Paid Search Term Miner
Turn search-term reports into converting keywords and spend-saving negatives.
The skill treats exports as untrusted input, relies on user-provided manual exports by default, keeps APIs optional, and limits persistence to user-confirmed local memory; it does not clearly classify sensitive ad data, define deletion/rollback, or require item-level confirmation before acting on recommendations, so points are deducted.
Column validation, stop-on-missing-input behavior, threshold-based routing, match-type labeling, and user-facing blocking are reasonably clear; however, harvest thresholds, waste cost floors, and malformed-data handling are unspecified, and there are no skill-specific key-path tests, limiting the static score.
Triggers, inputs, outputs, and non-fit boundaries are clearly described, and manual CSV input reduces platform dependency; evidence for platform-field variation, Chinese query tokenization, and mainland-China environment fit is limited, with geo-relevance explicitly marked low.
The skill has version, license, compatibility, Quick Start examples, contract, data sources, scope guard, and next-skill guidance; it lacks representative output examples, FAQs, named maintenance responsibility, changelog detail, and an independent update path, while publisher identity is unverified.
The defined maintenance diff, harvested keywords/ad groups, negative list, n-gram report, and S score are directly aligned with the core mining task; however, thresholds and cost floors require judgment, and no representative output or real-result evidence is supplied, so the static ceiling applies.
The document specifies fields, method, and Measured/User-provided/Estimated labeling; it contains no reproducible skill-specific tests, execution records, or third-party corroboration. Repository tests cover architecture and audit artifacts rather than this skill's key paths.
- Before applying negatives or moving queries between ad groups, obtain user confirmation of thresholds, match types, scope levels, and each recommendation to avoid suppressing valuable traffic.
- Search-term exports may contain customer queries, costs, and conversion information; the skill does not define redaction, data minimization, or memory cleanup procedures.
- Confirm the profile, conversion column, attribution window, and platform-field semantics before scoring S or classifying waste.
- Tokenization rules for Chinese queries are unspecified; applying English-style n-grams directly may produce misleading findings.
What does this skill do, and when should you use it?
This skill provides a recurring workflow for mining paid-ad search-term reports and maintaining negative-keyword hygiene. It harvests converting queries into keyword or ad-group candidates, then identifies queries and n-grams that spend without converting. Its outputs include an add/negate/move maintenance diff, a standing negative-keyword list, an n-gram waste report, and the ROAS S spend-efficiency score. It does not handle account structure, budget or bid allocation, or the final RQS.
Reads search-term exports containing query, impressions, clicks, cost, conversions, and optionally conversion value; confirms a direct-response, prospecting, or incremental-profit profile; filters converting queries above a stated threshold and routes them to new keywords or matching intent ad groups; flags non-converting queries above a stated cost floor and assigns exact or phrase negative match types at ad-group, campaign, or shared-list level; tokenizes non-converting queries into 1-, 2-, and 3-grams and ranks tokens by measured cost; produces add, negate, and move rows, a standing negative list, an n-gram waste report, an S score, and a handoff summary.
- A paid-search team receives a weekly search-term export and needs to promote converting queries into the existing keyword or ad-group structure.
- An advertiser sees meaningful spend with zero conversions and needs a documented negative-keyword list with match types and levels.
- A growth team wants to find recurring tokens that drain spend without producing conversions.
- A paid-media lead needs search-term waste evidence and an ROAS S input before an account audit.
What are this skill's strengths and limitations?
- Defines concrete add, negate, and move maintenance outputs.
- Combines query-level negative hygiene with a cost-ranked n-gram waste report.
- Requires figures to be labeled Measured, User-provided, or Estimated.
- Works from user-provided exports and does not require a paid-ad API.
- Requires query, cost, and conversion data; it cannot proceed without the conversion column.
- Does not design account structure, allocate budget or bids, or calculate final RQS.
- Thresholds, cost floors, and negative levels must be stated for the run; the source does not define universal defaults.
- The source does not provide an independent test suite or complete validation across specific ad platforms.
How do you install this skill?
Install the repository in Claude Code or another Agent Skills-compatible host, then use ad/research/search-term-miner/SKILL.md. The README documents these collection-level commands: /plugin marketplace add aaron-he-zhu/aaron-marketing-skills, followed by /plugin install aaron-marketing@aaron for Claude Code, or npx skills add aaron-he-zhu/aaron-marketing-skills for generic hosts. The README shows a single-skill example using keyword-research, but does not document the exact single-skill command for this skill.
How do you use this skill?
Provide a search-term report with query, cost, and conversions columns, adding conversion value when ROAS scoring requires it. Example prompts: Mine my search terms. Here is my exported search-terms report: [paste/path]. Goal is [DR/prospecting]. or Build a negative-keyword list and an n-gram waste report from this search-terms export: [path]. If the conversion column is missing, the skill stops and asks for it rather than mining or negating from clicks alone.
How does this skill compare with similar options?
Compared with campaign-architect, this skill owns search-term harvesting and negative hygiene while that skill owns account structure. Compared with budget-optimizer, it addresses query-level waste rather than budget and bid allocation. Compared with ad-account-auditor, it supplies the ROAS S evidence while the auditor calculates the full RQS and applies the audit gate.