memstack-product-feedback-analyzer

memstack-product-feedback-analyzer is a skill for Claude Code from cwinvestments/memstack. It costs 61 tokens per session (2,790 once invoked), scanned A, original, MIT.

A guide for analyzing customer feedback from support tickets, reviews, and surveys by grouping, scoring, and ranking what people say.

In plain words
What is it for?
Use it to identify feature requests, track sentiment, rank needs, and produce action items.
Why use it?
It turns scattered feedback into priorities instead of letting isolated requests determine the product roadmap.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the memstack plugin — 87 skills, 2 commands, 6 hooks shipped together

Good fit Use it to identify feature requests, track sentiment, rank needs, and produce…

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Install with agentmods
npx agentmods add skills/cwinvestments/memstack/feedback-analyzer
Install

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.

Any agent
npx skills add cwinvestments/memstack --skill feedback-analyzer
Clone the repo
git clone --depth 1 https://github.com/cwinvestments/memstack

Made for: Claude Code.

Or install memstack, the plugin that ships this one along with the rest of its 87 skills, 2 commands, 6 hooks.

Wrote 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.

agentmods badge for memstack-product-feedback-analyzer

README.md
[![agentmods](https://agentmods.dev/badge/skills/cwinvestments/memstack/feedback-analyzer.svg)](https://agentmods.dev/skills/cwinvestments/memstack/feedback-analyzer)
Your own site
<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>
Per session 61 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,790 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 2d ago against content hash 0791e1095eb2, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

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.

skills/product/feedback-analyzer/SKILL.md · 299 lines

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:

  1. Source: where is the feedback? (support tickets, app reviews, survey responses, social media, sales call notes)
  2. Volume: how much feedback? (helps determine analysis approach)
  3. Time range: what period does this cover?
  4. Format: text dump, CSV, spreadsheet, or screenshot?
  5. Product context: any recent launches, changes, or known issues?

Read the full file on GitHub · 299 lines

Changes

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.

  1. 2d ago Changed 0791e1095eb2
  2. 3d ago First seen · 299 lines · 61 tokens per session scan A da3c55948e0e

Subscribe to this mod's changes

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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