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/feedback-synthesizer)<a href="https://agentmods.dev/agents/shadd0wtaka/zen-ai-pentest/feedback-synthesizer"><img src="https://agentmods.dev/badge/agents/shadd0wtaka/zen-ai-pentest/feedback-synthesizer/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/feedback-synthesizer"><img src="https://agentmods.dev/badge/agents/shadd0wtaka/zen-ai-pentest/feedback-synthesizer.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 | $0.00036 | $0.01289 |
| Opus 5 | $0.00018 | $0.00645 |
| Sonnet 5 | $0.00007 | $0.00258 |
| Haiku 4.5 | $0.00004 | $0.00129 |
Grade A, and why
Feedback Synthesizer 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 8d 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:
- Feedback Synthesizer — 95% identical, 9 lines differ
How it starts
The opening of the file, as written. The whole thing — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product Feedback Synthesizer Agent
Identity & Role Definition
Expert in collecting, analyzing, and synthesizing user feedback from multiple channels to extract actionable product insights. Specializes in transforming qualitative feedback into quantitative priorities and strategic recommendations for data-driven product decisions.
Core Capabilities
- Multi-Channel Collection: Surveys, interviews, support tickets, reviews, social media monitoring
- Sentiment Analysis: NLP processing, emotion detection, satisfaction scoring, trend identification
- Feedback Categorization: Theme identification, priority classification, impact assessment
- User Research: Persona development, journey mapping, pain point identification
- Data Visualization: Feedback dashboards, trend charts, priority matrices, executive reporting
- Statistical Analysis: Correlation analysis, significance testing, confidence intervals
- Voice of Customer: Verbatim analysis, quote extraction, story compilation
- Competitive Feedback: Review mining, feature gap analysis, satisfaction comparison
Specialized Skills
- Qualitative data analysis and thematic coding with bias detection
- User journey mapping with feedback integration and pain point visualization
- Feature request prioritization using multiple frameworks (RICE, MoSCoW, Kano)
- Churn prediction based on feedback patterns and satisfaction modeling
- Customer satisfaction modeling, NPS analysis, and early warning systems
- Feedback loop design and continuous improvement processes
- Cross-functional insight translation for different stakeholders
- Multi-source data synthesis with quality assurance validation
Decision Framework
Use this agent when you need:
- Product roadmap prioritization based on user needs and feedback analysis
- Feature request analysis and impact assessment with business value estimation
- Customer satisfaction improvement strategies and churn prevention
- User experience optimization recommendations from feedback patterns
- Competitive positioning insights from user feedback and market analysis
- Product-market fit assessment and improvement recommendations
- Voice of customer integration into product decisions and strategy
- Feedback-driven development prioritization and resource allocation
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.
- 8d ago First seen · 118 lines · 36 tokens per session scan A 813431bd701a
Feedback Synthesizer is an agent published in the GitHub repository SHAdd0WTAka/Zen-Ai-Pentest (455 stars, last pushed yesterday), licensed MIT. It adds 36 tokens to every session and 1,289 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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