competitor-ad-teardown
Skill0 starsChanged 3 months agoMITView on GitHub
Analyzes a competitor's currently-running public ads — using ad longevity to separate proven winners from tests — and turns their angles into specific positioning openings for your own offer. Use whenever someone mentions competitor ads, the Meta Ad Library, Google Ads Transparency Center, "what are my competitors running", ad inspiration, swipe files, or asks how a rival is advertising. This covers paid advertising creative specifically — for competitive research on pricing, features, SEO, or organic content, it does not apply.
1 other project uses a version of this.
Adopted by
Most recently taken up by
- kelpi-ai/meta-ads-skillstheir own version, first seen Jul 2, 2026
Other versions1 project uses a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 1
- kelpi-ai/meta-ads-skills★ 3same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository6 other files it gives its agents.
- ad-fatigue-detectorSkill
- budget-pacerSkill
- daily-auditorSkill
- negative-keyword-builderSkill
- quality-score-fixerSkill
- search-term-minerSkill
The file
We split this skill into the rules it gives an agent
5 rules. About 0 are common, 5 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈5
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 14
All public, all first-party disclosure — no scraping of private data, no impersonation:
- Distinctive: line 20
Ask the person for the competitor's exact brand name or page, their market/region (libraries are region-filtered and results differ), and …
- Distinctive: line 22
If a browser tool is available, look the ads up directly.
- Distinctive: line 35
Always report the start date alongside every ad you cite, and lead the analysis with the long-runners.
- Distinctive: line 91
Keep it honest: Use only publicly disclosed library data.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Competitor Ad Teardown
Competitor ad libraries are public, and almost everyone reads them wrong. They screenshot a few ads, note that "they use social proof", and produce a swipe file that changes nothing.
The value is in two things people skip: using how long an ad has been running as a proxy for whether it works, and turning the pattern into an opening for a different position rather than a copy of theirs.
Where the data comes from
All public, all first-party disclosure — no scraping of private data, no impersonation:
- Meta Ad Library (
facebook.com/ads/library) — every active Meta ad, searchable by advertiser, with start dates. This is the richest source because of those dates. - Google Ads Transparency Center (
adstransparency.google.com) — active Google/YouTube ads by advertiser and region, with date ranges. - TikTok Creative Center, LinkedIn Ads Library — same idea, varying detail.
Ask the person for the competitor's exact brand name or page, their market/region (libraries are region-filtered and results differ), and their own offer and positioning — without that last piece you can describe what a competitor does but not what it means for them.
If a browser tool is available, look the ads up directly. If not, ask the person to open the library and paste what they see, and tell them precisely what to capture: ad copy, format, start date, and the landing page it points to.
How to read what you find
Longevity is the signal
This is the core technique. Ad platforms let advertisers run anything; only ads that make money survive.
- Running 30+ days, and especially 90+ → validated. Someone is paying for it every day because the math works. Treat these as the competitor's proven positions.
- Running under 2 weeks → a test. Interesting for direction, but no evidence it works. Don't build strategy on it.
- Many near-identical variants running simultaneously → an active testing program and usually a well-funded account. Note which element varies between them: whatever they're testing is what they believe drives results.
- One ad running for a year → their control. This is the single most informative asset in the library; it represents the message they've never managed to beat.
Always report the start date alongside every ad you cite, and lead the analysis with the long-runners.
Then classify the angles
For each proven ad, extract: the core promise, the angle it uses to make that promise credible, the offer mechanic (discount, trial, bundle, guarantee, financing), the format, and the audience it implies (the language and imagery tell you who they think they're talking to).
Angles cluster into a small number of families — price/value, speed/convenience, status/identity, risk reversal, fear/loss avoidance, social proof, novelty, authority. Tally which families they occupy heavily and which they leave empty.
Find the opening
The output people actually need is a gap, and gaps come in a few shapes:
- Unoccupied angle — they compete entirely on price; nobody in the category is claiming reliability.
- Unserved segment — every ad speaks to enterprise buyers; SMB is unaddressed.
- Weak proof — they claim a result but never evidence it; you have case studies.
- Offer friction — their conversion path demands a demo call; a self-serve trial undercuts it.
- Format absence — everything is static; the category has no one doing short-form video well.
A gap is only real if you can credibly fill it. Check each against the person's actual offer and say so; an opening you can't service is just trivia.
What to produce
COMPETITOR TEARDOWN — [competitor], [market], [date checked]
[n] active ads · [n] running 30+ days · oldest running since [date]
PROVEN POSITIONS (long-runners — what's paying for itself)
[start date · days live] [angle] — [the promise, in their words]
…
ACTIVE TESTS (recent, unproven)
…
ANGLE MAP
Heavily occupied: […]
Absent: […]
YOUR OPENINGS
1. [gap] — why it's open, and the specific claim you could make instead
…Close with two or three ad concepts for the person's own offer that exploit the strongest opening — each as a positioning brief (promise, angle, proof, offer), not finished copy they'd paste unchanged.
Judgment worth applying
Longevity is evidence, not proof. Big brands run ads for reasons unrelated to direct response, and a long-running ad may simply be neglected. Weigh it, don't worship it.
You are not seeing their results. The library shows what runs, never what it costs or returns. Never state or imply you know a competitor's performance — say "still running after 6 months, which suggests it works", not "their best performer".
Don't copy — position against. Beyond the legal problem with copying creative, running the same angle as an incumbent with a bigger budget is a losing trade. The point of the analysis is differentiation.
Small samples mislead. A competitor with 4 ads tells you little. Say when the sample is too thin, and consider widening to 3–4 competitors to see genuine category patterns.
Check the landing page too. The ad is half the message; where it goes reveals the offer, the funnel, and often the price. Note when you couldn't check it.
Keep it honest. Use only publicly disclosed library data. Don't attempt to access private account data, don't impersonate a competitor, and don't reproduce their creative assets — describe them.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

