Share Case logo

Share Case

OrganizationPopular
NeuroAIHub
share-case

One-command community case sharing — capture research context from your session and submit to GitHub Discussions

Overview

PublisherNeuroAIHub
RepositoryBrainPilot
Skill nameshare-case
Stars
1.1K
Forks
74
Bundled files
Instructions only
LicenseAGPL-3.0
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 NeuroAIHub on GitHub. Read the source before you install it.

Installation

Install the Share Case 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/NeuroAIHub/BrainPilot/tree/main/packages/skills/skills/01_Meta-Skills/share-case
  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/NeuroAIHub/BrainPilot.git /tmp/BrainPilot
mkdir -p .claude/skills
cp -r /tmp/BrainPilot/packages/skills/skills/01_Meta-Skills/share-case .claude/skills/share-case
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Share Case 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 Share Case 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 Share Case 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.

Share Case

Purpose

This meta-skill captures a researcher's experience using any skill in this repository and submits it as a structured case to GitHub Discussions. It handles all formatting and submission so users never need to leave their terminal or know Git.

When to Use This Skill

Activate when the user:

  • Says "share this case", "share my experience", "分享这个案例"
  • Wants to document how they used a skill in their research
  • Asks how to contribute a usage example

Research Planning Protocol

Before starting the case sharing process, you MUST:

  1. Identify the skill used — Which skill(s) from this repository were used in the current session?
  2. Clarify sharing scope — What aspects of the experience does the user want to share?
  3. Declare what will be extracted — List the specific conversation elements that will be included
  4. Note privacy considerations — Are there dataset names, lab identifiers, or participant details that should be anonymized?
  5. Present the extraction plan to the user and WAIT for confirmation before proceeding.

For detailed methodology guidance, see skills/research-literacy/SKILL.md.

⚠️ Verification Notice

This skill was generated by AI from academic literature. All parameters, thresholds, and citations require independent verification before use in research. If you find errors, please open an issue.


Prerequisites

This skill supports two submission methods:

  1. Direct submission via gh CLI (Recommended)

    • Requires gh CLI installed and authenticated
    • Run gh auth status to check
    • Install from https://cli.github.com/ and run gh auth login
  2. Manual submission via web browser

    • No gh CLI required
    • Opens GitHub Discussions page in browser
    • User copies and pastes the generated case

The skill will check for gh availability and let the user choose their preferred method.

GitHub Discussions must be enabled on the repository. The skill submits to the "Show & Tell" category.


Interactive Flow

Step 1 — Quick Survey

Present these questions using multiple-choice format:

Q1: Which skill did you use?

  • Auto-detect from the current session context (list skills that were activated)
  • Let the user confirm or select manually from the full skill list

Q2: What was your research scenario?

  • Formal experiment
  • Coursework or teaching
  • Method exploration
  • Paper reproduction
  • Other (free text)

Q3: How helpful was the skill? (1-5)

  • 1 = Not helpful
  • 2 = Slightly helpful
  • 3 = Moderately helpful
  • 4 = Very helpful
  • 5 = Extremely helpful

Q4: What was most valuable? (select all that apply)

  • Correct methodology guidance
  • Validated parameters and thresholds
  • Pitfall warnings
  • Complete pipeline coverage
  • Literature references
  • Other (free text)

Step 2 — Context Extraction

Extract the following from the current conversation:

  1. Research context — The user's original research question and experimental setup
  2. Key recommendations — The main suggestions the skill provided (parameters, methods, warnings)
  3. Follow-up adjustments — Any modifications made during the conversation
  4. Outcome summary — What the user ultimately decided to do

Format each as a concise paragraph. Do NOT include raw conversation transcripts — synthesize into readable summaries.

Step 3 — User Review and Submission Choice

Display the complete case preview using the format below.

