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 nicofains1/agentic-ads --skill openclaw-skillgit clone --depth 1 https://github.com/nicofains1/agentic-adsWrote 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/nicofains1/agentic-ads/openclaw-skill)<a href="https://agentmods.dev/skills/nicofains1/agentic-ads/openclaw-skill"><img src="https://agentmods.dev/badge/skills/nicofains1/agentic-ads/openclaw-skill/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/nicofains1/agentic-ads/openclaw-skill"><img src="https://agentmods.dev/badge/skills/nicofains1/agentic-ads/openclaw-skill.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 | $0.00017 | $0.01137 |
| Opus 5.5 | $0.00007 | $0.00455 |
| Sonnet 5.5 | $0.00003 | $0.00227 |
| Haiku 4.5 | $0.00002 | $0.00114 |
Grade A, and why
agentic-ads 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 1mo 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 — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agentic Ads — Sponsored Suggestions Skill
You have access to a sponsored ads system that pays you (the bot developer) when you show relevant ads to users. Use it responsibly to enhance user experience while earning revenue.
When to Search for Ads
Search for ads only when the user is asking for product or service recommendations. Good triggers:
- "Can you recommend..." / "What's the best..."
- "I'm looking for..." / "Help me find..."
- Shopping, product comparison, service discovery queries
- "Where can I buy..." / "Best deals on..."
Do NOT search for ads when:
- The user is asking factual questions, coding help, or general knowledge
- The user has opted out of ads (said "no ads", "stop showing ads", etc.)
- The conversation is sensitive (health, legal, financial advice)
How to Search
Call the search_ads tool with context extracted from the user's message:
search_ads({
query: "natural language description of what the user wants",
keywords: ["specific", "product", "terms"],
category: "product category if clear",
geo: "US", // user's country if known
language: "en",
max_results: 3
})
Tips for good matching:
- Extract 2-5 specific keywords from the user's intent
- Include the product category when obvious (e.g., "footwear", "electronics", "streaming")
- Pass geo/language when you know the user's location
How to Evaluate Results
Only show an ad if it is genuinely relevant to what the user asked for.
- Check
relevance_score— higher is better (0.0 to 1.0) - Read the
creative_text— does it actually help answer the user's question? - If no ads are relevant, don't show any. The user's trust is more important than ad revenue.
How to Present Ads
- Always disclose: Mark sponsored content clearly. Use phrases like "Sponsored suggestion" or a brief note.
- Integrate naturally: Weave the ad into your response as a helpful recommendation, not a banner ad.
- Limit frequency: Show at most 1-2 sponsored suggestions per response.
- Provide value: The ad should genuinely help the user. If it doesn't add value, skip it.
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.
- 1mo ago First seen · 124 lines · 17 tokens per session scan A 66a27f3ee2f2
agentic-ads is a skill published in the GitHub repository nicofains1/agentic-ads (3 stars, last pushed 6mo ago), licensed MIT. It adds 17 tokens to every session and 1,137 once invoked, about $0.0001 per session on Opus 5.5. 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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