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.
npx skills add tonydzi/second-brain-starter-kit --skill geo-content-researchgit clone --depth 1 https://github.com/tonydzi/second-brain-starter-kitWrote 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/skills/tonydzi/second-brain-starter-kit/geo-content-research)<a href="https://agentmods.dev/skills/tonydzi/second-brain-starter-kit/geo-content-research"><img src="https://agentmods.dev/badge/skills/tonydzi/second-brain-starter-kit/geo-content-research/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/skills/tonydzi/second-brain-starter-kit/geo-content-research"><img src="https://agentmods.dev/badge/skills/tonydzi/second-brain-starter-kit/geo-content-research.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 | $0.00059 | $0.07020 |
| Opus 5.5 | $0.00024 | $0.02808 |
| Sonnet 5.5 | $0.00012 | $0.01404 |
| Haiku 4.5 | $0.00006 | $0.00702 |
Grade A, and why
geo-content-research 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 6d 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.
This is a copy
98% identical to geo-content-research — 48 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 597 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GEO Content Research — Produce prompts.csv
You are a Generative Engine Optimization (GEO) strategist. Your job is to surface the exact queries people ask AI chatbots about this category, and emit them as a strictly-formatted CSV that downstream pipeline steps can consume.
The core insight: AI engines have no paid ranking. You can't buy a ChatGPT recommendation. They only evaluate content quality, data structure, and source authority. Finding the queries where the brand should be mentioned is the first step — this skill's deliverable.
Output contract: Your final response text IS the deliverable. It MUST be raw CSV matching
prompts.csv.schema.mdexactly. No prose, no code fences, no explanation around the CSV. The harness captures your final output verbatim, validates it against the schema, and fails the artifact if the shape is wrong. See Phase 3 for the exact format.
Scope in autonomous mode: Phases 1–3 only. The legacy Phases 4–6 (Content Blueprint, Content Generation, Authority Infiltration) belong to separate skills (
geo-content-planning,write-seo-geo-content) and are not this skill's job anymore. Do the research, emit the CSV, stop.
How This Skill Works
Three phases, executed in order:
- Product Intelligence — Understand the product, audience, and competitive context (use the brand DNA context provided; don't block on user answers in autonomous mode)
- AI Prompt Research — Discover the exact queries people ask AI chatbots about this category
- Emit prompts.csv — Score, prioritize, and emit the strict CSV deliverable
Phases 4–6 of the legacy version (content blueprints, page generation, authority infiltration) are no longer part of this skill — they live in geo-content-planning and write-seo-geo-content.
Phase 1: Product Intelligence Gathering
Start here every time. Ask the user for:
Required information
- Product/brand name and URL (if live)
- Product category — what is it, what does it do in one sentence
- Target customer — who buys this, what problem does it solve for them
- Key differentiators — what makes this product better or different from competitors
- Price point — approximate range (budget / mid-range / premium)
- Top 3 competitors — brands users compare against
- Any existing content — do they have a blog, reviews, product specs pages?
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 6d ago Changed d8ed8fe7e7a9
- 19d ago Changed · -1 lines ec75547f32a0
- 24d ago Changed · +13 lines c77cad665ba6
- 1mo ago First seen · 585 lines · 59 tokens per session scan A f2aee13f03bd
geo-content-research is a skill published in the GitHub repository tonydzi/second-brain-starter-kit (12 stars, last pushed yesterday), licensed MIT. It adds 59 tokens to every session and 7,020 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 98% identical to geo-content-research, differing in 48 lines, and is treated as a copy.
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