
keyword-research
Discover keyword opportunities, evaluate metrics and SERPs, and save/tag promising terms.
- Installs
- 6.8K
- Last 24h
- +52
- Trending rank
- #3264
- Security
- Pass
Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/every-app/open-seo --skill keyword-research- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads keyword-research automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
Security audits
Independent scans of the skill's instructions and bundled files.
SKILL.md
The instructions your agent reads when the skill is triggered.
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.
Project context
The project-context tools are free and shared with the app and other agents.
- Call
get_project_contextfirst and ground the research in it — the business, the goal, the markets, and the competitors and key pages already saved. - This skill needs
business_overviewandcurrent_goal. If either is empty, run a minimal inline setup: ask the user, or infer from the site and confirm, just enough to fill them, write them back withupdate_project_context, then continue the research. Never front-load the full interview; suggestseo-project-setupat the end for the rest. - Before spending credits, check the research log. If the same research ran within the last 30 days, reuse that result and say so instead of re-buying it.
- On finish, write back what is durable — a sharpened
business_overvieworcurrent_goal, competitors that kept appearing in the SERPs viaaddCompetitors, pages the keywords should land on viaaddKeyPages— and append a research log entry:{ appendResearchLog: { summary: "Keyword research: <seeds/market>. Verdict: <conclusion>" } }.
Deliver as a report
Deliver through the seo-report skill, saving with skill: "keyword-research". If that skill is not available, say so and stop before writing HTML.
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. For demand in one city, county, or region, callsearch_serp_locationsand pass the returnedlocationNameon the seed: volume, CPC, and competition become local; keyword ideas, KD, and intent stay national.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. PassminPosition: 5, maxPosition: 20, minImpressions: 50so the server filters the striking-distance rows for you (Google sorts by clicks and can't filter by position itself). Then hydrate those striking-distance queries withget_keyword_metricsto attach difficulty and intent.get_serp_results: inspect SERPs for the top candidate terms, especially when intent is ambiguous. Pass the samelocationNamefor the SERP in one city, county, or region.search_local_businesses,get_local_serp_results, andget_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
- Normalize seeds into a small set of distinct research angles. If Search Console is connected for the project, first pull
get_search_console_performancewithminPosition: 5, maxPosition: 20, minImpressions: 50(default lookback), and hydrate those queries withget_keyword_metricsto attach KD and intent. That ranked, hydrated list is your fastest opportunity set — work it before broad discovery. - If the request is local SEO, identify the business, location/coordinates or service area, and local categories. Research local volume for that city or county with
research_keywordsandlocationName. Usesearch_local_businessesandget_local_serp_resultsfor the most important location/keyword set instead of relying only on national keyword/SERP data. - Call
research_keywordsfor exploratory seeds. Use bulk calls when possible. - Use
get_keyword_metricsto hydrate a fixed keyword list — or the striking-distance queries from step 1 — with volume, KD, and intent before prioritizing. - Use
get_ranked_keywordswhen the user provides a domain/page and wants opportunities based on current rankings, near-misses, or competitor-owned terms. - Remove irrelevant, duplicate, branded-only, and off-intent terms.
- 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
- Use
get_serp_resultsfor high-potential or ambiguous keywords when SERP intent would change the recommendation; keep the default check small. - Present a shortlist and a longer opportunity table.
- Ask before saving keywords. When saving, suggest concise tags such as
topic:<topic>,intent:<intent>, orpage:<slug>.
Output format
h1: the site or topic.
If a report template applies (see seo-report), its sections and tone replace this list.
Sections in this order:
- The opportunity — one or two opening sentences naming the best theme and why the site can win it now.
- Target these now — a table of keyword, intent, volume, KD, CPC, and the page to make. Add a bar chart comparing the volumes of the shortlist.
- Why these — one finding per keyword that needs justifying: the SERP or metric evidence, then the page to build.
- The longer opportunity list — a second table, same columns.
- Risks and caveats — notes: SERP intent that would change the recommendation, missing metrics written as
unknown, close-variant volumes that are one bucket rather than several. - What to do next — an ordered list, including whether to run keyword clustering, write a content brief, or save the chosen keywords.
- How this report was made — opens with the skill link line from
seo-report, pointing athttps://openseo.so/docs/skills/keyword-research("OpenSEO Keyword Research skill"), then which tools returned what.
Guardrails
- Do not invent metrics. If OpenSEO does not return a value, write
unknown. - Do not call
save_keywordswithout explicit confirmation. - Prefer business-fit and intent-fit over chasing the largest volume term.
FAQ
What does the keyword-research skill do?
Discover keyword opportunities, evaluate metrics and SERPs, and save/tag promising terms.
