Analyzing Own Ads

Own ads

Audit what a company is advertising itself, work out what is holding, and map what it already covers. Research only — nothing is generated, posted or planned here.

Not for competitors' ads — that is researching-competitor-ads; not for deciding what to make next — that is planning-campaigns.

Workflow

Step 1: Settle whose page this is

You need a brand name and a website.

• Read them from the brand profile in ./supercmo-company, where one exists. Where it doesn't, move on.
• Ask the user for whichever is missing.
• A website but no name → take the brand name off the site and carry on.

Step 2: Scope the run

Ask once, bundled into a single message, always with a free-text way out. Skip any of these the brief already answers.

Take whatever the brief says about what they are after, in their own words, and don't ask again. Carry those words through to the audit, which closes by answering them.

Where nothing is given and nothing is answered, run it neutrally: a quick scan, the market the user's own site sells to, and an audit that says no goal was set. Say in one sentence what you took, and keep going.

Step 4's script holds these numbers and enforces them — pass it the depth and don't re-count.

Step 3: Pull the ads

Read references/pulling-own-ads.md and follow it. Pull the live set and the stopped set separately, and keep them apart.

Every pull is written to a response file, and the call returns its path in saved.path. All of a run's responses land in one directory — note that directory, it is Step 4's only input. Read saved.cursor for the next page. Don't take the ads apart yourself.

Step 4: Build the ledger

Run the script over the directory the responses were saved to:

python3 <skill-dir>/scripts/build_ledger.py <responses-dir> --depth <quick|detailed> --out <saved.output_dir>

--depth is what Step 2 settled. --out is saved.output_dir, the same on every response of the run — the folder the tool set aside for it. Run the script by its full path rather than moving into the skill directory. Every file this run produces goes in that folder.

Read ledger.md. It holds the counts and campaigns, the ads to watch with their media URLs, the copy every ad ran with — grouped so repeated wording shows as repeated — and the row behind every ad. Say in one line how many ads are in the ledger and how many are marked to watch, then carry on — don't stop for approval. The cap lines at the top of the file go into the audit's coverage section verbatim.

Step 5: Watch the ads

Read references/reading-one-ad.md and follow it.

ledger.md's Watch these section lists each ad's ref and its media URL, split into videos and stills. Send them in batches of at most ten — video_analysis for the videos, image_analysis for the stills. They are watched in parallel, so a batch takes about as long as its slowest ad.

Write each teardown to per-ad-teardowns.md in the run's folder, as its batch comes back — the ref as a heading, then the teardown verbatim. Never watch the same ref twice, and never watch an ad the ledger didn't mark.

Step 6: Read the set and write the audit

Read references/reading-own-ads.md and follow it. It writes own-ads-audit.md into the run's folder.

Name the patterns from the teardowns, then check them against the ledger, citing refs and counts. A claim with no refs behind it doesn't go in.

Audit the whole ledger, not only the ads you watched. Every row carries run length, status, format, campaign and call-to-action, so the run-length distribution, the format mix and the campaign structure are drawn from all of them — including the rows marked no (outside the watch allowance) or no (same creative as an ad already watched). Only findings about how an ad is built come from the teardowns, and those are counted over the ads actually read.

The ads read are the longest-running, live and stopped taken separately. That is a sample of the long end, not of the whole set — so the newest ads are in the ledger but mostly unwatched, and any claim about what the account is trying now rests on metadata and copy.

Step 7: Hand it over

The run's folder holds ledger.md, per-ad-teardowns.md and own-ads-audit.md.

Open the reply by answering the user's question, in their words. Use what they asked for in the brief, or what they said at Step 2.

If they never asked for anything specific, give what is covered — the axes the account already runs, with counts, and the ones with nothing on them.

Back the answer with the two or three findings from the audit it rests on, each with its count and refs. Nothing else from the audit goes in the reply — it is on disk. Close with the folder.

Edge cases

• error: "no_provider_configured" on any tool → relay the tool's hint (the user must set their key).
• A pull comes back empty under the market filter → record it as empty for that market. Don't re-pull without country.
• search_companies returns a reseller or a similarly-named business → show the candidates and let the user pick. A misattributed own ad reports creative they have never run.
• build_ledger.py says a response is unprojected → that pull went out without trim: true, which is the default. Pull it again with trim: true and re-run the script.
• The ledger holds an advertiser this run never asked about → the responses directory carries an earlier run's pulls. Pass this run's response files instead of the directory; the script takes a list of paths.
• build_ledger.py reports fewer ads than you pulled → a response in the directory failed or came back empty and was skipped. Open the first lines of each file to find it, relay what it says, and re-run that pull.
• video_analysis rejects a clip as too large, or an ad's creative can't be fetched → record that ref as unwatched in per-ad-teardowns.md and name it in the audit's coverage. The row stays in the ledger and in every count that comes off metadata.
• The user asks for spend, CPA, ROAS, CTR or any real result → not here. The public library carries none of it. Say so plainly and offer the run-length proxy instead.

Scripts

• scripts/build_ledger.py — reads the run's responses and writes ledger.md (Step 4). Python 3, standard library only; --help gives the full usage.

Reference

• references/pulling-own-ads.md — reaching the brand's own page and pulling what it runs: the endpoints and their accepted values, proving the page is the brand's own and not a reseller's, paging deep enough to reach the long-runners, getting the retired ads, what each ad comes back carrying, and the traps that bill for nothing (Step 3).
• references/reading-one-ad.md — the prompts sent to the vision model for a video and for a still, and how each teardown is recorded (Step 5).
• references/reading-own-ads.md — turning the teardowns and the ledger into own-ads-audit.md: coverage and limits, naming a pattern rather than assuming one, the lines of inquiry, how much evidence a conclusion needs, and closing without recommending (Step 6).