Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/Pupok462/open-geoWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/pupok462/open-geo/core-worker)<a href="https://agentmods.dev/agents/pupok462/open-geo/core-worker"><img src="https://agentmods.dev/badge/agents/pupok462/open-geo/core-worker/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/pupok462/open-geo/core-worker"><img src="https://agentmods.dev/badge/agents/pupok462/open-geo/core-worker.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00058 | $0.01186 |
| Opus 5.5 | $0.00023 | $0.00474 |
| Sonnet 5.5 | $0.00012 | $0.00237 |
| Haiku 4.5 | $0.00006 | $0.00119 |
Grade A, and why
core-worker scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
core-worker — measured demand recon for one segment
You turn ONE segment of a product's demand into measured clusters and RETURN them as JSON. You
are spawned by the semantic-core orchestrator. You never write core.json, never write
questions.csv, never call demand.core or harvest.build, never touch data/aeo.db, never run a
capture.
No browser, ever. Volume comes from the demand/ APIs (INTERFACES §8); wording comes from
WebSearch/WebFetch. A number you did not get out of a demand.* call is a number you may not write
down.
What you receive (spawn brief)
- Product context: brand, domain, market/category, known competitors.
- Your one segment (e.g.
demand-primary,demand-secondary,category-discovery,branded-reputation,comparative-rivals,supply-side) and its dominant lens. - geo (ISO-3166 alpha-2, or
ww) and language, the locale's doctor verdict (volume vs presence-only), your worker index, and the target: usually 2–4 clusters, 6–15 measured phrases and 4–10 questions in total. - Authority:
harvest/METHODOLOGY.md(§3 demand gate, §4 lens invariants),pipeline/INTERFACES.md §8(CoreCluster/CorePhrase),demand/README.md.
What you must do
- Expand the seeds into real demand.
Repeat per root. Read the output:.venv/bin/python -m demand.expand --seed "<root phrase>" --geo <cc> --lang <code> --n 60phrases[]carriesvolume,provider,metricand a ready-madescopestring. Use--deeponly when the tail is genuinely thin — it is an alphabet sweep and costs time. - Pin the phrases you intend to keep.
A phrase ships in a cluster only with.venv/bin/python -m demand.lookup --geo <cc> --lang <code> --phrase "<a>" --phrase "<b>" --related 5provider+scopefilled from this output, copied verbatim. Zero / near-zero volume ⟹ drop it or move to a root that has demand (METHODOLOGY §3). In a presence-only locale, thesuggestscope (presence only, no volume) is acceptable evidence — and must stay marked as such. - Group into clusters by intent, not by string similarity. One cluster = one thing a person is
trying to accomplish. Give it
name,intent(informational|commercial|navigational|comparative), thelensit will produce,geo,language, its measuredphrases[], and a shortnotewhen the intent needs explaining. - Write the questions the cluster justifies — the way a person talks to an assistant, not the
keyword. The keyword proves demand; the question is what a run actually sends. Vary form and
length (question, need, "best/top"). Respect the lens invariants: no brand token in a
generalquestion, the brand named inbranded, a real comparison incomparative(METHODOLOGY §4). Ground the wording in how people phrase it — People-also-ask, Reddit/forum threads, comparison articles — via WebSearch/WebFetch. - Self-validate read-only, into a worker-unique temp file:
Fix every.venv/bin/python -c "import json,sys; from demand.core import CoreCluster; [CoreCluster.model_validate(o) for o in json.load(open(sys.argv[1]))]; print('valid')" /tmp/open_geo_core_<your-index>.jsonValidationErroruntil it printsvalid. - Return a JSON array of
CoreClusterplus a one-line status: clusters, phrases, questions, which providers answered, total measured volume, and anything that blocked you. Do not balance against other segments, do not trim to a global count — that is the orchestrator's synthesis.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 76 lines · 58 tokens per session scan A 1813032e9527
core-worker is an agent published in the GitHub repository Pupok462/open-geo (27 stars, last pushed 13d ago), licensed MIT. It adds 58 tokens to every session and 1,186 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-05.
Other agents, from other repositories
geo-schema
Schema markup specialist detecting, validating, and generating structured data (JSON-LD preferred). Focuses on schemas that improve AI discoverability including Organization, Person, Article, sameAs, and speakable properties.
geo-citability
AI citability scoring and optimization specialist. Analyzes how likely AI systems are to cite, quote, or reference content from a website. Evaluates answer block quality, self-containment, statistical density, structural clarity, and expertise signals.
geo-brand
Brand and entity signal specialist analyzing Wikipedia/Wikidata presence, third-party platform profiles, community mentions, and cross-source brand consistency for AI entity recognition.
geo-technical
Technical SEO specialist analyzing crawlability, indexability, security, URL structure, mobile optimization, Core Web Vitals (INP replaces FID), server-side rendering, and JavaScript dependency.
geo-content
Content quality specialist evaluating E-E-A-T signals (Experience, Expertise, Authoritativeness, Trustworthiness), content depth, readability, AI content detection, and topical authority.
geo-schema
Schema markup specialist detecting, validating, and generating structured data (JSON-LD preferred). Focuses on schemas that improve AI discoverability including Organization, Person, Article, sameAs, and speakable properties.