TF-IDF Entity Gap Analyzer

TF-IDF Entity Gap Analyzer

AGENT ROLE: Autonomous content-gap agent. Compare the user's page against the collective entity/term coverage of the ranking set and emit the JSON in references/output.schema.json. Report gaps as evidence-ranked recommendations, not filler suggestions.

OBJECTIVE

For a target query and the user's URL, quantify which entities and salient terms the top-ranking pages cover that the user's page under-covers, so the content can be expanded to competitive depth.

INPUTS

• query (REQUIRED): the target keyword.
• target_url (REQUIRED): the user's page to improve.
• competitor_count (OPTIONAL, default 10): top organic results to profile.
• entity_backend (OPTIONAL enum google_nl|tfidf): default google_nl if a key is set, else tfidf (local, keyless).

AUTHENTICATION

• SERP_API_KEY (REQUIRED) to fetch the ranking set. IF unset THEN STOP error.code="AUTH_MISSING_SERP_KEY".
• google_nl backend: REQUIRE env NL_API_KEY (Cloud Natural Language API enabled). Endpoint POST https://language.googleapis.com/v1/documents:analyzeEntities?key={NL_API_KEY}. IF unset THEN fall back to the built-in tfidf backend and note entity_backend="tfidf".
• Page fetches: keyless HTTPS GET, UA seoskills-entity-gap/1.0, honor robots.

EXPECTED TOOL CALLS

• Run scripts/entity_gap.py --query "..." --url {target_url}.
• SERP fetch → top-N organic URLs → GET + extract main text of each → entity/term extraction on each doc and on the target.

PROCEDURE (deterministic)

STEP 1 — FETCH the top-competitor_count organic URLs for query (exclude the target's own domain from competitors). STEP 2 — EXTRACT main text from each competitor + the target (strip nav/boilerplate; require ≥ 200 words or mark thin). STEP 3 — PROFILE terms:

• google_nl: entities with salience per doc.
• tfidf: compute TF-IDF over the corpus (competitors + target) after stopword removal; keep top terms per doc. STEP 4 — AGGREGATE competitor coverage: for each entity/term, coverage = docs_containing / competitor_count and mean weight. STEP 5 — DIFF vs target: gap = coverage - target_presence. Keep terms with coverage >= 0.4 AND target_presence low. STEP 6 — SCORE priority = coverage * mean_weight; EMIT gaps sorted by priority desc, plus covered_well (target already strong) for context.

RATE LIMITS & ERROR HANDLING

• SERP 429/quota → backoff 2^attempt (max 5) then STOP error.code="RATE_LIMITED".
• NL API 429 → backoff (max 4); on persistent failure switch that doc to tfidf and set mixed_backend=true.
• Per-page fetch failure (timeout 12s, non-200, robots) → skip that competitor, note in skipped_competitors; continue if ≥ 3 competitors succeeded, else STOP error.code="INSUFFICIENT_CORPUS".

MISSING / INSUFFICIENT DATA

• IF the target page is thin (< 200 words) THEN still return gaps but set target_thin=true.
• Never recommend a term the target already covers well; the gap must be positive.
• Exclude the target's own domain from the competitor set to avoid self-comparison.

OUTPUT

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

FILES

• scripts/entity_gap.py — SERP + page extraction, NL/TF-IDF profiling, gap scoring.
• references/output.schema.json — output contract.