Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add TrueClicks/claude-plugins --skill skill-015-ngram-analysisgit clone --depth 1 https://github.com/TrueClicks/claude-pluginsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/trueclicks/claude-plugins/skill-015-ngram-analysis)<a href="https://agentmods.dev/skills/trueclicks/claude-plugins/skill-015-ngram-analysis"><img src="https://agentmods.dev/badge/skills/trueclicks/claude-plugins/skill-015-ngram-analysis.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00038 | $0.00777 |
| Opus 5 | $0.00019 | $0.00388 |
| Sonnet 5 | $0.00008 | $0.00155 |
| Haiku 4.5 | $0.00004 | $0.00078 |
Grade A, and why
skill-015-ngram-analysis scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill 015: N-gram Analysis for Search Term Patterns
Purpose
Analyze 1-word, 2-word, and 3-word combinations (n-grams) within search terms to identify systemic patterns of waste or opportunity. N-gram analysis reveals patterns invisible at the individual query level.
Data Requirements
Data Source: Standard
Standard Data:
data/performance/campaigns/*/*/search_terms_metrics_30_days.md- Search query performancedata/account/campaigns/*/negative_keywords.md- Existing negativesdata/account/shared_negative_lists.md- Shared negative lists
Reference GAQL:
SELECT
search_term_view.search_term,
metrics.impressions,
metrics.clicks,
metrics.cost_micros,
metrics.conversions,
metrics.conversions_value
FROM search_term_view
WHERE segments.date DURING LAST_30_DAYS
AND metrics.impressions > 0
Use /google-ads:get-custom if you need different date ranges or additional metrics.
Analysis Steps
- Extract n-grams: Tokenize search terms into 1-grams, 2-grams, and 3-grams; normalize (lowercase, remove punctuation)
- Aggregate metrics: For each n-gram, sum metrics from all containing search terms
- Identify waste patterns: Flag n-grams with Cost >= $50 AND Conversions = 0, or appearing in 5+ zero-conversion terms
- Identify opportunity patterns: Flag n-grams with Conv >= 3 AND CPA below target, or Conv Rate >= 5%
- Cross-reference negatives: Check if wasteful n-grams already covered by existing negatives
Thresholds
| Condition | Severity |
|---|---|
| N-gram Cost >= $100 AND Conv = 0 | Critical |
| N-gram in 10+ terms AND Conv = 0 | Critical |
| N-gram Cost >= $50 AND Conv = 0 | Warning |
| N-gram Conv >= 5 AND CPA <= Target CPA | Info (opportunity) |
Output
Use Short format by default. Use Detailed if user requests comprehensive analysis.
Short:
## N-gram Analysis Audit
**Account:** [Name] | **Search Terms:** [X] | **Patterns Found:** [Y]
### Critical ([Count])
- **"[n-gram]"**: $[X] spent across [Y] terms, 0 conversions → Add as phrase negative
### Warnings ([Count])
- **"[n-gram]"**: $[X] spent, 0 conversions → Add as phrase negative
### Opportunities ([Count])
- **"[n-gram]"**: [X] conversions at $[Y] CPA → Create dedicated ad group
### Recommendations
1. Add "[n-gram]" as negative (saves ~$[X]/month)
2. Create ad group for "[n-gram]" theme
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 80 lines · 38 tokens per session scan A 690f57de972c
skill-015-ngram-analysis is a skill published in the GitHub repository TrueClicks/claude-plugins (2 stars, last pushed 2mo ago), licensed MIT. It adds 38 tokens to every session and 777 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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