Internal Link Opportunity Mapper

Internal Link Opportunity Mapper

AGENT ROLE: Autonomous internal-linking agent. Build the site's page-similarity matrix and existing link graph, find high-similarity unlinked pairs, and emit ranked recommendations per references/output.schema.json.

OBJECTIVE

Recommend the highest-value missing internal links: pairs of pages that are semantically related but not currently linked, prioritized by relevance and the target page's link deficit — plus surface orphan pages.

INPUTS

• start_url (REQUIRED): crawl seed; host defines scope.
• max_pages (OPTIONAL, default 1000).
• similarity_threshold (OPTIONAL, default 0.75): cosine floor to call two pages "related".
• max_recommendations (OPTIONAL, default 200).
• embedding_backend (OPTIONAL enum openai|tfidf): default openai if a key is set, else local tfidf.

AUTHENTICATION

• Crawl: keyless HTTPS GET, UA seoskills-internal-links/1.0, honor robots.
• openai backend: REQUIRE env OPENAI_API_KEY; endpoint POST https://api.openai.com/v1/embeddings (model text-embedding-3-small). IF unset THEN fall back to local tfidf cosine and note embedding_backend="tfidf".

EXPECTED TOOL CALLS

• Run scripts/internal_links.py --start {url} --max 1000.
• BFS crawl (same host); per page store main text + outbound internal links; then embed each page's text.

PROCEDURE (deterministic)

STEP 1 — CRAWL (bounded BFS), capturing per page: {url, title, main_text, internal_links_out}. STEP 2 — EMBED each page's title + main_text (OpenAI batch, or local TF-IDF vectors). STEP 3 — EXISTING GRAPH: build the set of directed internal links already present. STEP 4 — CANDIDATES: for every unordered page pair with cosine >= similarity_threshold, IF neither direction is already linked THEN it is a candidate. STEP 5 — DIRECTION + PRIORITY: recommend linking FROM the higher-authority/older page TO the one with the larger link deficit (fewer inbound internal links). priority = cosine * (1 / (1 + inbound_links(target))). STEP 6 — ANCHOR: propose an anchor from the target page's title / most salient shared n-gram present in the source's text. STEP 7 — ORPHANS: list pages with 0 inbound internal links. EMIT recommendations sorted by priority desc.

RATE LIMITS & ERROR HANDLING

• Crawl politeness: ≤ 5 concurrent, ≥ 200ms between same-host requests, obey robots Crawl-delay. HARD stop at max_pages (hit_cap=true).
• OpenAI embeddings 429 → honor Retry-After else backoff 2^attempt (max 5); on persistent failure switch to tfidf and set embedding_backend="tfidf" (never fail the whole run for embeddings).
• Per-page fetch failure → skip; a page needs ≥ 100 words to be embedded (else excluded, listed in skipped).

MISSING / INSUFFICIENT DATA

• IF fewer than 5 embeddable pages THEN STOP error.code="INSUFFICIENT_PAGES" (nothing meaningful to link).
• Never recommend a link that already exists in either direction; never recommend self-links.
• Anchor text must appear in the source page's vocabulary; if none fits, return the target title and mark anchor_confidence="low".

OUTPUT

One JSON object per references/output.schema.json. No prose.

FILES

• scripts/internal_links.py — crawler, embeddings/TF-IDF cosine, candidate ranking, anchor suggestion.
• references/output.schema.json — output contract.