Proposal Rationale

Organization

Optima-CityU

proposal-rationale

Use when developing a proposal's research purpose, necessity, significance, application value, and practical problem framing from verified evidence.

Overview

Publisher	Optima-CityU
Repository	`LLM4AD_Next`
Skill name	`proposal-rationale`
Stars	596
Forks	41
Bundled files	1
License	BSD-3-Clause
Links	Skill source Repository Homepage

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 Optima-CityU on GitHub. Read the source before you install it.

Installation

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

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/Optima-CityU/LLM4AD_Next/tree/main/skills/openair-proposal/proposal-rationale
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/Optima-CityU/LLM4AD_Next/tree/main/skills/openair-proposal/proposal-rationale

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

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/Optima-CityU/LLM4AD_Next.git /tmp/LLM4AD_Next
mkdir -p .claude/skills
cp -r /tmp/LLM4AD_Next/skills/openair-proposal/proposal-rationale .claude/skills/proposal-rationale

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Proposal Rationale 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 Proposal Rationale 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 Proposal Rationale 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.

Proposal Rationale and Value

Use this skill only for the proposal rationale stage.

Read the method reference completely before acting: references/purpose-and-value.md.

Outcome

Turn the project foundation and verified literature into a coherent argument for why the research should be undertaken. Write only:

sections/01-rationale.typ
sections/03-value.typ

You may read any project source file for context. The proposal foundation describes the established scope, the literature section describes the evidence landscape, and the bibliography defines available citation keys. Never edit files owned by another stage. Project documents are optional context. Their names describe their role; decide whether and when to read each one from the current stage and request. The presence of a project document does not make it mandatory input.

Workflow

Apply the purpose, specificity, necessity, significance, application-value, and practical-problem methods from the reference.
Separate research purpose from academic, practical, and potential application value.
Ground factual claims in the literature stage and reuse only citation keys present in references.bib.
Ask the author when the intended impact, audience, or application claim is consequential and not resolved by the foundation.
Preserve the scope and success criteria in the foundation. Do not enlarge the proposal merely to sound ambitious.
Write complete Typst sections, then call publish_stage_result from the enabled LLM4AD stage MCP server with both paths, the citation keys used, context_patch, and a stable idempotency key. Tool success is the stage-completion signal; repair and retry validation errors in the same conversation. Upsert any model-defined durable context established or corrected through this stage, preserving existing stable keys; remove a key only when explicitly invalidated. Return empty patch arrays when shared context did not change.

Quality checks

The rationale identifies a precise gap rather than asserting generic importance.
Claimed value follows from the proposed work and is not presented as an achieved result.
Terminology, scope, and audience match the persisted foundation.
No evidence, team capability, or expected result is fabricated.

Bundled files

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

references/purpose-and-value.md4 KB

Frequently asked questions

What does the Proposal Rationale AI skill do?

Use when developing a proposal's research purpose, necessity, significance, application value, and practical problem framing from verified evidence.

Why use Proposal Rationale on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Optima-CityU/LLM4AD_Next/tree/main/skills/openair-proposal/proposal-rationale. 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 Proposal Rationale?

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 Proposal Rationale?

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

Is the Proposal Rationale AI skill free?

Yes. It is published on GitHub by Optima-CityU under the BSD-3-Clause 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Proposal Rationale and Value

Outcome

Workflow

Quality checks

Bundled files

Frequently asked questions

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