Andrew Yim
87635e4208
- gsc_signal_delta.py: extract `found` local var; add first branch in verdict_hint chain so a term absent from both GSC windows yields INCONCLUSIVE (not ARTIFACT). Existing ARTIFACT / CONFIRMED-PARTIAL / PARTIAL branches unchanged (elif chain). - test_gsc_signal_delta.py: add test_absent_claim_term_inconclusive asserting found=False and "INCONCLUSIVE" in verdict_hint for a term in neither fixture. - code/CLAUDE.md: one-line surge-tuning note — verdict_hint/in_top_movers are calibrated for upward claims; for drops, inspect top_decliners directly. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01KuT3W81t88QQFaxY2ruWv2
26 lines
1.2 KiB
Markdown
26 lines
1.2 KiB
Markdown
# seo-signal-validation — code environment notes
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## Helper: scripts/gsc_signal_delta.py
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Deterministic L1/L4 GSC delta. Feed it two saved GSC query exports (recent,
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prior) as JSON or TSV (columns: query, clicks, impressions, position).
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```bash
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python3 scripts/gsc_signal_delta.py \
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--recent recent.tsv --prior prior.tsv \
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--recent-days 28 --prior-days 30 --claim-term "호텔"
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```
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Returns day-normalized site totals, top gainers/decliners, and a `verdict_hint`
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(heuristic only — the final verdict is the skill's job, after L2/L3).
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**Surge-tuning note**: `verdict_hint` and `in_top_movers` are calibrated for upward "surge" claims (movers ranked by click gain). For a claimed *drop*, inspect `top_decliners` directly rather than relying on the hint.
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## Getting the exports
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`mcp__dda__gsc_fetch_performance` (property pinned per workspace, e.g. JHR
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`sc-domain:josunhotel.com`) → save the query-dimension rows to a file → run the
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script. GSC anonymizes ~43% of query clicks; the disclosed subset ≠ the whole.
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## Env / access
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- `GOOGLE_KG_API_KEY` for `mcp__ourseo__search_knowledge_graph` (L3).
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- GSC/GA4 only exist for first-party properties — third-party entities skip L1.
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- Never crawl/audit Marriott for JHR (sameAs only).
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