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 seranking-planable/smm-skills --skill ai-search-gaps-to-social-campaigngit clone --depth 1 https://github.com/seranking-planable/smm-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/seranking-planable/smm-skills/ai-search-gaps-to-social-campaign)<a href="https://agentmods.dev/skills/seranking-planable/smm-skills/ai-search-gaps-to-social-campaign"><img src="https://agentmods.dev/badge/skills/seranking-planable/smm-skills/ai-search-gaps-to-social-campaign/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/seranking-planable/smm-skills/ai-search-gaps-to-social-campaign"><img src="https://agentmods.dev/badge/skills/seranking-planable/smm-skills/ai-search-gaps-to-social-campaign.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 | $0.00170 | $0.02388 |
| Opus 5 | $0.00085 | $0.01194 |
| Sonnet 5 | $0.00034 | $0.00478 |
| Haiku 4.5 | $0.00017 | $0.00239 |
Grade A, and why
ai-search-gaps-to-social-campaign 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.
This is a copy
100% identical to ai-search-gaps-to-social-campaign — 0 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 — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI-search gaps → social campaign
Use SE Ranking's AI Search data to see which prompts and narratives a brand owns, which competitors own, and which are wide open — then build social content in Planable that stakes a claim in the missing narratives, and instrument it so impact is measurable.
Scope note (read this). SE Ranking's AI Search MCP tools expose brand presence, link presence, share of voice, and the prompts behind them. They do not expose sentiment scoring. Do not report or imply sentiment from these tools. Social content is one lever on AI visibility — LLM citation is also driven by website content and authority, which is outside what these two MCPs publish.
Prerequisites
- SE Ranking MCP connected (AI Search Data API; optionally a project for the AI Result Tracker, which enables ongoing prompt tracking).
- Planable MCP connected, with the destination workspace and pages.
- The user provides: target domain + brand name, country (default
us), competitor domains + brand names (up to 10), and optionally which engines to focus on (default: all ofai-overview,ai-mode,chatgpt,perplexity,gemini).
Connector health check
Before doing anything else, verify both MCPs are reachable:
- SE Ranking: call
DATA_getSubscription. If it fails or returns an auth error, stop immediately and tell the user:"The SE Ranking connector isn't responding — please reconnect it before we continue. Setup guide: https://seranking.com/api/integrations/mcp/"
- Planable: call
list_workspaces. If it fails or returns an auth error, stop immediately and tell the user:"The Planable connector isn't responding — please reconnect it before we continue. Setup guide: https://help.planable.io/hc/en-us/articles/27538577098780-How-to-connect-Planable-MCP-to-your-AI-tools"
Only continue to the process steps below once both calls return a successful response.
Process
1. Resolve the brand and scope
If the user gives a domain but not the exact brand string, call DATA_getAiSearchBrand(target, source) to get the name SE Ranking attributes to it. Do the same for each competitor. Confirm the Planable workspace and target platforms.
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 · 109 lines · 170 tokens per session scan A bbc51820c20c
ai-search-gaps-to-social-campaign is a skill published in the GitHub repository seranking-planable/smm-skills (3 stars, last pushed 2mo ago), licensed MIT. It adds 170 tokens to every session and 2,388 once invoked, about $0.0009 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to ai-search-gaps-to-social-campaign, differing in 0 lines, and is treated as a copy.
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