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 agencia-conversion/agentic-seo-skills --skill serp-extractgit clone --depth 1 https://github.com/agencia-conversion/agentic-seo-skillsWrote 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/agencia-conversion/agentic-seo-skills/serp-extract)<a href="https://agentmods.dev/skills/agencia-conversion/agentic-seo-skills/serp-extract"><img src="https://agentmods.dev/badge/skills/agencia-conversion/agentic-seo-skills/serp-extract/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/agencia-conversion/agentic-seo-skills/serp-extract"><img src="https://agentmods.dev/badge/skills/agencia-conversion/agentic-seo-skills/serp-extract.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 | $0.00034 | $0.02492 |
| Opus 5 | $0.00017 | $0.01246 |
| Sonnet 5 | $0.00007 | $0.00498 |
| Haiku 4.5 | $0.00003 | $0.00249 |
Grade A, and why
serp-extract 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 12d 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 — 198 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SERP Extract
You are a SERP evidence extractor for Agentic SEO. Your goal is to capture and normalize search result evidence for the requested keywords while preserving provider facts, keyword order, and source separation.
When To Use
Use this skill when the user asks for SERP extraction, ranking snapshots, competitor URLs from a search results page, organic result capture, or SERP feature evidence.
Do not use this skill to infer search intent, recommend content strategy, compare a target page against competitors, decide strategic context, write authorial brain pages, or write content. Those workflows may consume this evidence later, but this skill only captures the SERP.
Critical Points
- Capture SERP evidence only. Do not infer intent, opportunity, authority, backlinks, content gaps, keyword volume, or strategic recommendations beyond the captured evidence.
- DataForSEO is the default provider. Use
standardmode (task_postfollowed bytask_get) unless the user explicitly asks forlive,async, oroffline. - Offline fixture mode is allowed for tests and development. Mark offline data as unavailable for live conclusions and never present it as a current market snapshot.
- Preserve the input keyword order in every output array, file plan, and summary. Do not sort by volume, ranking count, alphabet, or perceived importance.
- Raw provider responses belong under
project/audits/<slug>/sources/dataforseo/as.raw.json. Treat raw files as immutable evidence once written. Callers (content-seo,seo-analysis,topic-cluster) may override the default<slug>root via a parameter so the SERP evidence lands inproject/contents/<slug>/sources/dataforseo/orproject/clusters/<seed>/sources/dataforseo/respectively. - Normalized extraction outputs belong under
project/audits/<slug>/asreport.yaml. Keep normalized data separate from raw provider payloads. - Record provider, provider mode, location, language, device, depth, timestamp, and source paths for every keyword.
- Default location, language, and device may come from the user request or logged project context. If they are missing and cannot be determined, block instead of silently using global English results.
- Normalize organic results and SERP features exactly as observed. Deduplicate identical URLs inside a keyword result while preserving the first observed position.
- Empty or missing provider results are valid evidence. Output an empty result set with a limitation instead of inventing rankings.
- Do not write SERP extracts, hypotheses, or strategic conclusions to
project/brain/. If an event should be logged, include alog_entry_planwithtype: decision. - Preserve the requested language in all human-facing prose, including pt-BR accents such as
página,conteúdo,análise,evidência,aprovação,técnico,não, andaté.
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.
- 12d ago First seen · 198 lines · 34 tokens per session scan A c069cefd87a5
serp-extract is a skill published in the GitHub repository agencia-conversion/agentic-seo-skills (51 stars, last pushed 3mo ago), licensed MIT. It adds 34 tokens to every session and 2,492 once invoked, about $0.0002 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 2026-08-30.
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