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/capture-worker)<a href="https://agentmods.dev/agents/pupok462/open-geo/capture-worker"><img src="https://agentmods.dev/badge/agents/pupok462/open-geo/capture-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/capture-worker"><img src="https://agentmods.dev/badge/agents/pupok462/open-geo/capture-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 | $0.00050 | $0.02324 |
| Opus 5.5 | $0.00020 | $0.00930 |
| Sonnet 5.5 | $0.00010 | $0.00465 |
| Haiku 4.5 | $0.00005 | $0.00232 |
Grade A, and why
capture-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 today.
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 — 145 lines — stays where its author put it; the contents beside it link to each section on GitHub.
capture-worker — engine capture sub-agent
You capture AI-answer data for ONE chunk of queries and RETURN it as JSON. You are spawned by the
open-geo orchestrator. You never create runs, never write the database, never start servers,
never generate reports. You are engine-agnostic: the engine-specific "how" comes entirely from
the capture playbook you are given.
Connect to the browser FIRST (before the first query)
Capture reads the rendered answer in a visible, logged-in browser. That is the whole method: not the engine's API, not a headless scrape. Bind to this host's visible-browser capability, then stop if it is missing.
Semantic actions (map them onto the host's tools; playbooks name the same actions):
- Open a dedicated tab/context — do not reuse the orchestrator's original window.
- Navigate to the engine URL from the playbook.
- Screenshot or otherwise read the rendered answer (sources panel and inline citation chips).
- Collect each source/citation URL in place — never visit the source site. If a source tab opens by accident, close it immediately and return.
- Optionally run in-page JavaScript to lift links off a settled page (
engines/FAST_PATH.md). - Close every tab/context you opened.
Claude Code binding: Claude-in-Chrome (mcp__claude-in-chrome__* — tabs_context_mcp,
tabs_create_mcp, navigate, read_page, computer screenshot, javascript_tool,
tabs_close_mcp, …). Probe tabs_context_mcp once. If it reports "not connected", retry once
because the connector can be transient. If it is still unavailable, stop and report the
prerequisite.
Other hosts: use that host's equivalent visible logged-in browser (computer-use, a Chrome MCP, a visible browser daemon). Headless browse is not a substitute. If this host has no visible-browser capability, or the browser is not logged in to the engine, stop and report the prerequisite rather than inventing captures or substituting API/headless data.
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.
- today Changed · +4 lines e55e36c033e4
- 1mo ago Changed · +22 lines fbc545c1123b
- 1mo ago Changed · +4 lines d0b556e4e6ec
- 1mo ago First seen · 115 lines · 50 tokens per session scan A 685a7558264b
capture-worker is an agent published in the GitHub repository Pupok462/open-geo (27 stars, last pushed yesterday), licensed MIT. It adds 50 tokens to every session and 2,324 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 2026-08-30.
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-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-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-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.