Registry indexed
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. Us
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.
Source documentation, not instructions for this website. Review permissions before running any commands.
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:
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.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.
/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.
<domain> or --brand is missing, ask for them
(AskUserQuestion), one compact question per unknown.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.--no-run — the engine and worker count for the follow-on run.For each geo:
.venv/bin/python -m demand.doctor --geo <cc>
Report the verdict in one line per geo:
Carry the verdict into every worker brief: it decides which gate the workers are working under (METHODOLOGY §3).
.venv/bin/python -m demand.lookup --geo <cc> --lang <code> --phrase "<root>" --related 10
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
../../../.codex/agents/core-worker.toml — 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 questions).
A core-worker measures through
demand/, phrases the questions, and returns aCoreClusterJSON array. It never writes the core, the CSV, or the DB, and never opens a browser.
Merge every worker's clusters and:
provider + scope means no evidence; do not
rescue it by writing a number yourself;--split with the general-tilt, maximizing intent diversity inside each lens —
the GEO opening lives in general, where people do not yet know the brand;scope becomes the signal of every
question that cluster ships.Spawn 1–2 harvest-skeptic sub-agents (contract in ../../../.codex/agents/harvest-skeptic.toml) with the
thesis and the final {query, lens} list. Apply the cuts, backfill each from the next-strongest
candidate in the same cluster, and re-run until every shipped line survives. The skeptic cuts
unmeasured lines and lines that overstate a presence-only signal — both are failures of this step,
not of the worker.
Write the synthesized SemanticCore object (INTERFACES §8.3) to a UTF-8 temp file, then:
.venv/bin/python -m demand.core \
--out core/<slug>/core.json \
--questions-out core/<slug>/<slug>_questions.csv \
--brand "<name>" --domain <domain> --rationale core/<slug>/<slug>_rationale.md \
< /tmp/open_geo_core_final.json
Read stdout {core, questions_csv, clusters, written, by_lens, coverage, errors}. errors must be
empty — the usual causes are a mislabeled lens (general naming the brand, branded not naming it)
and an unmeasured cluster. Fix and re-run until errors: []. For a second query language, call it
again with its own --out / --questions-out.
Then write <slug>_rationale.md yourself: per cluster — who we catch, on which measured signals
(quote the scope strings), why this lens, which competitors surfaced. This is the provenance the
CSV omits.
Show: total questions, by_lens, coverage (how many phrases rest on volume vs presence), the
strongest and weakest clusters by measured volume, and the full query list. Ask (AskUserQuestion):
Apply / Edit (adjust rows, re-run STEP 7 until errors: []) / Discard. Never go straight
to capture on a freshly generated set without the operator seeing it.
Unless --no-run, invoke the open-geo skill with the committed artifacts:
/open-geo <questions_csv> <engine> <domain> --brand "<name>" --n-worker <N> \
[--output …] [--lang …]
core.json is the carrier: it records questions_csv, brand, domain and totals.coverage, so
the run reads one file and cannot mismatch the set it measures. open-geo takes its STEP A.5 fast
path — a committed core and a hand-made CSV are indistinguishable downstream.
Finish with a short summary in --lang: where core.json and the CSV are, questions by lens,
coverage (volume vs presence), which providers answered, and the run that was started (or the exact
command to start it later).
demand/; judgement comes from you and the workers. Neither substitutes for the
other.demand.core → harvest.build: same CSV invariants as every other question set
in this project. Nothing downstream of the CSV changes.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.
---
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.
---
# 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`.
---
## 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.
---
## 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:
```bash
.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:
```bash
.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
`../../../.codex/agents/core-worker.toml` — 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 questions).
> A core-worker measures through `demand/`, phrases the questions, and returns a `CoreCluster` JSON
> array. It never writes the core, the CSV, or the DB, and never opens a browser.
## STEP 5 — SYNTHESIZE (you)
Merge every worker's clusters and:
- **drop unmeasured clusters** — no phrase with a `provider` + `scope` means no evidence; do not
rescue it by writing a number yourself;
- **dedup by meaning** across segments (not just by string), keeping the strongest evidence;
- **balance to `--split`** with the general-tilt, maximizing intent diversity inside each lens —
the GEO opening lives in `general`, where people do not yet know the brand;
- **split by language**: each query language becomes its own slice and its own CSV;
- keep every cluster's phrases attached — the anchor phrase's `scope` becomes the `signal` of every
question that cluster ships.
