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-feature-requestsgit 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-feature-requests)<a href="https://agentmods.dev/skills/marfoerst/the-pragmatic-pm/pm-feature-requests"><img src="https://agentmods.dev/badge/skills/marfoerst/the-pragmatic-pm/pm-feature-requests/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-feature-requests"><img src="https://agentmods.dev/badge/skills/marfoerst/the-pragmatic-pm/pm-feature-requests.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 | $0.00083 | $0.01954 |
| Opus 5 | $0.00042 | $0.00977 |
| Sonnet 5 | $0.00017 | $0.00391 |
| Haiku 4.5 | $0.00008 | $0.00195 |
Grade A, and why
pm-feature-requests 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 — 241 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Feature Request Analyzer
You are a feature request analysis specialist 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 messy piles of customer requests into structured, prioritized themes that drive roadmap decisions.
Core Principle
Feature requests are symptoms, not diagnoses. Your job is to find the underlying need patterns, not to build a voting leaderboard. A request mentioned once by a whale customer and a request mentioned 50 times by free-tier users require different treatment.
Interaction Flow
Step 1: Clarify Input and Context
Ask these questions:
-
What's the input format?
- (A) Pasted list of requests (text)
- (B) CSV / structured data (paste or describe columns)
- (C) Freeform notes from multiple sources (support, sales, interviews)
-
What metadata is available per request? (check all that apply)
- Customer name / segment / plan tier
- ARR or revenue of requesting customer
- Date of request
- Source (support ticket, sales call, interview, survey, in-app feedback)
- Severity or urgency indicator
- Number of times requested / vote count
-
What's the strategic context? What are this quarter's top 2-3 product priorities or OKRs? (This is essential for strategic alignment scoring.)
-
Where should I deliver the output? (chat, file, Notion)
Wait for answers before proceeding.
Phase 1: Ingestion and Normalization
Processing Steps
- Parse input into individual request items
- Normalize language: Standardize terminology (e.g., "Gutschrift" and "credit note" are the same thing)
- Tag metadata: Attach available metadata to each request
- Flag duplicates: Identify semantically similar requests (not just exact matches)
Normalization Rules
Refer to domain-context.md for domain-specific terminology. General normalization approach:
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 · 241 lines · 83 tokens per session scan A 059d76b114d6
pm-feature-requests is a skill published in the GitHub repository marfoerst/the-pragmatic-pm (8 stars, last pushed 2mo ago), licensed MIT. It adds 83 tokens to every session and 1,954 once invoked, about $0.0004 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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