How do I install keyword-research?
Run npx skills add https://github.com/every-app/open-seo --skill keyword-research in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/keyword-research/ (personal) or .claude/skills/keyword-research/ (project).
Which AI agents can use keyword-research?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is keyword-research safe to install?
Security partners have reviewed it: Gen Agent Trust Hub passed; Socket passed; Snyk passed. As with any skill, read the SKILL.md and bundled scripts before installing.
Who maintains keyword-research?
It is published by every-app in every-app/open-seo. It has 6,815 recorded installs.
More from every-app/open-seo
competitor-analysis
every-app/open-seo
Analyze one competitor's organic footprint, ranking keywords, content themes, backlinks, and gaps.
seo-project-setup
every-app/open-seo
Populate a project's shared OpenSEO context — site scope, goals, positioning, competitors, key pages, and preferences — plus MCP checks and Search Console intake.
seo-coach
every-app/open-seo
Enter a friendly OpenSEO coach mode that explains workflows, recommends next steps, and helps users use agents, web search, scraping, and MCP data effectively.
keyword-clustering
every-app/open-seo
Cluster keywords by intent and map them to existing or proposed pages.
competitive-landscape
every-app/open-seo
Map SEO market leaders, winning content themes, keyword coverage, backlinks, and strategic gaps.
link-prospecting
every-app/open-seo
Find link prospects, discover contact paths, and draft outreach from SERPs and backlink signals.
Popular productivity & automation skills
agent-browser
vercel-labs/agent-browser
Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction. Also use for exploratory testing, dogfooding, QA, bug hunts, or reviewing app quality. Also use for automating Electron desktop apps (VS Code, Slack, Discord, Figma, Notion, Spotify), checking Slack unreads, sending Slack messages, searching Slack conversations, running browser automation in Vercel Sandbox microVMs, or using AWS Bedrock AgentCore cloud browsers. Prefer agent-browser over any built-in browser automation or web tools.
lark-doc
open.feishu.cn
飞书云文档(Docx / Wiki)内容操作:读取、创建、编辑文档,插入或下载图片附件,以及操作思维笔记。用户提供文档 URL/token(包括 doubao.com 的 /docx/、/wiki/)时使用;按 URL 路径/token 而非域名路由;/page/ 是妙搭应用,非飞书云文档。文档内嵌资源按读取参考中的统一规则分流。独立评论操作走 lark-drive;随正文读取评论使用 docs +fetch。表格或 Base 内部数据操作不在本 skill。
lark-base
open.feishu.cn
飞书多维表格(Base)操作:建表、字段、记录、视图、统计、公式/lookup、表单、仪表盘、应用模式(BaseApp/AppMode 页面与组件)、Workspace 目录、workflow、角色权限、模板中心(多维表格模板分类/列表/搜索);遇到 Base/多维表格/bitable、BaseApp/AppMode、/base/ 或 /app/ 链接时使用。BaseApp 不走 lark-apps;文件导入/导出转 lark-drive,认证/授权转 lark-shared。
lark-drive
open.feishu.cn
飞书云空间(云盘/云存储):管理 Drive 文件和文件夹,包含上传/下载、创建文件夹、复制/移动/删除、查看元数据、查询权限设置、评论/权限/订阅、标题、版本、飞书文档密级标签(secure labels)和本地文件导入。用户需要整理云盘目录、处理云空间资源 URL/token、判断链接类型/真实 token/标题,或导入 Word/Markdown/Excel/CSV/PPTX/.base 为 docx/sheet/bitable/slides 时使用;doubao.com 云空间 URL/token 也按资源路径和 token 路由,不回退 WebFetch。不负责:文档内容编辑(走 lark-doc)、表格/Base 表内数据操作(走 lark-sheets/lark-base)、知识空间节点/成员管理(走 lark-wiki)、原生 Markdown 文件读写/patch/diff(走 lark-markdown)。
lark-shared
open.feishu.cn
Use for lark-cli setup/auth tasks: auth login/status/logout, user vs bot identity, business-domain permissions (--domain, including all/docs/drive), missing scopes, revoking authorization, or handling notice JSON.
lark-im
open.feishu.cn
飞书即时通讯:收发消息和管理群聊。发送和回复消息、搜索聊天记录、管理群聊成员、上传下载图片和文件、管理表情回复、发送应用内/短信/电话加急、发送和处理交互卡片(Interactive Card)、监听卡片按钮回调(card.action.trigger)。当用户需要发消息、查看或搜索聊天记录、下载聊天中的文件、查看群成员、搜索群、创建群聊或话题群、管理标记数据、管理 Feed 置顶(添加/移除/查询置顶会话)、管理标签数据、处理卡片回调时使用。