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 RightBlogger/bloggingskills --skill serp-analysisgit clone --depth 1 https://github.com/RightBlogger/bloggingskillsWrote 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/rightblogger/bloggingskills/serp-analysis)<a href="https://agentmods.dev/skills/rightblogger/bloggingskills/serp-analysis"><img src="https://agentmods.dev/badge/skills/rightblogger/bloggingskills/serp-analysis/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/rightblogger/bloggingskills/serp-analysis"><img src="https://agentmods.dev/badge/skills/rightblogger/bloggingskills/serp-analysis.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 | $0.00118 | $0.00924 |
| Opus 5 | $0.00059 | $0.00462 |
| Sonnet 5 | $0.00024 | $0.00185 |
| Haiku 4.5 | $0.00012 | $0.00092 |
Grade A, and why
serp-analysis 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 — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SERP analysis
You read the current top results for one keyword and report what it takes to win the page. You return an editorial angle and a brief, not a list of words to sprinkle in. The point is to find where the leaders are weak, not to copy them.
Initial assessment
- If
.agents/blog-context.mdexists, read it first. It sets the niche, audience, and target country and language, and the SERP differs by all of them. Honor those over any default. - Confirm the target keyword. If the user gave a URL instead, work out the keyword it is built to rank for and analyze against that. If neither the user nor the context fixes a country and language, ask before guessing, or note your assumption in the output.
- Check what you can fetch. If a search or SERP tool is connected (for example an MCP that returns results, or the ability to fetch result URLs directly), use it. If nothing can reach the web, say so plainly and ask the user to paste the top few competing pages so you can still run the analysis.
Method
- Pull the top three to five organic results. Skip ads and skip pure aggregators unless they genuinely compete. Note who ranks: the kind of site, not just the URL, since a forum thread and a vendor guide signal very different things about the page.
- Read the dominant intent. From the titles and the pages themselves, decide what the searcher actually wants: a how-to, a comparison, a definition, a buying decision, a tool. Get this wrong and depth will not save the piece.
- Map the table stakes. List the subtopics and headings that nearly every result covers. These are the sections a reader expects; leaving one out reads as a gap, not a choice.
- Spot the optional angles. Note the subtopics only some pages cover. These are where the field disagrees about what matters, and where a sharper take can live.
- Find the gap. This is the work. Look for what is missing, thin, outdated, or generic across the results: a step nobody shows, a claim nobody backs, advice that aged out, a question left hanging. Look for where you can be more specific, more current, or more credible than what ranks now.
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 · 75 lines · 118 tokens per session scan A 367cc33a9a83
serp-analysis is a skill published in the GitHub repository RightBlogger/bloggingskills (2 stars, last pushed 2mo ago), licensed MIT. It adds 118 tokens to every session and 924 once invoked, about $0.0006 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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