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 saski/arnesto --skill user-feedback-synthesizergit clone --depth 1 https://github.com/saski/arnestoWrote 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/saski/arnesto/user-feedback-synthesizer)<a href="https://agentmods.dev/skills/saski/arnesto/user-feedback-synthesizer"><img src="https://agentmods.dev/badge/skills/saski/arnesto/user-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/skills/saski/arnesto/user-feedback-synthesizer"><img src="https://agentmods.dev/badge/skills/saski/arnesto/user-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 | $0.00031 | $0.00408 |
| Opus 5 | $0.00015 | $0.00204 |
| Sonnet 5 | $0.00006 | $0.00082 |
| Haiku 4.5 | $0.00003 | $0.00041 |
Grade A, and why
user-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 5d 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.
This is a copy
100% identical to user-feedback-synthesizer — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Domain Context
This skill implements a proven product management framework. The approach combines best practices from industry leaders and is designed for practical application in day-to-day PM work.
Input Requirements
- Context about your product, feature, or problem
- Relevant data, research, or constraints (recommended but optional)
- Clear articulation of what you're trying to achieve
User Feedback Synthesizer
When to Use
- After conducting user interviews or surveys
- When you have a backlog of support tickets to analyze
- To identify common pain points from feedback across multiple sources
- Before planning your product roadmap to understand user needs
What This Skill Does
Analyzes user feedback data to extract insights, cluster themes, identify severity levels, and provide actionable recommendations that inform product decisions.
Instructions
Act as a user research expert. Help me analyze this collection of user feedback by:
- Clustering feedback into themes
- Identifying severity levels for each theme
- Suggesting potential solutions
- Prioritizing issues by impact and frequency
- Highlighting quick wins
Your feedback data: [paste feedback here]
Best Practices
- Include context about your product and target users
- Mix feedback from multiple sources (interviews, support tickets, surveys, reviews)
- Look for patterns across different user segments
- Note the frequency of each pain point
- Validate insights with follow-up questions to users
Example
Input: 50 support tickets about onboarding + 20 user interview transcripts Output: 3 main themes identified (unclear value proposition, confusing UI, missing integrations), prioritized by impact with specific solution recommendations and estimated effort
Further Reading
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.
- 5d ago First seen · 56 lines · 31 tokens per session scan A 58e6f4cd49d6
user-feedback-synthesizer is a skill published in the GitHub repository saski/arnesto (5 stars, last pushed 2d ago), licensed Unlicense. It adds 31 tokens to every session and 408 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to user-feedback-synthesizer, differing in 0 lines, and is treated as a copy.
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