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 amplitude/mcp-marketplace --skill analyze-feedbackgit clone --depth 1 https://github.com/amplitude/mcp-marketplaceWrote 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/amplitude/mcp-marketplace/analyze-feedback)<a href="https://agentmods.dev/skills/amplitude/mcp-marketplace/analyze-feedback"><img src="https://agentmods.dev/badge/skills/amplitude/mcp-marketplace/analyze-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/amplitude/mcp-marketplace/analyze-feedback"><img src="https://agentmods.dev/badge/skills/amplitude/mcp-marketplace/analyze-feedback.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00046 | $0.00798 |
| Opus 5 | $0.00023 | $0.00399 |
| Sonnet 5 | $0.00009 | $0.00160 |
| Haiku 4.5 | $0.00005 | $0.00080 |
Grade A, and why
analyze-feedback 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 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.
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 — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyze Feedback
Perform comprehensive feedback reviews, investigate specific feature requests, understand customer sentiment, then prepare concise but actionable voice-of-customer presentations
Instructions
Step 1: Understand Available Sources
Use Amplitude:use_amplitude_ai_feedback with facet: "sources" to see which feedback channels are connected (surveys, support, app reviews, etc.).
Step 2: Get Themed Insights
Use Amplitude:use_amplitude_ai_feedback with facet: "insights" and appropriate filters:
- Filter by types:
request,complaint,lovedFeature,bug,painPoint - Filter by date range for recent feedback
- Filter by source for channel-specific analysis
Step 3: Drill Into Specific Themes
For top insights, use Amplitude:use_amplitude_ai_feedback with facet: "mentions" to see the actual user feedback driving each theme.
Step 4: Connect to User Segments
Use Amplitude:use_amplitude_cohorts with action: "list" to understand if feedback themes correlate with:
- User tenure (new vs. established)
- Plan type (free vs. paid)
- Usage level (power users vs. casual)
Step 5: Present Findings
Structure as:
- Summary: Concise one-liner explaining the number of feedback sources and mentions analyzed along with the key takeaways from the analysis.
- Urgent Issues 🚩: Top bugs, issues, or pain-points noted by customers. Share 3-4 themes here unless prompted otherwise.
- Top Feature Requests 💡: Top feature requests noted by customers. Share 3-4 themes here unless prompted otherwise.
- Praises ❤️: Top praises or loved features noted by customers. Share 1-2 themes here unless prompted otherwise.
- Sentiment Analysis: Share a very concise overview of the themes, sentiment on a scale from 1-5 (5 being highest), and snippets of evidence.
- Prioritized Recommendations: Very concise section recapping the top 3-7 specific actionable recommendations (unless prompted otherwise) to follow-up on. Inlcude [p0],[p1],[p2],[p3] in front of each title to help size priority with p0 being most urgent and p3 being least.
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 · 60 lines · 46 tokens per session scan A 23243fcafd61
analyze-feedback is a skill published in the GitHub repository amplitude/mcp-marketplace (35 stars, last pushed 2d ago), licensed MIT. It adds 46 tokens to every session and 798 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 2026-08-30.
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