link-prospecting
Skill1.2k starsChanged 20 days agoMITView on GitHub
Find link prospects, discover contact paths, and draft outreach from SERPs and backlink signals.
We split this skill into the rules it gives an agent
10 rules. 6 are common, 0 are distinctive, and 4 are very specific to this project.
6
Common
Most of the 6 edited versions we read keep these.
0
Distinctive
This author's own choices. Worth a look.
4
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Common: line 31
After OpenSEO identifies good prospects, use available non-OpenSEO browsing or search tools for public contact discovery.
- Common: line 44
Only record contact details that were actually found.
- Very specific: line 60
Use get_serp_results in batches for the most relevant patterns.
- Very specific: line 66
Call get_serp_results for those queries.
- Very specific: line 67
If competitors are provided, call get_backlinks_overview for the strongest competitor domains or pages first.
- Common: line 70
Keep topical relevance and editorial pages.
- Common: line 103
Do not invent email addresses, social handles, or contact names.
- Very specific: line 104
Do not say OpenSEO found contact details unless an OpenSEO tool returned them.
- Common: line 105
If contact details are not available after a reasonable search, recommend specific discovery steps such as checking the author page, …
- Common: line 106
Avoid spammy mass outreach.
“Common” is measured: kept by at least 6 in 10 of the 6 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
OpenSEO Link Prospecting
Goal
Find realistic pages, sites, and authors that might reference the user's page, product, study, guide, or tool. Use OpenSEO for prospect discovery, then use available web/search/browser tools for contact discovery.
Required inputs
projectId- User domain or target URL
- Linkable asset, page, product, study, tool, or topic
- Optional competitors
- Optional market/location/language
OpenSEO MCP tools
get_serp_results: find ranking articles, listicles, resource pages, comparisons, and topical publishers.get_backlinks_overview: inspect competitor domain or page backlink/referring-domain patterns.get_domain_overview: qualify important prospect domains.get_ranked_keywords: understand what a prospect or competitor ranks for when topical fit matters.search_local_businessesandget_local_serp_results: use for local SEO link prospecting when nearby businesses, local competitors, or Maps/category signals can reveal partnership targets.research_keywords: expand prospecting queries.
Contact discovery tools
After OpenSEO identifies good prospects, use available non-OpenSEO browsing or search tools for public contact discovery. Depending on the client, this may be web search, page fetches, browser automation, or a search API.
Look for:
- Author byline pages
- Contact pages
- Editorial guidelines
- About/team pages
- LinkedIn, X, Bluesky, or other professional profiles
- Newsletter or publication masthead pages
- Public email addresses in page HTML or visible page text
- Structured data such as
Person,Organization,sameAs, oremail
Only record contact details that were actually found. Include the source URL for any email, profile, or contact form.
Prospecting query patterns
Build queries from the asset/topic:
<topic> resourcesbest <category> tools<competitor> alternatives<topic> statistics<topic> guide<topic> examples<topic> templates<topic> software<topic> for <audience>
Use get_serp_results in batches for the most relevant patterns. Send at most 10 queries per call.
Workflow
- Clarify the linkable asset and the reason someone would reference it.
- Build 5-10 prospecting queries by default.
- Call
get_serp_resultsfor those queries. - If competitors are provided, call
get_backlinks_overviewfor the strongest competitor domains or pages first. Continue without backlink evidence if it is unavailable. - For local SEO, use
search_local_businessesandget_local_serp_resultsaround priority locations to identify nearby competitors, categories, and local SERP evidence before searching for local chambers, associations, campus resources, community pages, and directories. - Filter prospects:
- Keep topical relevance and editorial pages.
- Prioritize articles, directories, resource pages, comparisons, statistics pages, templates, and curated lists.
- Deprioritize homepages, login pages, thin affiliate pages, spam, unrelated forums, and direct competitors unless a comparison angle is valid.
- For each good prospect, define the outreach angle:
- Broken/missing resource
- Better current data
- Useful tool/template
- Alternative or comparison inclusion
- Expert quote or supporting reference
- For the strongest prospects, visit or search the prospect site to find the best contact path.
- Draft outreach messages. If contact details were found, include the source. If not, list the next best contact-discovery path.
Output format
Start with:
- Best outreach angle
- Highest-priority prospect type
- Any data limitations
Then include:
| Prospect URL | Site/domain | Source | Relevance | Suggested angle | Contact path | Priority |
Then provide 2-3 reusable outreach drafts:
- Resource/list inclusion
- Article update/reference suggestion
- Competitor alternative/comparison angle
Guardrails
- Do not invent email addresses, social handles, or contact names.
- Do not say OpenSEO found contact details unless an OpenSEO tool returned them. Attribute contact discovery to the web/search/browser source used.
- If contact details are not available after a reasonable search, recommend specific discovery steps such as checking the author page, contact page, LinkedIn, X, or a reputable contact-enrichment tool.
- Avoid spammy mass outreach. Personalize by page and reason.
- Flag prospects that are direct competitors or likely paid placements.
Trending
More talk lately.thin dataabout the whole repository
Who's talking about jwuthri-cloud/Tracely-ai
The biggest 1 of 1 mention linking to the repository that our crawl found. Each line is their own headline.
Other versions9 projects use a version of this.
Word for word · 3
- Jwuthri/Tracely-ai★ 1.5k100% same
- VladSez/easy-invoice-pdf★ 1.1k100% same
- petera2c/simple-table★ 231100% same
Edited · 5
- every-app/open-seo★ 26k58% same
- kid7st/agenticseo★ 232% same
- ventra-rocket/EchoSEO★ 294% same
- winsley-jeune/harborrank-skills★ 130% same
- ictlondon/kraftskills★ 072% same
Wrote their own version · 1
- fenjo26/OpenGSC★ 52same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
Used alongsideProjects with it also use local-seo.
Other files people use alongside this one.
Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
huiferSkilllocal-seo
本地 SEO 分析和优化专家。自动检测项目是否需要本地 SEO,分析 NAP(Name, Address, Phone)一致性、本地关键词优化、Google Business Profile (GBP) 优化和本地结构化数据生成。提供本地商家搜索引擎排名优化建议,包括 NAP 标准化、本地关键词策略、GBP 完整性检查、评论策略、地图嵌入和本地 SEO 审计。
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