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 kelpi-ai/meta-ads-skills --skill competitor-ad-teardowngit clone --depth 1 https://github.com/kelpi-ai/meta-ads-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/kelpi-ai/meta-ads-skills/competitor-ad-teardown)<a href="https://agentmods.dev/skills/kelpi-ai/meta-ads-skills/competitor-ad-teardown"><img src="https://agentmods.dev/badge/skills/kelpi-ai/meta-ads-skills/competitor-ad-teardown/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/kelpi-ai/meta-ads-skills/competitor-ad-teardown"><img src="https://agentmods.dev/badge/skills/kelpi-ai/meta-ads-skills/competitor-ad-teardown.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 | $0.00051 | $0.00631 |
| Opus 5 | $0.00026 | $0.00316 |
| Sonnet 5 | $0.00010 | $0.00126 |
| Haiku 4.5 | $0.00005 | $0.00063 |
Grade A, and why
competitor-ad-teardown 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 — 39 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Competitor Ad Teardown
Doctrine
Your competitors are running your angle experiments for you, in public, for free. The Meta Ad Library shows every active ad a page runs. Two signals separate proven angles from noise: an angle with many creative variations (nobody makes 12 versions of a loser) and an angle that has run for months (nobody pays to keep a loser alive). You are not stealing ads. You are reading the market's already-graded homework, then writing your own answer.
When to use
- Before Angle Writer, to seed it with market-proven directions.
- When your tested angles all plateaued and you need fresh hypotheses.
- Works with no MCP: the Ad Library is public (facebook.com/ads/library). Web access helps.
Run it
Competitor ad teardown for my offer: [ONE-LINE OFFER]
My competitors: [2-5 NAMES OR PAGE URLS. If I only know one, suggest lookups for adjacent players.]
For each competitor, using the Meta Ad Library (facebook.com/ads/library, search their page, country: all):
1. PROVEN ANGLES: group their active ads by the underlying message, not the visual. Flag groups with 3+ variations (they are paying to scale that message) and any ad running 90+ days (it is paying them back).
2. For each proven angle, extract: WHO it targets (read it off the creative), the PAIN or desire it names, the PROMISE it makes, and the emotional register (fear, status, relief, belonging).
3. Their hero message (the one claim their whole account leans on) and their visible differentiator.
4. THE GAPS: which of my potential buyers do their angles ignore? Which pains does nobody in this market name? That white space is my opportunity list.
Then give me 3-5 angle hypotheses for MY offer: each one either (a) a proven market angle re-aimed at my differentiator, or (b) a gap angle nobody is running. Never copy their copy; the output is directions, not text.
Guardrails
- Never reproduce a competitor's copy or creative. Patterns are free; plagiarism is a brand risk and usually a losing move anyway (their angle is fit to their offer, not yours).
- Never cite a competitor's implied results as fact ("they must be printing money"). Longevity and variation count are the only honest signals the Library gives.
- Date-stamp the teardown. Ad Libraries rot; a 6-month-old teardown is a history lesson.
What ships with it
1 file 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 · 39 lines · 51 tokens per session scan A c065c448365e
competitor-ad-teardown is a skill published in the GitHub repository kelpi-ai/meta-ads-skills (3 stars, last pushed 2mo ago), licensed MIT. It adds 51 tokens to every session and 631 once invoked, about $0.0003 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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