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 prashishh/seo-geo-report-engine --skill serp-intelgit clone --depth 1 https://github.com/prashishh/seo-geo-report-engineWrote 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/prashishh/seo-geo-report-engine/serp-intel)<a href="https://agentmods.dev/skills/prashishh/seo-geo-report-engine/serp-intel"><img src="https://agentmods.dev/badge/skills/prashishh/seo-geo-report-engine/serp-intel/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/prashishh/seo-geo-report-engine/serp-intel"><img src="https://agentmods.dev/badge/skills/prashishh/seo-geo-report-engine/serp-intel.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 | $0.00231 | $0.01770 |
| Opus 5 | $0.00115 | $0.00885 |
| Sonnet 5 | $0.00046 | $0.00354 |
| Haiku 4.5 | $0.00023 | $0.00177 |
Grade A, and why
serp-intel 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 10d 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 — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
serp-intel
Live Google SERP intelligence via DataForSEO (the committed data backbone). Its biggest value is
capturing the Google AI Overview and its citations, the single most important AI-search surface,
which neither the Perplexity probe (web-research) nor Ahrefs Brand Radar covers. Codex-safe: pure
stdlib HTTP, no MCP.
Setup
DataForSEO credentials are per-profile (config/secrets.<profile>.env). Check the active profile with
./bin/mkt profile show; switch with ./bin/mkt profile use personal|business. Ahrefs + OpenRouter are
shared across profiles. Check budget: python3 -c "import sys;sys.path.insert(0,'tools');from connectors import dataforseo as d;from lib.config import load_secrets;load_secrets();print(d.balance())".
What this plan's DataForSEO includes (re-verified 2026-07-01: FULLY UNLOCKED)
DataForSEO removed its per-product subscription gate, so everything works now across 5 engines:
- SERP — organic + Google AI Overview + People-Also-Ask + related.
- Labs —
keyword_overview(volume + CPC + keyword difficulty + intent in one call),keyword_difficulty(cheap bulk KD),keyword_ideas(expansion),ranked_keywords(every keyword a domain ranks for — the Ahrefs organic-keywords equivalent),domain_intersection(competitor keyword gap),competitors_domain. We now get KD from DataForSEO directly (no longer Ahrefs-only). - Backlinks —
backlinks_summary,referring_domains,backlinks_bulk_ranks(DR-equiv for ≤1000 domains in one call — ideal for scoring a competitor list). - OnPage —
onpage_instant(single-page technical read). - AI Optimization —
llm_query/llm_models: query ChatGPT / Gemini / Perplexity / Claude directly and capture the answer + its citations. A live, cross-engine GEO-visibility engine.
One caveat: DataForSEO Labs covers ~94 locations and Nepal (2524) is NOT one of them (US 2840 and
India 2356 are). For Labs-unsupported markets, use keyword_volume (Google Ads, any location) and
serp_competitors (SERP-derived, any location).
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.
- 10d ago First seen · 97 lines · 231 tokens per session scan A b9efbb81994b
serp-intel is a skill published in the GitHub repository prashishh/seo-geo-report-engine (5 stars, last pushed 2mo ago), licensed MIT. It adds 231 tokens to every session and 1,770 once invoked, about $0.0012 per session on Opus 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 2026-08-31.
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