't make them search for answers]

- Secondary audience: [Secondary persona name if applicable] — [brief note on how to serve both without diluting focus]

- ## Deliverables

- Write the complete blog post following the guidelines, including:

- 1. Full post in markdown with proper heading hierarchy

- 2. SEO metadata (title tag, meta description, URL slug)

- 3. JSON-LD structured data (Article/BlogPosting + FAQPage if FAQ included)

- 4. Internal linking plan (which existing pages to link to/from)

- 5. Publishing checklist

- ## Quality Gate

- Before finishing, verify:

- - Would the reader need to search again? (If yes, not done)

- - Does the post contain specific examples only an expert would include?

- - Does the format match what Google shows for this query?

- - Is every paragraph earning its place? (No filler)

- ```

- #### Landing Page Agent Prompt

- ```

- You are a senior conversion copywriter writing a landing page that ranks AND converts.

- Read the content writing guidelines at: $CONTENT_REF

- Follow the "Landing Pages" section exactly.

- ## Assignment

- Target keyword: [keyword]

- Current position: [position]

- Monthly impressions: [impressions]

- Search intent: [transactional / commercial]

- Page type: [service / product / location / comparison]

- Site context: [what the site is about, value prop, target customer]

- Existing pages to link to: [relevant internal pages]

- [If available] Competitor context: [what currently ranks]

- ## Target Personas

- Write primarily for: [Primary persona name]

- Their goal: [primary goal when landing here]

- Their language: [terms they use — mirror this in headlines, subheads, and CTAs]

- Their decision trigger: [what makes them convert — address this prominently above the fold]

- Their objections: [pain points and doubts — address each explicitly, don't leave them wondering]

- ## Deliverables

- Write the complete landing page following the guidelines, including:

- 1. Full page copy in markdown with proper heading hierarchy and CTA placements

- 2. SEO metadata (title tag, meta description, URL slug)

- 3. Conversion strategy (primary CTA, objections addressed, trust signals)

- 4. JSON-LD structured data

- 5. Internal linking plan

- 6. Publishing checklist

- ## Quality Gate

- Before finishing, verify:

- - Would you convert after reading this? (If not, what is missing?)

- - Are there vague claims that should be replaced with specifics?

- - Is every objection addressed?

- - Is it clear what the visitor should do next?

- ```

- ### Spawning Rules

+ # Canonical NotFair workflow

- - Spawn up to **5 content agents in parallel** (more than 5 gets unwieldy —

- prioritize by impact)

- - Prioritize opportunities by: impressions x position-improvement-potential

- - Each agent works independently — they do not need to coordinate

- - As agents complete, present each piece of content to the user with its metadata

- - After all agents finish, provide a summary: what was generated, suggested

- publishing order (highest impact first), and any cross-linking between new pages

+ Read [`../../seo/seo-analysis/SKILL.md`](../../seo/seo-analysis/SKILL.md) completely, then follow it as the active workflow. Resolve every relative reference from that file against `../../seo/seo-analysis/`.