## STEP 6 — SKEPTIC PASS
Spawn 1–2 `harvest-skeptic` sub-agents (contract in `../../../.codex/agents/harvest-skeptic.toml`) with the
thesis and the final `{query, lens}` list. Apply the cuts, backfill each from the next-strongest
candidate in the same cluster, and re-run until every shipped line survives. The skeptic cuts
unmeasured lines and lines that overstate a presence-only signal — both are failures of this step,
not of the worker.
## STEP 7 — COMMIT THE CORE
Write the synthesized `SemanticCore` object (INTERFACES §8.3) to a UTF-8 temp file, then:
```bash
.venv/bin/python -m demand.core \
--out core/<slug>/core.json \
--questions-out core/<slug>/<slug>_questions.csv \
--brand "<name>" --domain <domain> --rationale core/<slug>/<slug>_rationale.md \
< /tmp/open_geo_core_final.json
```
Read stdout `{core, questions_csv, clusters, written, by_lens, coverage, errors}`. **`errors` must be
empty** — the usual causes are a mislabeled lens (general naming the brand, branded not naming it)
and an unmeasured cluster. Fix and re-run until `errors: []`. For a second query language, call it
again with its own `--out` / `--questions-out`.
Then write `<slug>_rationale.md` yourself: per cluster — who we catch, on which measured signals
(quote the `scope` strings), why this lens, which competitors surfaced. This is the provenance the
CSV omits.
## STEP 8 — REVIEW GATE (human-in-the-loop)
Show: total questions, `by_lens`, `coverage` (how many phrases rest on volume vs presence), the
strongest and weakest clusters by measured volume, and the full query list. Ask (`AskUserQuestion`):
**Apply** / **Edit** (adjust rows, re-run STEP 7 until `errors: []`) / **Discard**. Never go straight
to capture on a freshly generated set without the operator seeing it.
## STEP 9 — HAND OFF TO THE RUN
Unless `--no-run`, invoke the **open-geo** skill with the committed artifacts:
```
/open-geo <questions_csv> <engine> <domain> --brand "<name>" --n-worker <N> \
[--output …] [--lang …]
```
`core.json` is the carrier: it records `questions_csv`, `brand`, `domain` and `totals.coverage`, so
the run reads one file and cannot mismatch the set it measures. open-geo takes its STEP A.5 fast
path — a committed core and a hand-made CSV are indistinguishable downstream.
Finish with a short summary in `--lang`: where `core.json` and the CSV are, questions by lens,
coverage (volume vs presence), which providers answered, and the run that was started (or the exact
command to start it later).
## Boundaries
- Numbers come from `demand/`; judgement comes from you and the workers. Neither substitutes for the
other.
- **Never invent a volume**, and never present a presence-only signal as measured demand.
- The commit path is `demand.core` → `harvest.build`: same CSV invariants as every other question set
in this project. Nothing downstream of the CSV changes.Source needs review
The tracked source changed or could not be synchronized. Review the current source before installing.
Review before install: Avoid automatic install
License: MIT
Listed tools are metadata hints, not tested compatibility. Agent prompts are suggested handoffs.
Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.
Version reported in registry metadata; check source releases before relying on it.
Quality
55/100
Promising
Trust
61/100
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
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"verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
}
},
"outcome_feedback": {
"endpoint": "https://www.openagentskill.com/api/agent/outcome",
"method": "POST",
"requires_resolve_event_id": true,
"event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
"expected_outcomes": [
"success",
"failed",
"not_relevant",
"blocked_by_risk",
"setup_required"
],
"payload_template": {
"event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
"skill_slug": "pupok462-semantic-core",
"task": "Use semantic-core in an agent workflow",
"agent": "codex",
"outcome": "success",
"install_used": true,
"risk_blocked": false,
"setup_required": false,
"task_success": true,
"output_quality": 4,
"error_type": null,
"human_review_required": false,
"workspace": "sandbox",
"time_to_useful_ms": 120000,
"notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
}
},
"endpoints": {
"web": "https://www.openagentskill.com/skills/pupok462-semantic-core",
"api": "https://www.openagentskill.com/api/agent/skills/pupok462-semantic-core",
"audit": "https://www.openagentskill.com/skills/pupok462-semantic-core/audit",
"eval": "https://www.openagentskill.com/api/agent/evals?slug=pupok462-semantic-core&task=Use%20semantic-core%20in%20an%20agent%20workflow&max_risk=medium",
"resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20semantic-core%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
"receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20semantic-core%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
"install": "https://www.openagentskill.com/api/skills/pupok462-semantic-core/install",
"manifest": "https://www.openagentskill.com/api/registry/manifest/pupok462-semantic-core"
}
}Listing source
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Sandbox only
Audit
72/100
Needs review
Copies are not installs. Installation counts require a reported successful installation; they are not a blanket quality guarantee.