First, check gh CLI availability:

bash
gh auth status 2>&1

Then present submission options using AskUserQuestion:

If gh is available:

  • Submit via gh CLI (Recommended) — Direct submission to GitHub Discussions
  • Open in browser — Manual submission via web interface
  • Delete sections — Remove specific paragraphs before submitting
  • Anonymize — Replace specific names (datasets, labs, participants)
  • Abort — Cancel without submitting

If gh is NOT available:

  • Open in browser — Manual submission via web interface
  • Delete sections — Remove specific paragraphs before submitting
  • Anonymize — Replace specific names (datasets, labs, participants)
  • Abort — Cancel without submitting

You MUST wait for explicit user confirmation before proceeding to Step 4.

Step 4 — Submit to GitHub

Based on the user's choice in Step 3:

Option A: Submit via gh CLI

Create a GitHub Discussion in the "Show & Tell" category.

First, get repository and category IDs:

bash
gh api graphql -f query='
{
  repository(owner: "HaoxuanLiTHUAI", name: "awesome_cognitive_and_neuroscience_skills") {
    id
    discussionCategories(first: 10) {
      nodes {
        id
        name
      }
    }
  }
}'

Then create the discussion:

bash
gh api graphql -f query='
mutation {
  createDiscussion(input: {
    repositoryId: "REPO_ID",
    categoryId: "SHOW_AND_TELL_CATEGORY_ID",
    title: "Community Case: SKILL_NAME",
    body: "CASE_BODY_HERE"
  }) {
    discussion {
      url
    }
  }
}'

On success, display the Discussion URL:

✅ Case shared successfully!
🔗 Discussion URL: [URL]

Thank you for contributing to the community!

On failure, fall back to Option B.

Option B: Open in Browser
  1. Save the case locally to the current directory as case-skill-name-YYYYMMDD.md

  2. Open the GitHub Discussions page:

bash
xdg-open "https://github.com/HaoxuanLiTHUAI/awesome_cognitive_and_neuroscience_skills/discussions/new?category=show-and-tell" 2>/dev/null || \
open "https://github.com/HaoxuanLiTHUAI/awesome_cognitive_and_neuroscience_skills/discussions/new?category=show-and-tell" 2>/dev/null || \
echo "Please open: https://github.com/HaoxuanLiTHUAI/awesome_cognitive_and_neuroscience_skills/discussions/new?category=show-and-tell"
  1. Inform the user:
✅ Case saved to: case-skill-name-YYYYMMDD.md

🌐 Opening GitHub Discussions in your browser...

📋 Next steps:
1. Title: "Community Case: [Skill Name]"
2. Copy the content from case-skill-name-YYYYMMDD.md
3. Paste it into the discussion body
4. Click "Start discussion"

Thank you for contributing to the community!

Case Format Template

The submitted Discussion body uses this format:

markdown
## Community Case: [skill-name]

### Quick Info
- **Skill used**: `[skill-name]`
- **Scenario**: [selected option from Q2]
- **Rating**: [stars from Q3, e.g., ⭐⭐⭐⭐]
- **Most valuable**: [selected options from Q4]

### Research Context
> [User's research question and experimental setup — synthesized from conversation]

### What the Skill Suggested
- [Key recommendation 1]
- [Key recommendation 2]
- [Key recommendation 3]

### What I Actually Did & Result
> [User's experience applying the recommendations and the outcome]

### User's Tips
> [Optional: Additional insights the user wants to share with the community]

---
*Submitted via the `share-case` meta-skill.*

Privacy Principles

  • Nothing is submitted without explicit user confirmation after full preview
  • Users can remove any section or paragraph before submission
  • Users can anonymize dataset names, lab names, and participant identifiers
  • Users can choose to submit anonymously (no GitHub username attribution in the case body)
  • The skill never auto-submits — the user must explicitly approve

Frequently asked questions

What does the Share Case AI skill do?

One-command community case sharing — capture research context from your session and submit to GitHub Discussions

Why use Share Case on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/NeuroAIHub/BrainPilot/tree/main/packages/skills/skills/01_Meta-Skills/share-case. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Share Case?

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 Share Case?

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

Is the Share Case AI skill free?

Yes. It is published on GitHub by NeuroAIHub under the AGPL-3.0 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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