Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/siddiqss/semantic-seo-suitenpx agentmods add skills/siddiqss/semantic-seo-suite/link-opportunitiesWrote 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/siddiqss/semantic-seo-suite/link-opportunities)<a href="https://agentmods.dev/skills/siddiqss/semantic-seo-suite/link-opportunities"><img src="https://agentmods.dev/badge/skills/siddiqss/semantic-seo-suite/link-opportunities/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/siddiqss/semantic-seo-suite/link-opportunities"><img src="https://agentmods.dev/badge/skills/siddiqss/semantic-seo-suite/link-opportunities.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 | $0.00127 | $0.01015 |
| Opus 5 | $0.00063 | $0.00508 |
| Sonnet 5 | $0.00025 | $0.00203 |
| Haiku 4.5 | $0.00013 | $0.00102 |
Grade A, and why
link-opportunities 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 11d 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 — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
link-opportunities
The authority layer the content engine omits. A perfect map on a zero-authority domain still loses to funded incumbents — this plans the external links (and the citations that travel with them) that make ranking possible. It reads the same brand workspace, respects the tier, and tags every prospect's provenance.
Read first: ../../framework/off-page-authority.md (plays, anchor rules, the honesty
line), then ../../framework/eeat-signals.md (why off-site entity consistency matters).
Preconditions
entity-profile.json(competitors drive the backlink gap) +topical-map.json.- Prospect discovery needs
web_search: true(T1) ordataforseo: true(T2). At T0 the skill produces the plan, plays, and anchor mix but cannot name real prospects — say so; do not invent domains.
Workflow
-
Build the plan skeleton (T0, offline).
python ../../scripts/link_prospects.py --map brands/<slug>/topical-map.json \ --entity-profile brands/<slug>/entity-profile.json --brand "<Brand>" \ --out brands/<slug>/outreach/link-plan.mdEmits, per node (priority-ordered core→outer): the fitting plays, an off-page anchor mix, and a competitor backlink-gap worksheet. Priorities are
derived; play notes areasserted. -
Discover real prospects.
- T1 (web_search): for top-priority commercial nodes, search the target query + "best/ alternatives/ vs" and record the listicles/reviews that already rank — inclusion targets. For each competitor, search their brand + "review / integration / alternative" to surface sites already covering the category.
- T2 (DataForSEO backlinks): pull each competitor's referring domains, filter for
topical relevance, and rank the gap. Authority numbers here are
measured. - Record every prospect with: URL, why-relevant, authority (
measuredor left blank — never guessed), the node it supports, and the play. Score withlink_prospects.py:score_prospect(unknown authority scores on relevance + link type, flagged unknown).
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 79 lines · 127 tokens per session scan A 5c3a1b4edf7a
link-opportunities is a skill published in the GitHub repository siddiqss/semantic-seo-suite (9 stars, last pushed 2mo ago), licensed MIT. It adds 127 tokens to every session and 1,015 once invoked, about $0.0006 per session on Opus 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 2026-08-31.
Other skills, from other repositories
fire-your-seo-agency
A procedure for improving how a website appears in search engines and how AI answer systems find and cite it. It covers search, answer-engine, generative-AI, and Naver visibility.
geo-loop
Run one bounded eGEOagents loop iteration over a workspace domain - read the charter and fresh collector data, do ONE unit of work, write substrate artifacts, append one Timeline entry and one LOG line. Use for loop mode, /geo:loop, scheduled GEO runs, or continuous monitoring.
content-scoring
Score content against the 10 GEO criteria with evidence and prioritized fixes. Use when users ask to score, rate, evaluate, or estimate ranking strength.
schema-generator
Generate JSON-LD schema markup for pages and content types with an implementation checklist. Use when users ask for schema, structured data, rich snippets, or markup.
competitive-analysis
Analyze AI-search competitors for a query and recommend ranking strategy. Use when users ask competitor analysis, who ranks, or competitive landscape.
validation-doctor
Check Brave Search and Chrome DevTools MCP availability and provide exact setup snippets. Use when validation dependencies are missing or uncertain.