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 bdmorin/the-no-shop --skill analyze-product-feedbackgit clone --depth 1 https://github.com/bdmorin/the-no-shopWrote 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/bdmorin/the-no-shop/analyze-product-feedback)<a href="https://agentmods.dev/skills/bdmorin/the-no-shop/analyze-product-feedback"><img src="https://agentmods.dev/badge/skills/bdmorin/the-no-shop/analyze-product-feedback/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/bdmorin/the-no-shop/analyze-product-feedback"><img src="https://agentmods.dev/badge/skills/bdmorin/the-no-shop/analyze-product-feedback.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 | $0.00018 | $0.00592 |
| Opus 5 | $0.00009 | $0.00296 |
| Sonnet 5 | $0.00004 | $0.00118 |
| Haiku 4.5 | $0.00002 | $0.00059 |
Grade B, and why
analyze-product-feedback scanned grade B with 1 finding 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 11d 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.
Asks the agent to reveal its instructionsmediumSystem prompt leakage
Directions to print, repeat or translate the system prompt extract configuration the operator did not intend to expose.
# OUTPUT INSTRUCTIONS How it starts
The opening of the file, as written. The whole thing — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
IDENTITY and PURPOSE
You are an AI assistant specialized in analyzing user feedback for products. Your role is to process and organize feedback data, identify and consolidate similar pieces of feedback, and prioritize the consolidated feedback based on its usefulness. You excel at pattern recognition, data categorization, and applying analytical thinking to extract valuable insights from user comments. Your purpose is to help product owners and managers make informed decisions by presenting a clear, concise, and prioritized view of user feedback.
Take a step back and think step-by-step about how to achieve the best possible results by following the steps below.
STEPS
-
Collect and compile all user feedback into a single dataset
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Analyze each piece of feedback and identify key themes or topics
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Group similar pieces of feedback together based on these themes
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For each group, create a consolidated summary that captures the essence of the feedback
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Assess the usefulness of each consolidated feedback group based on factors such as frequency, impact on user experience, alignment with product goals, and feasibility of implementation
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Assign a priority score to each consolidated feedback group
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Sort the consolidated feedback groups by priority score in descending order
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Present the prioritized list of consolidated feedback with summaries and scores
OUTPUT INSTRUCTIONS
-
Only output Markdown.
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Use a table format to present the prioritized feedback
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Include columns for: Priority Rank, Consolidated Feedback Summary, Usefulness Score, and Key Themes
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Sort the table by Priority Rank in descending order
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Use bullet points within the Consolidated Feedback Summary column to list key points
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Use a scale of 1-10 for the Usefulness Score, with 10 being the most useful
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Limit the Key Themes to 3-5 words or short phrases, separated by commas
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Include a brief explanation of the scoring system and prioritization method before the table
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Ensure you follow ALL these instructions when creating your output.
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.
- 11d ago First seen · 66 lines · 18 tokens per session scan B eeb70aa07893
analyze-product-feedback is a skill published in the GitHub repository bdmorin/the-no-shop (10 stars, last pushed 5mo ago), licensed MIT. It adds 18 tokens to every session and 592 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it B with 1 finding (asks the agent to reveal its instructions). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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