Search Term Verdict

Search Term Verdict — Google Ads

Classify Google Ads search queries into actionable verdicts and produce ready-to-paste negative keyword lists with UI paths for implementation.

Read-only by design. This skill does not modify your account. All negative keyword and promotion recommendations are delivered as copy-paste artifacts and Google Ads UI navigation paths.

Account Context

Read ${CLAUDE_PLUGIN_ROOT}/profile/account-profile.md at the start of every run. If it exists:

• Use known account IDs -- skip list_accounts discovery.
• Apply KPI targets as anomaly detection thresholds (e.g., flag CPA > target CPA).
• Note active tests when interpreting performance shifts.
• Check watch list for follow-up items from prior sessions. If it doesn't exist, fall back to list_accounts and suggest running platform-setup.

Data Access

• mcp__google-ads__query: Execute GAQL SELECT queries for term and keyword coverage.
• mcp__google-ads__list_accounts: Confirm account context before running analysis.

Agent Acceleration

When the search term report contains more than 1000 rows, spawn the search-term-classifier agent to classify terms in parallel batches. Pass search term rows with metrics in batches of ~500 and collect consolidated verdicts with n-gram analysis. For reports with 1000 or fewer rows, classify sequentially (no agent needed).

Workflow

Phase 1: Extract data

1. Run references/gaql-queries.md Query A for full search term coverage.
2. Run Query B for keyword-level mapping in Search campaigns.
3. Keep date range default at LAST_30_DAYS unless user requests a different window.
4. Skip rows where search_term_view.status = EXCLUDED for actioning, but count them in coverage notes.

Phase 2: Classify each search term

Assign one verdict per row:

• NEGATE: irrelevant or wasteful term.
• PROMOTE: high-intent term that should become a dedicated keyword.
• INVESTIGATE: ambiguous term requiring user judgment.
• KEEP: term is aligned and performing acceptably.

Use this weight order:

1. Conversion and cost efficiency.
2. Semantic relevance to campaign intent.
3. Match type drift signals.
4. Existing exclusion status.
5. Volume significance.

Use references/verdict-heuristics.md for edge cases and conflict checks.

Phase 3: Build output package

Return three sections:

1. Verdict summary table.
2. Negative keyword package grouped by campaign or ad group level.
3. Promotion candidates with suggested ad group placement.

Every recommendation must include rationale and spend/conversion context.

Negative match type guidance:

• Use EXACT negative when only a specific phrase should be blocked.
• Use PHRASE negative when the core phrase is irrelevant regardless of surrounding words (most common choice).
• Avoid BROAD negatives unless the single word is unambiguously irrelevant.

Level guidance:

• ad_group level: mismatch is scoped to one ad group's theme.
• campaign level: mismatch applies across the entire campaign.
• Account-level (shared negative list): if exclusions are universal, recommend adding terms to a shared negative keyword list.

N-gram analysis:

When search term volume is high:

1. Build 2-gram and 3-gram frequency tables.
2. Rank grams by total spend and zero-conversion spend.
3. Use high-cost recurring grams to accelerate negative mining candidates.
4. Present top n-grams as a separate section to inform bulk negative decisions.

N-gram output should inform suggestions, not replace row-level judgment.

Phase 4: Produce action artifacts

Instead of executing mutations, produce ready-to-use artifacts:

Copy-paste negative keyword lists

Group negatives by match type for easy pasting into Google Ads:

-- PHRASE match negatives (campaign: [Campaign Name]) --
"term one"
"term two"
"term three"

-- EXACT match negatives (campaign: [Campaign Name]) --
[specific query one]
[specific query two]

UI paths for adding negatives

At campaign level: Google Ads > Campaigns > [Campaign Name] > Keywords > Negative keywords > + > paste keywords > select 'Campaign' level > Save

At ad group level: Google Ads > Campaigns > [Campaign Name] > Ad Groups > [Ad Group Name] > Keywords > Negative keywords > + > paste keywords > select 'Ad group' level > Save

Via shared negative keyword list: Google Ads > Tools & Settings > Shared Library > Negative keyword lists > [list name] > + Keywords > paste list > Save Then apply to campaigns: Google Ads > Campaigns > [Campaign Name] > Settings > Negative keyword lists > + > select list > Save

Promotion candidate actions

For PROMOTE verdicts, provide:

• Recommended keyword text and match type.
• Target ad group name.
• UI path: Google Ads > Campaigns > [Campaign Name] > Ad Groups > [Ad Group Name] > Keywords > + > enter keyword > select match type > Save

Output format

## Search Term Verdict - [Date]

### Google Ads: [Name] ([Customer ID])

**Coverage note:** [hidden search-term caveat, data volume, date range]

#### Summary
| Verdict | Count | Spend | Notes |
|---|---:|---:|---|

#### Top N-Grams (by wasted spend)
| N-Gram | Occurrences | Total Spend | Conversions | Suggested Action |
|---|---:|---:|---:|---|

#### Negative Keyword Recommendations
| Keyword | Level | Parent | Match Type | 30d Spend | Reason |
|---|---|---|---|---:|---|

#### Copy-Paste Negative Lists

**Campaign: [Campaign Name]**

-- PHRASE match -- "term"

**Shared List: [Suggested List Name]**

-- PHRASE match -- "universal negative"

#### Promotion Candidates
| Search Term | Campaign | Suggested Ad Group | Conv | CPA | Why promote |
|---|---|---|---:|---:|---|

#### Investigate
| Term | Campaign | 30d Spend | Conv | Why review needed |
|---|---|---:|---:|---|

Guardrails

• Flag potential positive-keyword collisions before recommending negatives.
• Mention search-term privacy threshold and estimated data coverage gap.
• When data volume exceeds 10,000 rows, recommend narrower date/campaign scope.
• Read-only: This skill produces copy-paste artifacts and UI paths only. No account modifications are made.

References

• references/gaql-queries.md
• references/verdict-heuristics.md