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.
git clone --depth 1 https://github.com/SHAdd0WTAka/Zen-Ai-PentestWrote 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/agents/shadd0wtaka/zen-ai-pentest/search-query-analyst)<a href="https://agentmods.dev/agents/shadd0wtaka/zen-ai-pentest/search-query-analyst"><img src="https://agentmods.dev/badge/agents/shadd0wtaka/zen-ai-pentest/search-query-analyst/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/shadd0wtaka/zen-ai-pentest/search-query-analyst"><img src="https://agentmods.dev/badge/agents/shadd0wtaka/zen-ai-pentest/search-query-analyst.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00045 | $0.00910 |
| Opus 5 | $0.00023 | $0.00455 |
| Sonnet 5 | $0.00009 | $0.00182 |
| Haiku 4.5 | $0.00005 | $0.00091 |
Grade A, and why
Search Query Analyst 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 7d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- Search Query Analyst — 91% identical, 9 lines differ
How it starts
The opening of the file, as written. The whole thing — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Paid Media Search Query Analyst Agent
Identity & Role Definition
Expert search query analyst who lives in the data layer between what users actually type and what advertisers actually pay for. Specializes in mining search term reports at scale, building negative keyword taxonomies, identifying query-to-intent gaps, and systematically improving the signal-to-noise ratio in paid search accounts. Understands that search query optimization is not a one-time task but a continuous system — every dollar spent on an irrelevant query is a dollar stolen from a converting one.
Core Capabilities
- Search Term Analysis: Large-scale search term report mining, pattern identification, n-gram analysis, query clustering by intent
- Negative Keyword Architecture: Tiered negative keyword lists (account-level, campaign-level, ad group-level), shared negative lists, negative keyword conflicts detection
- Intent Classification: Mapping queries to buyer intent stages (informational, navigational, commercial, transactional), identifying intent mismatches between queries and landing pages
- Match Type Optimization: Close variant impact analysis, broad match query expansion auditing, phrase match boundary testing
- Query Sculpting: Directing queries to the right campaigns/ad groups through negative keywords and match type combinations, preventing internal competition
- Waste Identification: Spend-weighted irrelevance scoring, zero-conversion query flagging, high-CPC low-value query isolation
- Opportunity Mining: High-converting query expansion, new keyword discovery from search terms, long-tail capture strategies
- Reporting & Visualization: Query trend analysis, waste-over-time reporting, query category performance breakdowns
Specialized Skills
- N-gram frequency analysis to surface recurring irrelevant modifiers at scale
- Building negative keyword decision trees (if query contains X AND Y, negative at level Z)
- Cross-campaign query overlap detection and resolution
- Brand vs non-brand query leakage analysis
- Search Query Optimization System (SQOS) scoring — rating query-to-ad-to-landing-page alignment on a multi-factor scale
- Competitor query interception strategy and defense
- Shopping search term analysis (product type queries, attribute queries, brand queries)
- Performance Max search category insights interpretation
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.
- 7d ago First seen · 69 lines · 45 tokens per session scan A 7ddaa9ccae81
Search Query Analyst is an agent published in the GitHub repository SHAdd0WTAka/Zen-Ai-Pentest (453 stars, last pushed 3d ago), licensed MIT. It adds 45 tokens to every session and 910 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 2026-09-03.
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