search-term-miner
Skill2 starsChanged 4 months agoMITView on GitHub
Sort paid search queries into keep, review, and exclude candidates using business fit and real performance evidence. Use to find waste without blindly blocking useful demand.
1 other project uses a version of this.
Adopted by
Most recently taken up by
- riz1k9/claude-ad-skillstheir own version, first seen Aug 13, 2026
Other versions1 project uses a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 1
- riz1k9/claude-ad-skills★ 0same 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 repository11 other files it gives its agents.
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 15
Use a connected Google Ads MCP, or paste a search-term export with campaign, ad group, triggering keyword, match type, clicks, spend, …
- Distinctive: line 39
Do not create a universal spend cutoff.
- Distinctive: line 40
Do not blacklist a word across the account when only a specific query is proven wrong.
- Distinctive: line 41
Search-term reports may omit low-activity queries.
- Distinctive: line 42
Keep facts and classifications separate.
≈ “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
Search Term Miner
Doctrine
The search terms report is where Google shows the demand it actually bought. A zero-conversion query can be clear waste, an under-tested query, or a legitimate offer that converts later. Classify by business fit first, then use spend, outcomes, conversion delay, and the account target to decide how confident the label should be.
When to use
- During a weekly or monthly search-term review.
- Use a connected Google Ads MCP, or paste a search-term export with campaign, ad group, triggering keyword, match type, clicks, spend, conversions, and value.
- Bring the offer, valid customer intents, exclusions, target CPA or ROAS, and conversion delay when available.
Run it
Run a READ-ONLY search-term review. Use live Google Ads data if connected; otherwise use the export I paste. Change nothing.
1. Confirm the business offer, valid acquisition intents, date range, currency, primary conversion, target, and conversion delay. Mark missing context.
2. Classify each useful term as:
- keep: clearly fits the offer and has supporting evidence;
- review: fit or performance is uncertain, or the sample is immature;
- exclude candidate: clearly does not fit the business or has enough reliable evidence of waste.
3. For each term show campaign, ad group, triggering keyword, spend, clicks, conversions, CPA or ROAS when calculable, business-fit reason, confidence, and suggested negative scope for later review.
4. Rank exclude candidates by spend in this report. Protect real offers and known conversion paths, even when words such as "free", "template", or "how to" appear.
5. Show the total spend of exclude candidates inside the supplied report scope. Do not call it a percentage of the whole account unless total account spend was provided.
6. End with three lists: confirmed exclude candidates, needs review, and terms worth building around.
Do not add negatives. Hand confirmed candidates to the Negative Keyword Builder for match-type and collision checks. End with "No changes were made."Guardrails
- Zero conversions is not automatically waste. Consider business fit, spend relative to the target, conversion lag, and sample size.
- Do not create a universal spend cutoff. Use the advertiser's economics and evidence.
- Do not blacklist a word across the account when only a specific query is proven wrong.
- Search-term reports may omit low-activity queries. State that the review covers reported terms, not every search.
- Keep facts and classifications separate. A recommendation is not an account change.
Good output looks like
A ranked, evidence-backed list of queries to keep, review, or send for negative-keyword drafting, with the report's coverage limits stated plainly.
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
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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.

