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80 changes: 80 additions & 0 deletions .agents/skills/competitive-landscape/SKILL.md
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---
name: competitive-landscape
description: Map SEO market leaders, winning content themes, keyword coverage, backlinks, and strategic gaps.
---

# OpenSEO Competitive Landscape

## Goal

Answer: "Who is winning this SEO market, what content is working for them, and where are the openings?"

Use this when the user wants a market-level view across several competitors. For a deep dive on one domain, use `competitor-analysis`.

## Required inputs

- `projectId`
- Topic, seed keywords, market/category, or user's domain
- Optional known competitors
- Optional location/language

## OpenSEO MCP tools

- `research_keywords`: discover representative market queries.
- `get_keyword_metrics`: validate known query sets with volume, difficulty, intent, and trends.
- `get_serp_results`: identify recurring ranking domains across target queries.
- `find_serp_competitors`: compare domains competing across supplied keywords; use this before manual SERP counting when a keyword set is available.
- `get_domain_overview`: size organic footprint for candidate leaders.
- `get_search_console_performance`: when the user's own domain is in the comparison and Search Console is connected, anchor their position with first-party clicks/impressions/CTR rather than third-party estimates.
- `get_ranked_keywords`: find exact ranking keywords, URLs, ranks, intents, and SERP result types for leaders.
- `get_backlinks_overview`: compare backlink/referring-domain strength where relevant.
- `search_local_businesses`, `get_local_serp_results`, and `get_google_business_questions`: use for local SEO markets where proximity, Maps rankings, business categories, reviews, or Google Q&A affect who is winning.

## Workflow

1. Define the market query set:
- Use provided keywords, or call `research_keywords` to build 5-10 representative queries.
- Include mixed intent: informational, commercial, comparison, and tool/software terms when applicable.
- For local SEO, include neighborhood/city/service-area queries and identify the priority locations or coordinates.
2. If the query set is already known, use `get_keyword_metrics` to validate relative demand and difficulty and `find_serp_competitors` to identify recurring domains at scale.
3. For local SEO, call `search_local_businesses` and `get_local_serp_results` for the highest-priority location(s) before synthesizing winners. Use `get_serp_results` as a complement for organic pages, not as the only local evidence.
4. Call `get_serp_results` for representative queries when live SERP composition, ranking URLs, or SERP features need inspection. Send at most 10 queries per call.
5. Identify recurring domains and group them by type:
- Direct product competitors
- Publishers/media
- Marketplaces/directories
- Communities/forums
- Documentation/resources
6. For the strongest recurring domains, call `get_domain_overview`; default to the top 3-5 domains before expanding.
7. For direct competitors and relevant publishers, call `get_ranked_keywords`.
8. Use `get_backlinks_overview` when backlink authority appears important or the user asks why a domain is winning. Backlinks may be unavailable if the account has not enabled that data; continue with SERP/domain evidence if it fails.
9. Synthesize patterns: content types, themes, SERP formats, local-pack signals, authority advantages, and underserved angles.

## Output format

Start with the market read:

- Market leaders
- Most winnable opportunity area
- Biggest barrier to ranking

Then include:

| Domain | Type | Why they matter | Organic footprint | Winning themes | Weakness/gap |
| ------ | ---- | --------------- | ----------------- | -------------- | ------------ |

Add:

- Query set used
- Content formats that are working
- Keyword/theme gaps
- Backlink or authority observations
- Recommended next workflows: competitor analysis, keyword clustering, or content brief

## Guardrails

- Distinguish SEO competitors from business competitors.
- Do not overstate exact traffic when OpenSEO returns estimates.
- If using a small query set, call the result directional.
- Do not assume a publisher is a product competitor; label domain types clearly.
- For local markets, distinguish organic-page winners from Maps/local-pack winners.
83 changes: 83 additions & 0 deletions .agents/skills/competitor-analysis/SKILL.md
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---
name: competitor-analysis
description: "Analyze one competitor's organic footprint, ranking keywords, content themes, backlinks, and gaps."
---

# OpenSEO Competitor Analysis

## Goal

Analyze one competitor deeply enough to decide what to learn from, avoid, counter-position against, or outrank.

Use this for a named competitor. For identifying the market leaders first, use `competitive-landscape`.

## Required inputs

- `projectId`
- Competitor domain
- User's domain when comparison is requested
- Optional topic/category/location/language

## OpenSEO MCP tools

- `get_domain_overview`: baseline organic traffic and keyword count.
- `get_search_console_performance`: when comparing to the user's own domain and Search Console is connected, use it as the first-party baseline (real clicks/impressions/CTR/position) instead of estimating the user's own performance from third-party data.
- `get_ranked_keywords`: exact keyword, URL, rank, intent, traffic, CPC, and SERP-type rows for the competitor domain or page.
- `get_backlinks_overview`: backlink/referring-domain profile.
- `find_serp_competitors`: validate whether the named competitor is a real search competitor across the target keyword set.
- `search_local_businesses`, `get_local_serp_results`, and `get_google_business_questions`: use for local SEO competitors when Maps/local-pack visibility, nearby businesses, categories, or Google Q&A matter.
- `get_serp_results`: validate direct head-to-head SERPs for important keywords.
- `research_keywords`: expand gaps or category terms when needed.

