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 skillmds/skillmd --skill ads-competitorgit clone --depth 1 https://github.com/skillmds/skillmdWrote 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/skillmds/skillmd/ads-competitor)<a href="https://agentmods.dev/skills/skillmds/skillmd/ads-competitor"><img src="https://agentmods.dev/badge/skills/skillmds/skillmd/ads-competitor/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/skillmds/skillmd/ads-competitor"><img src="https://agentmods.dev/badge/skills/skillmds/skillmd/ads-competitor.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 | $0.00068 | $0.00238 |
| Opus 5.5 | $0.00027 | $0.00095 |
| Sonnet 5 | $0.00014 | $0.00048 |
| Haiku 4.5 | $0.00007 | $0.00024 |
Grade A, and why
ads-competitor 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 4d 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 ads-competitor — 2 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.
What it actually says
Competitor Ad Intelligence
- Confirm named competitors, market, geography, customer, objective, and decision.
- Use official transparency libraries, public ads, auction data supplied by the operator, and other terms-compliant sources.
- Record capture date, platform, placement, observable creative/message, landing destination, and source URL.
- Separate direct observations from inferred audience, spend, performance, or strategy; never present estimates as account facts.
- Cluster durable themes, formats, offers, funnel paths, and gaps without copying protected creative or text.
- Return evidence-backed opportunities, risks, experiments, and source records.
Respect platform terms, robots and access controls, copyright, trademarks, and privacy. Do not bypass authentication or scrape private account surfaces.
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.
- 4d ago First seen · 20 lines · 68 tokens per session scan A 064cfb0fcdc5
ads-competitor is a skill published in the GitHub repository skillmds/skillmd (1 stars, last pushed yesterday), licensed MIT. It adds 68 tokens to every session and 238 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to ads-competitor, differing in 2 lines, and is treated as a copy.
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