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 cwinvestments/memstack --skill feedback-analyzergit clone --depth 1 https://github.com/cwinvestments/memstackWrote 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/cwinvestments/memstack/feedback-analyzer)<a href="https://agentmods.dev/skills/cwinvestments/memstack/feedback-analyzer"><img src="https://agentmods.dev/badge/skills/cwinvestments/memstack/feedback-analyzer.svg" alt="Measured on agentmods" 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 | $0.00061 | $0.02790 |
| Opus 5 | $0.00030 | $0.01395 |
| Sonnet 5 | $0.00012 | $0.00558 |
| Haiku 4.5 | $0.00006 | $0.00279 |
Grade A, and why
memstack-product-feedback-analyzer 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 2d 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 — 299 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Feedback Analyzer: Analyzing customer feedback...
Categorizes, scores, and prioritizes customer feedback from support tickets, reviews, and surveys into actionable reports with feature request rankings, sentiment trends, and action items.
Activation
When this skill activates, output:
Feedback Analyzer: Analyzing customer feedback...
Then execute the protocol below.
Context Guard
| Context | Status |
|---|---|
| User says "analyze feedback", "feedback analysis" | ACTIVE |
| User says "what are customers asking for" | ACTIVE |
| User has support tickets, reviews, or survey data to analyze | ACTIVE |
| User wants competitor pricing or market analysis | DORMANT: use Competitor Analysis |
| User wants to write a PRD from scratch | DORMANT: use PRD Writer |
Common Mistakes
| Mistake | Why It's Wrong |
|---|---|
| "Build what the loudest customer asks for" | Loudest ≠ most valuable. One enterprise client's niche request shouldn't override 500 users' common need. |
| "Count votes to prioritize" | "Most requested" ignores impact and effort. A rarely requested feature might retain your best customers. |
| "Ignore negative reviews" | 1-star reviews reveal real pain. Positive reviews confirm what works, negatives reveal what to fix. |
| "Read feedback literally" | Users describe symptoms, not root causes. "I need an export button" might mean "I can't get data out." |
| "Analyze once, never again" | Feedback is a continuous signal. Batch-analyze monthly or quarterly to spot trends. |
Protocol
Step 1: Collect Feedback Data
If the user hasn't provided feedback data, ask:
- Source: where is the feedback? (support tickets, app reviews, survey responses, social media, sales call notes)
- Volume: how much feedback? (helps determine analysis approach)
- Time range: what period does this cover?
- Format: text dump, CSV, spreadsheet, or screenshot?
- Product context: any recent launches, changes, or known issues?
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.
- 2d ago Changed 0791e1095eb2
- 3d ago First seen · 299 lines · 61 tokens per session scan A da3c55948e0e
memstack-product-feedback-analyzer is a skill published in the GitHub repository cwinvestments/memstack (419 stars, last pushed today), licensed MIT. It adds 61 tokens to every session and 2,790 once invoked, about $0.0003 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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