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Feature Prioritization

Community
assimovt
feature-prioritization

Prioritize features and backlog items using RICE scoring and Linear's enablers vs blockers lens. Use when asked to rank features, prioritize a backlog, decide what to build next, or evaluate feature requests against each other.

Overview

Publisherassimovt
Repositoryproductskills
Skill namefeature-prioritization
Stars
71
Forks
6
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by assimovt on GitHub. Read the source before you install it.

Installation

Install the Feature Prioritization AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/assimovt/productskills/tree/main/skills/feature-prioritization
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/assimovt/productskills.git /tmp/productskills
mkdir -p .claude/skills
cp -r /tmp/productskills/skills/feature-prioritization .claude/skills/feature-prioritization
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Feature Prioritization in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Feature Prioritization on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Feature Prioritization is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Prioritize with math, not opinions. RICE scoring forces explicit tradeoffs. The enabler/blocker lens from Linear ensures you're not just building fun things while adoption barriers remain.

RICE Scoring

Score every candidate feature on four dimensions:

  • Reach: How many users/accounts will this affect in a set time period? Use real numbers from analytics, not gut feel. "500 users/quarter" not "a lot."
  • Impact: How much will this move the target metric per user? Score 0.25 (minimal), 0.5 (low), 1 (medium), 2 (high), 3 (massive). Be honest — most features are a 1.
  • Confidence: How sure are you about Reach and Impact? 100% = hard data. 80% = strong evidence. 50% = gut feel. NEVER score 100% without quantitative data.
  • Effort: Person-weeks of work. Include design, engineering, QA, and any cross-team coordination. Round up.

RICE = (Reach x Impact x Confidence) / Effort

Example: SSO — Reach: 500 users/qtr, Impact: 2 (high — unlocks enterprise deals), Confidence: 80%, Effort: 4 person-weeks. RICE = (500 x 2 x 0.8) / 4 = 200. Tag: Blocker.

Rank by score. The math won't be perfect, but it forces you to justify each dimension.

Enablers vs Blockers (Linear)

After RICE scoring, classify each feature:

  • Blocker: Removes a barrier to adoption or retention. Users are churning, stuck, or can't even start because this is missing. Examples: missing SSO for enterprise deals, broken mobile experience, no data export.
  • Enabler: Delights existing users or deepens engagement. Users are already successful but this makes them more so. Examples: keyboard shortcuts, advanced filters, integrations.

Rule: Prioritize blockers over enablers when growing. Removing friction > adding delight when you're trying to grow. Flip this when retention is strong but engagement is flat.

Running a Prioritization Session

  1. List all candidates with a one-sentence description
  2. Score each on R, I, C, E independently — don't anchor on each other
  3. Calculate RICE scores and rank
  4. Tag each as Blocker or Enabler
  5. Check: are any Blockers ranked below Enablers? Justify or re-rank.
  6. Top 3-5 items = your next cycle

Guidelines

  • ALWAYS show the math. A prioritization without visible scores is just an opinion list.
  • NEVER let Confidence = 100% without hard data (analytics, A/B test results, or customer commit).
  • ALWAYS separate enablers from blockers before finalizing the rank.
  • NEVER prioritize more than one cycle at a time. The world changes. Re-score next cycle.
  • CRITICAL: If two items are within 20% RICE score of each other, they're effectively tied. Use the blocker/enabler lens to break ties, or pick the one with higher Confidence.

Built on Intercom's RICE framework and the Linear Method. Skills from productskills.

Frequently asked questions

What does the Feature Prioritization AI skill do?

Prioritize features and backlog items using RICE scoring and Linear's enablers vs blockers lens. Use when asked to rank features, prioritize a backlog, decide what to build next, or evaluate feature requests against each other.

Why use Feature Prioritization on TypingMind?

Because you install it once and use it with any model. Feature Prioritization is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Feature Prioritization in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/assimovt/productskills/tree/main/skills/feature-prioritization. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Feature Prioritization?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Feature Prioritization?

As many as you like. As long as a model supports skills, you can use Feature Prioritization with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Feature Prioritization AI skill free?

Yes. It is published on GitHub by assimovt under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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