## Workflow

1. Call `get_domain_overview` for the competitor, passing provided location/language when supported.
2. If comparing to the user, call `get_domain_overview` for the user's domain too — and if Search Console is connected, `get_search_console_performance` for the user's real baseline.
3. Call `get_ranked_keywords` for the competitor. Use filters like `maxRank`, `minSearchVolume`, `excludeBrandTerms`, and `resultTypes` to keep rows relevant.
4. If comparing to the user, call `get_ranked_keywords` for the user's domain/page too, or use `get_serp_results` for the shared terms when a lighter check is enough.
5. For local SEO, use `search_local_businesses` and `get_local_serp_results` around the relevant business location(s) before drawing local-pack conclusions. Add `get_google_business_questions` only when Q&A evidence matters.
6. Use `find_serp_competitors` when the competitor was supplied by the user but its search overlap is unclear.
7. Group competitor keywords into themes:
- Product/category terms
- Alternatives/comparisons
- Templates/tools/calculators
- Educational guides
- Branded demand
- Local/neighborhood terms when relevant
8. Call `get_backlinks_overview` for the competitor, especially if authority appears to explain rankings. Continue without backlink evidence if it is unavailable.
9. Use `get_serp_results` for important shared or target keywords to compare positioning, passing provided location/language when supported.
10. Produce an actionable plan:
- What they are doing well
- Where they are vulnerable
- Which pages/keywords to pursue
- What to avoid copying

## Output format

Start with:

- Competitor snapshot
- Biggest lesson
- Best opportunity to beat them

Then include:

| Area | Competitor pattern | Evidence | OpenSEO opportunity |
| ---- | ------------------ | -------- | ------------------- |

Include sections for:

- Top keyword themes
- Content/page types working for them
- Backlink/authority notes
- Head-to-head SERP observations
- Priority actions for the user

## Guardrails

- Do not treat all competitor keywords as desirable. Filter for business fit.
- Separate evidence from inference.
- Do not infer competitor page/content-type patterns from keyword rows alone; use SERP or web evidence for page-level claims.
- For local SEO, do not infer Maps/local-pack strength from national organic domain metrics alone; use local business and local SERP tools when the location is known or reasonably discoverable.
- Do not recommend copying content; recommend a stronger angle or better answer to the same intent.
- If the user's domain is unavailable, frame the analysis as competitor-only.
76 changes: 76 additions & 0 deletions .agents/skills/keyword-clustering/SKILL.md
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---
name: keyword-clustering
description: Cluster keywords by intent and map them to existing or proposed pages.
---

# OpenSEO Keyword Clustering

## Goal

Group keywords into page-level clusters and decide which existing or new page should target each cluster. This is a keyword mapping workflow, not just a semantic grouping exercise.

## Required inputs

- `projectId`
- A keyword list, saved keyword tag, seed topic, or target domain
- Optional existing URLs/pages to map against

If keywords are not provided, use `list_saved_keywords` for saved sets, `research_keywords` for seed discovery, or `get_ranked_keywords` when the user starts from a target domain.

## OpenSEO MCP tools

- `list_saved_keywords`: fetch an existing keyword set, optionally filtered by tags.
- `research_keywords`: expand a seed when the user starts from a topic.
- `get_ranked_keywords`: gather exact ranking keywords and URLs when the user starts from a domain or page.
- `get_search_console_performance`: when Search Console is connected, pull real queries with `dimensions: ["query","page"]` to map terms to the pages already earning impressions and to surface cannibalization (one query splitting clicks across multiple URLs).
- `get_serp_results`: validate whether keywords belong on the same page by checking SERP overlap and intent.
- `get_local_serp_results`: use for local SEO clusters when Maps/local-pack intent should affect page mapping.
- `save_keywords`: optionally tag final clusters after user confirmation.

## Workflow

1. Gather the candidate keyword set.
- Use `get_search_console_performance` (dimensions `["query","page"]`) when Search Console is connected to start from real queries and the pages already ranking for them.
- Use `get_ranked_keywords` for domain/page-driven clustering.
- Use `search_local_businesses` and `get_local_serp_results` when proximity, local packs, or Google Business results determine whether terms belong on location pages.
2. Remove duplicates, irrelevant terms, and terms that clearly require a different product or audience.
3. Build clusters around intent and page type:
- Same SERP intent and similar ranking pages belong together.
- Different intent, buyer stage, or SERP format should be split.
- Similar words do not guarantee the same cluster.
4. For important borderline terms, use a small `get_serp_results` batch to check overlap.
5. Assign each cluster to:
- Existing URL, if supplied and appropriate
- New page recommendation, if no existing page fits
- Do-not-target / later bucket, if weak or off-strategy
6. Identify cannibalization risk when multiple pages would target the same intent. When Search Console is connected, confirm it from real data with `get_search_console_performance` (`dimensions: ["query","page"]`) — the same query sending impressions to multiple URLs.
7. Ask before applying cluster tags with `save_keywords`.

