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Prioritization

Community
nahid-sparktales
prioritization

Choose what not to do and make the tradeoff legible — the constraint being spent, each candidate's cost and value with the evidence behind it, what is reversible, and what would change the answer. Use when a list is longer than the capacity, when asked what to cut or what ships first, or when a decision was made and nobody can reconstruct why. Not for deciding whether a problem is real (product-discovery), not for writing the scope of what wins (prd-and-stories), and not for committing the team on their behalf.

Overview

Publishernahid-sparktales
Repositoryagent-dispatcher
Skill nameprioritization
Stars
58
Forks
5
Bundled files
1
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.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by nahid-sparktales on GitHub. Read the source before you install it.

Installation

Install the 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/nahid-sparktales/agent-dispatcher/tree/main/skills/product/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/nahid-sparktales/agent-dispatcher.git /tmp/agent-dispatcher
mkdir -p .claude/skills
cp -r /tmp/agent-dispatcher/skills/product/prioritization .claude/skills/prioritization
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable 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 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 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.

Prioritization

Ranking is easy and useless. The decision that matters is what gets dropped, and the artifact that matters is a reason someone can disagree with in three months.

When this fires

More candidates than capacity; a request to say what ships first or what to cut; an existing priority order nobody can justify. It does not fire for a list of two items with an obvious dependency between them — say the order in one line and move on.

Procedure

  1. Name the constraint you are allocating. One release, one sprint, one person for two weeks, one review slot. Priority without a stated capacity is just an opinion about importance, and it is why ranked lists get agreed and then ignored.
  2. Make the list comparable. Candidates at wildly different sizes cannot be ranked against each other. Split the boulders into the first shippable slice, and say explicitly that what is being compared is the slice, not the ambition behind it.
  3. Write the cost honestly, in the unit of the constraint. Your estimate, its basis, and its uncertainty as a range where you have one. Include the costs that hide outside the build: migration, support load, the thing that now needs maintaining, the review it will consume.
  4. Write the value claim with its evidence grade. What changes for whom, and whether that is observed (data or traced cases), reported (someone said so), or assumed. Two items claiming equal value where one is observed and one is assumed are not equal, and collapsing that difference is the most common way a bad bet wins.
  5. Check dependency and sequencing before any scoring. What is blocked by what, what unblocks several others, what has an external date attached. Order that is forced is not a priority judgment — settle it first so the real tradeoff is what remains.
  6. Sort by reversibility, not only by size. A cheap one-way door — a public API, a data migration, a pricing change, a URL scheme, anything users will build habits on — deserves more scrutiny than an expensive change you can undo on Friday. Say which candidates are one-way.
  7. Ask what changes if each waits one cycle. Some costs grow (a migration through a growing dataset, an outage risk, a security gap), some shrink, most stay flat. Decay is what separates "later" from "never", and it is usually the only honest argument for doing a low-value item now.
  8. Make the cut, and write the line under it. Say what is in, what is out, and what is out for now with the condition that would bring it back. A list where nothing is out has not prioritized anything.
  9. Record the reasoning in a few lines, without ceremony. What the constraint was, which tradeoff decided it, and what evidence would flip it. Reach for a scoring framework only when many comparable items must be ranked by several people; for one team and a dozen items, a score is decoration that hides its own weights. Numbers invented to look objective are worse than a sentence that can be argued with.
  10. Present it as a recommendation. The people who own the roadmap decide, and commitments to customers, dates, or other teams are theirs to make — offer the cut and the reasoning, and let them take it. Do not announce, message, or record the decision anywhere outward-facing on your own initiative.

Checklist

  • The capacity being allocated is stated
  • Items are comparable in size, or explicitly sliced to be
  • Every item has a cost with its basis, in the constraint's unit
  • Every value claim carries an evidence grade
  • Forced ordering (dependencies, external dates) resolved before judgment calls
  • One-way doors identified as such
  • Decay checked: what gets worse or cheaper by waiting
  • Something is actually out, and "out for now" has a return condition
  • Reasoning written in a few lines; no scoring theatre
  • Delivered as a recommendation, with the decision left where it belongs

Failure handling

  • Two items are genuinely tied. Say so and break it on reversibility or on which one teaches you more. Manufacturing a tenth of a point to separate them is false precision.
  • Someone has already promised an item externally. Then it is a constraint, not a candidate. Move it above the line, say it was a commitment rather than a ranking, and rank what is left.
  • The evidence is thin everywhere. Prioritize the cheapest thing that produces evidence, and say that is what you are optimizing for. Do not dress up an assumption as a value score.
  • The list is all urgent. Ask what breaks, for whom, and when, if each waits a cycle. Urgency that survives that question is real; the rest was volume.
  • You are being asked to rank someone else's domain. Rank the effect on the outcome you can see, name the expertise you lack, and route the call. A confident ranking of work you do not understand is a liability.
  • The decision has already been made and you are being asked for justification. Say what the reasoning would have to be for it to hold, and whether the evidence supports it. Do not reverse-engineer a rationale.

Evidence to report

The constraint; the candidate list with cost, value and evidence grade per item; the forced ordering and why; which items are one-way doors; what was cut and what was deferred with its return condition; and the one or two facts that would change the answer. A ranked list with no capacity, no costs and nothing below the line is a preference, not a prioritization.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Prioritization AI skill do?

Choose what not to do and make the tradeoff legible — the constraint being spent, each candidate's cost and value with the evidence behind it, what is reversible, and what would change the answer. Use when a list is longer than the capacity, when asked what to cut or what ships first, or when a decision was made and nobody can reconstruct why. Not for deciding whether a problem is real (product-discovery), not for writing the scope of what wins (prd-and-stories), and not for committing the team on their behalf.

Why use Prioritization on TypingMind?

Because you install it once and use it with any model. 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 Prioritization in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/nahid-sparktales/agent-dispatcher/tree/main/skills/product/prioritization. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use 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 Prioritization?

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

Is the Prioritization AI skill free?

Yes. It is published on GitHub by nahid-sparktales 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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