/semantic-core

name: semantic-core
description: Build a measured semantic core for a brand through official keyword APIs (Yandex Wordstat for RU, Google Ads Keyword Planner / Bing Webmaster worldwide, autocomplete everywhere), commit it as core.json plus a questions.csv, and hand that straight to an open-geo visibility run. Use when the user wants demand research, a semantic core, keyword volume, or "collect the questions and then measure visibility" — no browser and no manual keyword tool.

Purpose

semantic-core — measured demand core, then the run

You are the orchestrator for one **core build**: find what people actually search around a product, **measure it through the platforms' own APIs**, cluster it by intent, write the assistant-style questions each cluster justifies, commit the whole thing as a `core.json` + `questions.csv`, and hand that to an **open-geo** visibility run.

Two halves, deliberately separated:

• **Deterministic** — the numbers. `demand/` asks Wordstat / Google Ads / Bing / autocomplete and

returns each figure *with its scope* (region, period, pull date). No browser, no logged-in session, no hand-typed volume. Contract: `pipeline/INTERFACES.md §8`, guide: `demand/README.md`.

• **Agentic** — the judgement. Which angles the product has, how a person phrases the need to an

assistant, which lines survive a skeptic. Authority: `harvest/METHODOLOGY.md` (§3 demand gate, §4 lens invariants, §5 segments).

> Run Python from the open-geo runtime root with its venv (`.venv/bin/python`). Code and intermediate > JSON are English; the summary you print follows `--lang`.

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INVOCATION

/semantic-core <domain> --brand "<name>" [--market "<category>"] [--competitors "a, b"]
               [--geo ru|us|ww|<cc>[,<cc>]] [--query-lang ru|en|<code>[,<code>]]
               [--n 36] [--split 16/10/10] [--out core/<slug>/core.json]
               [--run <engine>] [--n-worker N] [--output data|dashboard|pdf|both]
               [--lang en|ru|zh|ar] [--no-run]

| arg / flag | required | default | meaning | |---|---|---|---| | `<domain>` | yes | — | The product's site. Also the target of the follow-on run. | | `--brand "<name>"` | yes | — | Human brand name; enforces the lens/brand invariants at commit time. | | `--market` | no | inferred | Category in the user's words. Inferred from the homepage when absent — always echo the inference for confirmation. | | `--competitors` | no | — | Seed list; workers extend it. | | `--geo` | no | `ru` | ISO-3166 alpha-2 lowercase, or `ww` for worldwide. Comma-separated for several markets — each is measured on its own ruler. | | `--query-lang` | no | follows geo | The language **people search in** — independent of `--lang` (the deliverable language). A distinct language is a distinct slice with its own CSV. | | `--n` | no | `36` | Target questions across all slices. | | `--split` | no | derived | `general/branded/comparative`, general-tilted (for 36: `16/10/10`). | | `--out` | no | `core/<brand-slug>/core.json` | Where the core artifact lands. The CSV goes beside it as `<brand-slug>_questions.csv`. | | `--run <engine>` | no | ask | Engine for the follow-on open-geo run (`google`, `chatgpt_search`, `yandex_neuro`, …). | | `--n-worker` | no | ask | Capture concurrency for that run. | | `--no-run` | no | off | Build and commit the core, stop before the run. |

Missing required values go to **STEP 1** (wizard), never to a guess.

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STEP 1 — RESOLVE PARAMETERS

1. Take everything from the invocation. If `<domain>` or `--brand` is missing, ask for them (`AskUserQuestion`), one compact question per unknown. 2. Fetch the homepage (`WebFetch`) to infer **market/category** and obvious competitors. Echo the inference in one line and let the user correct it — a wrong category poisons every seed. 3. Ask only for what is still unknown: geo(s), query language(s), count, split, and — unless `--no-run` — the engine and worker count for the follow-on run.

STEP 2 — CAPABILITY CHECK (what can actually be measured)

For each geo:

.venv/bin/python -m demand.doctor --geo <cc>

Report the verdict in one line per geo:

• **volume available** — proceed; the core will rest on numbers.
• **presence only** — no volume ruler is configured for that locale. Say **exactly** which

credential is missing and what it unlocks (the doctor prints the steps), then ask whether to (a) proceed presence-only — a core grounded in real autocomplete phrasings but without volume, every line marked as such, or (b) pause while the user obtains the key. Never silently downgrade, and never fabricate a number to fill the gap.

Carry the verdict into every worker brief: it decides which gate the workers are working under (METHODOLOGY §3).

STEP 3 — SEEDS AND SEGMENTS

1. **Root phrases** (5–12): the job the product does, in the words of the market — not the brand's marketing words. Derive from the homepage, the category, and competitor positioning. 2. **Segments** (METHODOLOGY §5), derived from the product, not a fixed list: demand-primary, demand-secondary, category/discovery, branded-reputation, comparative-rivals, plus supply-side for a two-sided product and a per-language slice where a real audience exists. 3. Sanity-check the roots before fanning out — one cheap call each:

   .venv/bin/python -m demand.lookup --geo <cc> --lang <code> --phrase "<root>" --related 10

A root with zero demand is a wrong root: fix it here, not in five workers at once.

STEP 4 — FAN OUT (one `core-worker` per segment, in parallel)

Spawn one `core-worker` sub-agent per segment (Agent tool), all in one message so they run concurrently. Its contract lives in `../../agents/core-worker.md` — do not restate it. Each brief carries: the product context, its one segment + dominant lens, the geo/language and the **doctor verdict**, its worker index, the seeds relevant to it, and its target (2–4 clusters, 6–15 measured phrases, 4–10 question