Proposal Foundation Feasibility

Organization

Optima-CityU

proposal-foundation-feasibility

Use when documenting a proposal's research foundation, available conditions, team support, feasibility, and risk controls from author-supplied facts.

Overview

Publisher	Optima-CityU
Repository	`LLM4AD_Next`
Skill name	`proposal-foundation-feasibility`
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 Foundation Feasibility 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-foundation-feasibility
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-foundation-feasibility

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-foundation-feasibility .claude/skills/proposal-foundation-feasibility

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

Use it in TypingMind

Enable Proposal Foundation Feasibility 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 Foundation Feasibility 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 Foundation Feasibility 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 Research Foundation and Feasibility

Use this skill only for the proposal foundation and feasibility stage.

Read the method reference completely before acting: references/foundation-and-feasibility.md.

You may read any project source file for context. Write only sections/06-feasibility.typ.

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

Select the foundation, completed methods, author materials, and source-grounded facts needed to assess capability and feasibility.
Document only real preliminary work, team capabilities, facilities, data access, collaborations, and institutional support supplied by the author or trusted source material.
Explain which planned task or project risk each foundation item supports; do not present a biography or equipment inventory as proof by itself.
Assess goal, technical, resource, schedule, and risk feasibility against the completed objectives and methods.
Ask the author for missing consequential facts. Never invent publications, results, team members, facilities, data access, or available resources.
Keep achieved facts distinct from planned work and conditional risk controls.
Write the Typst section, then call publish_stage_result from the enabled LLM4AD stage MCP server with the written path, summary, any 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 durable author-confirmed facts or decisions newly established or corrected in this stage under model-defined stable keys. Do not repeat untouched blocks or force a fixed field structure.

Quality checks

Every feasibility claim has author-provided or source-grounded support.
Every required method has credible expertise, resources, time, and a fallback.
Achieved work and proposed work are unmistakably separated.
Names, results, facilities, and team facts are not inferred or embellished.

Bundled files

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

references/foundation-and-feasibility.md2 KB

Frequently asked questions

What does the Proposal Foundation Feasibility AI skill do?

Use when documenting a proposal's research foundation, available conditions, team support, feasibility, and risk controls from author-supplied facts.

Why use Proposal Foundation Feasibility on TypingMind?

Because you install it once and use it with any model. Proposal Foundation Feasibility 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 Foundation Feasibility 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-foundation-feasibility. 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 Foundation Feasibility?

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 Foundation Feasibility?

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

Is the Proposal Foundation Feasibility 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 Research Foundation and Feasibility

Workflow

Quality checks

Bundled files

Frequently asked questions

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