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 pmprompt/claude-plugin-product-management --skill feature-prioritization-assistantgit clone --depth 1 https://github.com/pmprompt/claude-plugin-product-managementWrote 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/pmprompt/claude-plugin-product-management/feature-prioritization-assistant)<a href="https://agentmods.dev/skills/pmprompt/claude-plugin-product-management/feature-prioritization-assistant"><img src="https://agentmods.dev/badge/skills/pmprompt/claude-plugin-product-management/feature-prioritization-assistant/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/pmprompt/claude-plugin-product-management/feature-prioritization-assistant"><img src="https://agentmods.dev/badge/skills/pmprompt/claude-plugin-product-management/feature-prioritization-assistant.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk 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 | $0.00033 | $0.00488 |
| Opus 5 | $0.00016 | $0.00244 |
| Sonnet 5 | $0.00007 | $0.00098 |
| Haiku 4.5 | $0.00003 | $0.00049 |
Grade A, and why
feature-prioritization-assistant 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- feature-prioritization-assistant — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 54 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Domain Context
This skill implements a proven product management framework. The approach combines best practices from industry leaders and is designed for practical application in day-to-day PM work.
Input Requirements
- Context about your product, feature, or problem
- Relevant data, research, or constraints (recommended but optional)
- Clear articulation of what you're trying to achieve
Feature Prioritization Assistant
When to Use
- Building your product roadmap
- Need to choose between multiple feature ideas
- Stakeholders are debating which features to build first
- Want to make data-driven prioritization decisions
- Need to justify prioritization decisions to leadership
What This Skill Does
Helps you systematically evaluate and prioritize features using the RICE framework (Reach, Impact, Confidence, Effort), providing scores and recommendations.
Instructions
Help me prioritize these features using the RICE framework. For each feature, help me estimate:
- Reach: How many users will this impact per month?
- Impact: How much will this impact each user? (Scale: 0.25=minimal, 0.5=low, 1=medium, 2=high, 3=massive)
- Confidence: How confident are we in our estimates? (Scale: 0-100%)
- Effort: How many person-months will this take to build?
Then calculate the RICE score: (Reach × Impact × Confidence) / Effort
Features to evaluate: [List your features with any context you have]
Best Practices
- Gather data on current user behavior before estimating Reach
- Base Impact on user research and pain point severity
- Be honest about Confidence levels - lower confidence for assumptions
- Include design, development, and testing time in Effort estimates
- Revisit estimates after initial discovery work
- Consider dependencies between features
Example
Input: 5 features (notifications, dark mode, API access, mobile app, analytics dashboard) Output: RICE scores calculated for each, ranked list with reasoning, recommendations on which to prioritize, and suggestions for validating assumptions on low-c...
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 · 54 lines · 33 tokens per session scan A fe6577f032d9
feature-prioritization-assistant is a skill published in the GitHub repository pmprompt/claude-plugin-product-management (49 stars, last pushed 6mo ago), licensed MIT. It adds 33 tokens to every session and 488 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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