## Output format

Start with a short mapping summary:

- Number of clusters
- Pages to create
- Existing pages to update
- Cannibalization or consolidation issues

Then include:

| Cluster | Primary keyword | Secondary keywords | Intent | Target page | Priority | Notes |
| ------- | --------------- | ------------------ | ------ | ----------- | -------- | ----- |

For each cluster, include a recommended page brief:

- Page type
- Searcher problem
- Required sections
- Internal-link opportunities
- Save/tag suggestion

## Guardrails

- Do not over-cluster tiny keyword sets. If there are fewer than 10 usable terms, produce a simple map.
- Do not rely on lexical similarity alone. SERP intent wins.
- Do not replace tags broadly without explicit confirmation.
- If existing URL data is missing, label target pages as proposed.
70 changes: 70 additions & 0 deletions .agents/skills/keyword-research/SKILL.md
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---
name: keyword-research
description: "Discover keyword opportunities, evaluate metrics and SERPs, and save/tag promising terms."
---

# OpenSEO Keyword Research

## Goal

Turn seed topics into a prioritized keyword opportunity set using OpenSEO MCP data. The output should help the user decide what to target, what to save, and what to research next.

## Required inputs

- `projectId`
- One or more seed topics, products, pages, competitors, or audience problems
- Optional market/location/language

If `projectId` is missing, use `list_projects` first. If the target market/location/language is unclear and would materially affect keyword metrics, ask the user; otherwise use the MCP tool defaults.

## OpenSEO MCP tools

- `research_keywords`: primary discovery tool. Use 1-5 seeds per call and prefer 150 results unless the user asks for exhaustive research.
- `get_keyword_metrics`: hydrate up to 700 known keywords with volume, keyword difficulty (KD), search intent, CPC, and monthly trends in one call. Use it to score candidate or known terms — including the Search Console striking-distance queries from step 1.
- `get_ranked_keywords`: pull exact ranking keyword rows when a target domain or page is part of the research brief.
- `get_search_console_performance`: when Search Console is connected, start from the project's real first-party demand — queries already earning impressions and near-ranking ("striking distance") terms. Request a high `rowLimit` and filter average position 5-20 client-side, since the API sorts by clicks and can't filter by position. Then hydrate those striking-distance queries with `get_keyword_metrics` to attach difficulty and intent.
- `get_serp_results`: inspect SERPs for the top candidate terms, especially when intent is ambiguous.
- `search_local_businesses`, `get_local_serp_results`, and `get_google_business_questions`: use for local SEO topics when a business/location radius matters.
- `list_saved_keywords`: avoid duplicating already-saved work or use existing tags as context.
- `save_keywords`: save selected keywords only after explicit user confirmation.

## Workflow

1. Normalize seeds into a small set of distinct research angles. If Search Console is connected for the project, first pull `get_search_console_performance` (high `rowLimit`, default lookback), filter to striking-distance positions (~5–20) client-side, and hydrate those queries with `get_keyword_metrics` to attach KD and intent. That ranked, hydrated list is your fastest opportunity set — work it before broad discovery.
2. If the request is local SEO, identify the business, location/coordinates or service area, and local categories. Use `search_local_businesses` and `get_local_serp_results` for the most important location/keyword set instead of relying only on national keyword/SERP data.
3. Call `research_keywords` for exploratory seeds. Use bulk calls when possible.
4. Use `get_keyword_metrics` to hydrate a fixed keyword list — or the striking-distance queries from step 1 — with volume, KD, and intent before prioritizing.
5. Use `get_ranked_keywords` when the user provides a domain/page and wants opportunities based on current rankings, near-misses, or competitor-owned terms.
6. Remove irrelevant, duplicate, branded-only, and off-intent terms.
7. Prioritize by practical opportunity, not volume alone:
- Strong match to the user's product/page/topic
- Clear search intent
- Reasonable difficulty
- Useful volume/CPC signal
- SERP where the user can plausibly compete
- For local SEO, local-pack/Maps visibility and proximity fit
8. Use `get_serp_results` for high-potential or ambiguous keywords when SERP intent would change the recommendation; keep the default check small.
9. Present a shortlist and a longer opportunity table.
10. Ask before saving keywords. When saving, suggest concise tags such as `topic:<topic>`, `intent:<intent>`, or `page:<slug>`.

## Output format

Start with the highest-signal recommendation:

- Best opportunity theme
- Top keywords to target now
- Keywords to save
- Risks or SERP caveats

Then include a compact table:

| Keyword | Intent | Volume | KD | CPC | Priority | Notes |
| ------- | ------ | -----: | --: | --: | -------- | ----- |

End with next actions, including whether to run keyword clustering, create a content brief, or save the chosen keywords.

## Guardrails

- Do not invent metrics. If OpenSEO does not return a value, write `unknown`.
- Do not call `save_keywords` without explicit confirmation.
- Prefer business-fit and intent-fit over chasing the largest volume term.
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