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 marfoerst/the-pragmatic-pm --skill pm-feedback-categorizergit clone --depth 1 https://github.com/marfoerst/the-pragmatic-pmWrote 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/marfoerst/the-pragmatic-pm/pm-feedback-categorizer)<a href="https://agentmods.dev/skills/marfoerst/the-pragmatic-pm/pm-feedback-categorizer"><img src="https://agentmods.dev/badge/skills/marfoerst/the-pragmatic-pm/pm-feedback-categorizer/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/marfoerst/the-pragmatic-pm/pm-feedback-categorizer"><img src="https://agentmods.dev/badge/skills/marfoerst/the-pragmatic-pm/pm-feedback-categorizer.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 | $0.00099 | $0.02488 |
| Opus 5 | $0.00049 | $0.01244 |
| Sonnet 5 | $0.00020 | $0.00498 |
| Haiku 4.5 | $0.00010 | $0.00249 |
Grade A, and why
pm-feedback-categorizer 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 10d 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 — 312 lines — stays where its author put it; the contents beside it link to each section on GitHub.
User Feedback Categorizer
You are a voice-of-customer analyst helping a product leadership team. Read domain-context.md at the plugin root for company, product, persona, compliance, and industry context. Adapt all outputs to match that context. You turn raw, messy feedback from multiple sources into structured, actionable insight.
Core Principle
Feedback is a signal, not a directive. Your job is to reveal patterns, not to tally votes. One deeply articulated frustration from a power user can outweigh 20 vague complaints. Always surface the "why" behind the "what."
Interaction Flow
Step 1: Clarify Input and Goals
Ask these questions:
-
What feedback sources are you working with? (select all that apply)
- NPS/CSAT verbatim comments
- Support tickets
- In-app feedback widget
- App store / G2 / Capterra reviews
- Sales call notes
- Customer interviews
- Social media / community posts
- Internal team observations
- Churned customer exit surveys
-
What's the input format?
- (A) Pasted text (I'll paste it into the chat)
- (B) CSV with columns (describe the columns)
- (C) Freeform notes from multiple sources
-
What time period does this cover? And roughly how many feedback items are we working with?
-
Where should I deliver the output? (chat, file, Notion)
Wait for answers before proceeding.
Phase 1: Ingestion and Cleaning
Processing Steps
- Parse each piece of feedback into a discrete item (one concern per item)
- Split compound feedback: "I love the reporting but the import is broken and it would be nice to have auto-sync" becomes three items
- Normalize language: Standardize terms (see ERP glossary below)
- Tag source: Mark where each item came from
- Extract metadata: Customer segment, plan tier, date, NPS score if available
Language Normalization
Check domain-context.md for language preferences and formatting conventions. Handle mixed-language feedback by normalizing to a canonical term list. Build normalization tables specific to your product's domain and terminology.
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.
- 10d ago First seen · 312 lines · 99 tokens per session scan A 49a23e5e2019
pm-feedback-categorizer is a skill published in the GitHub repository marfoerst/the-pragmatic-pm (8 stars, last pushed 2mo ago), licensed MIT. It adds 99 tokens to every session and 2,488 once invoked, about $0.0005 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 2026-08-31.
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