Borrowing it
Nothing to install: this file belongs to fenjo26/OpenGSC. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/fenjo26/OpenGSC/main/.agents/skills/link-prospecting/SKILL.mdgit clone --depth 1 https://github.com/fenjo26/OpenGSCWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/fenjo26/opengsc/link-prospecting)<a href="https://agentmods.dev/skills/fenjo26/opengsc/link-prospecting"><img src="https://agentmods.dev/badge/skills/fenjo26/opengsc/link-prospecting/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/fenjo26/opengsc/link-prospecting"><img src="https://agentmods.dev/badge/skills/fenjo26/opengsc/link-prospecting.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00042 | $0.00716 |
| Opus 5.5 | $0.00017 | $0.00286 |
| Sonnet 5.5 | $0.00008 | $0.00143 |
| Haiku 4.5 | $0.00004 | $0.00072 |
Grade A, and why
link-prospecting scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 23d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OpenGSC Link Prospecting
Goal
Produce a concrete outreach shortlist: which domains to contact, why they're likely to link, and what content to pitch — grounded in the fresh competitor backlinks the user's Link Monitor has already pulled from Ahrefs.
Required inputs
- Optional: a specific watched brand to focus on, and the user's own site for the backlink-health check.
OpenGSC MCP tools
get_link_mentions: fresh quality backlinks earned by the watched competitor brands +multiLinkerDomains(domains linking to 2+ brands — the highest-probability outreach targets). This is the core tool.get_backlinks: the user's OWN curated backlink inventory with liveness/indexed status — use to spot dead links worth reclaiming.list_sites: resolve the user's site if backlink health is in scope.
Workflow
- Call
get_link_mentions. If it returns a "no data yet" note, stop and tell the user to add brands and run a pull under SEO Tools → Link Monitor. - Prioritize
multiLinkerDomains: a domain that linked to two competitors but not the user is the warmest lead. - From
mentions, infer WHAT earns links in this niche: content types (studies, comparisons, tools), anchor patterns, and the context titles suggest. - If the user's site is known, call
get_backlinksand list dead (alive: false) links as reclamation targets — the cheapest wins in link building. - Build the outreach shortlist with a specific pitch angle per domain.
Output format
- Top 5 outreach targets: domain, DR (from data), which brands it linked, suggested pitch.
- Content/PR ideas the mention data supports (with example source titles as evidence).
- Reclamation list (dead backlinks), if any.
Guardrails
- Do not fabricate DR values or mentions; every claim must trace to a returned row.
- Never suggest paid links or PBNs; keep recommendations white-hat outreach/content.
Site context — read first, free
get_site_context is this skill's step zero. It returns the site's shared memory — business
overview, current goal, positioning, writing preferences, the competitor list, key pages and
the research log — and grounds every decision below in what the operator already told us.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 23d ago Changed · +17 lines 988de8f4fea6
- 2mo ago First seen · 40 lines · 42 tokens per session scan A e463d011da04
link-prospecting is a skill published in the GitHub repository fenjo26/OpenGSC (51 stars, last pushed yesterday), licensed MIT. It adds 42 tokens to every session and 716 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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