Install
$ agentstack add skill-siddiqss-semantic-seo-suite-link-opportunities ✓ scanned · ✓ verified — works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
About
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//topical-map.json \ --entity-profile brands//entity-profile.json --brand "" \ --out brands//outreach/link-plan.md ` Emits, 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 are asserted`.
- 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 with link_prospects.py:score_prospect (unknown authority scores on relevance + link type, flagged unknown).
- Write the outreach queue → append to
brands//outreach/link-plan.md:
- Ranked prospect table (prospect · node · play · authority[measured|unknown] · angle).
- Suggested anchor per placement, drawn from the node's anchor examples, honoring the
mix (keep exact-match ~10%).
- No fabricated contacts. If you can't find a real, published contact, mark it
"contact: research needed" — do not invent an email.
- Feed the loop.
- A high-priority node with no realistic prospects → recommend building a *linkable
asset* for it (free tool / data study), not low-value link spam.
- Queries where competitors are linked/cited and the brand is absent → shared signal
with answer-engine-optimizer.
- When a link lands, record the URL on the node; it raises that node in
seo-performance-tracker.
Definition of done
link-plan.mdwritten: anchor mix, competitor gap worksheet, priority-ordered plays.- If T1/T2 on: a ranked prospect queue with provenance on every authority value and no
invented domains or emails. If T0: the plan only, with an explicit note that discovery needs web_search/DataForSEO.
- Nothing fabricated — the whole point of doing this inside the suite.
Grounding ladder
- T0: offline plan skeleton, plays, anchor mix, competitor worksheet. Useful alone.
- T1 (web_search): + real listicle/review/resource prospects,
measured+ dated. - T2 (DataForSEO): + competitor backlink-gap with
measuredauthority numbers.
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: siddiqss
- Source: siddiqss/semantic-seo-suite
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.