Project Idea Validator logo

Project Idea Validator

OrganizationPopular
tinyfish-io
project-idea-validator

Researches any project idea against live data from GitHub and Dev.to to surface what already exists, how mature the space is, and where the real opportunity lives. Use when a developer describes something they want to build and wants to know if it's been done before. Triggers on phrases like "validate my idea", "has this been built", "is this already a thing", "what exists for X", "should I build this", "is this idea original", "check if my project exists", "what are the alternatives to what I want to build", "is the market saturated for X", or any request to research the competitive landscape before starting a project.

Overview

Publishertinyfish-io
Repositorytinyfish-cookbook
Skill nameproject-idea-validator
Stars
2.2K
Forks
333
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 tinyfish-io on GitHub. Read the source before you install it.

Installation

Install the Project Idea Validator 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.
  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/tinyfish-io/tinyfish-cookbook.git /tmp/tinyfish-cookbook
mkdir -p .claude/skills
cp -r /tmp/tinyfish-cookbook/skills/project-idea-validator-skill .claude/skills/project-idea-validator
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Project Idea Validator 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 Project Idea Validator 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 Project Idea Validator 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.

Project Idea Validator — Discover What Already Exists Before You Build

You have access to the TinyFish CLI (tinyfish), a tool that runs browser automations from the terminal using natural language goals. This skill uses it to search GitHub and Dev.to in parallel, then synthesizes results into a gap analysis report.

Pre-flight Check (REQUIRED)

Before making any TinyFish call, always run BOTH checks:

1. CLI installed?

PowerShell:

powershell
Get-Command tinyfish; tinyfish --version

bash/zsh:

bash
which tinyfish && tinyfish --version || echo "TINYFISH_CLI_NOT_INSTALLED"

If not installed, stop and tell the user:

Install the TinyFish CLI: npm install -g @tiny-fish/cli

2. Authenticated?

powershell
tinyfish auth status

If not authenticated, stop and tell the user:

You need a TinyFish API key. Get one at: https://agent.tinyfish.ai/api-keys

Then authenticate:

Option 1 — CLI login (interactive):

tinyfish auth login

Option 2 — PowerShell (current session only):

powershell
$env:TINYFISH_API_KEY="your_api_key_here"

Option 3 — PowerShell (persist across sessions):

powershell
[System.Environment]::SetEnvironmentVariable("TINYFISH_API_KEY", "your_api_key_here", "User")

Then close and reopen PowerShell for it to take effect.

Option 4 — bash/zsh (Mac/Linux):

bash
export TINYFISH_API_KEY="your_api_key_here"

Option 5 — Claude Code settings: Add to ~/.claude/settings.local.json:

json
{
  "env": {
    "TINYFISH_API_KEY": "your_api_key_here"
  }
}

Do NOT proceed until both checks pass.


What This Skill Does

Given a project idea (e.g. "a CLI tool that converts Figma designs to Tailwind components"), this skill:

  1. Searches GitHub for existing repos with similar purpose, tech stack, or keywords
  2. Searches Dev.to for articles, tutorials, or project showcases covering the same problem

It then synthesizes findings into a structured gap analysis: what exists, how mature it is, and where the opportunity still lives.


Core Command

bash
tinyfish agent run --url <url> "<goal>"

Flags

FlagPurpose
--url <url>Target website URL
--syncWait for full result (no streaming)
--asyncSubmit and return immediately
--prettyHuman-readable formatted output

Step-by-Step Workflow

Step 1 — Search GitHub

Search for existing repositories matching the idea. Run with --sync since you need the full list before synthesizing.

bash
tinyfish agent run --sync --url "https://github.com/search?q=<keywords>&type=repositories&s=stars&o=desc" \
  "Extract the top 10 search results as JSON: [{\"name\": str, \"owner\": str, \"description\": str, \"stars\": str, \"url\": str, \"last_updated\": str}]"

Example for a Figma-to-Tailwind CLI idea:

bash
tinyfish agent run --sync \
  --url "https://github.com/search?q=figma+tailwind+cli&type=repositories&s=stars&o=desc" \
  "Extract the top 10 repositories as JSON: [{\"name\": str, \"owner\": str, \"description\": str, \"stars\": str, \"url\": str, \"last_updated\": str}]"

Step 2 — Search Dev.to

Search for articles and project posts covering the same problem space. Run in parallel with Step 1 results processing.

bash
tinyfish agent run --sync --url "https://dev.to/search?q=<keywords>" \
  "Extract the top 10 articles as JSON: [{\"title\": str, \"author\": str, \"tags\": [str], \"published_at\": str, \"url\": str, \"reactions\": str}]"

Example:

bash
tinyfish agent run --sync \
  --url "https://dev.to/search?q=figma+tailwind+component+generator" \
  "Extract the top 10 articles as JSON: [{\"title\": str, \"author\": str, \"tags\": [str], \"published_at\": str, \"url\": str, \"reactions\": str}]"

Parallel Execution

Steps 1 and 2 are independent — run them at the same time. Do NOT wait for GitHub before starting Dev.to.

Good — Parallel calls:

bash
# Fire both simultaneously
tinyfish agent run --sync --url "https://github.com/search?q=<keywords>&type=repositories&s=stars&o=desc" \
  "Extract top 10 repositories as JSON: [{\"name\": str, \"owner\": str, \"description\": str, \"stars\": str, \"url\": str, \"last_updated\": str}]" &

tinyfish agent run --sync --url "https://dev.to/search?q=<keywords>" \
  "Extract top 10 articles as JSON: [{\"title\": str, \"author\": str, \"url\": str, \"reactions\": str}]" &

wait

Bad — Sequential calls:

bash
# Don't do this — wastes time and gives the same results
tinyfish agent run --url "https://github.com/..." "...also search Dev.to..."

Each source is its own call. Always.


Step 3 — Synthesize Into a Gap Analysis

Once both sources return results, synthesize findings into this structure:

## Project Idea Validation: <idea title>

### What Already Exists
- <project/article> — <what it does, stars/reactions, last active>
- ...

### Maturity Assessment
- GitHub: <active / abandoned / fragmented>
- Dev.to coverage: <heavy / moderate / sparse>

### Gaps & Opportunities
- <specific gap #1>
- <specific gap #2>
- ...

### Verdict
<1–2 sentences: is the space crowded, open, or ripe for a better take?>

Use the raw JSON from both sources as input. Do not hallucinate repo names, star counts, or article titles — only use what TinyFish returned.


Keyword Strategy

The quality of results depends heavily on your search terms. Before running, derive 2–3 keyword variants from the idea:

IdeaPrimary keywordsVariant keywords
Figma-to-Tailwind CLIfigma tailwind clifigma css export, design token tailwind
AI code review botai code review githubllm pull request, automated code feedback
Markdown-to-Notion syncmarkdown notion syncnotion import cli, notion api markdown

Run separate parallel calls for each variant if the first pass returns sparse results.


Managing Runs

bash
# List recent runs
tinyfish agent run list

# Get a specific run by ID
tinyfish agent run get <run_id>

# Cancel a running automation
tinyfish agent run cancel <run_id>

Output

The CLI streams data: {...} SSE lines by default. The final result is the event where type == "COMPLETE" and status == "COMPLETED" — the extracted data is in the resultJson field. Read the raw output directly; no script-side parsing is needed.

Frequently asked questions

What does the Project Idea Validator AI skill do?

Researches any project idea against live data from GitHub and Dev.to to surface what already exists, how mature the space is, and where the real opportunity lives. Use when a developer describes something they want to build and wants to know if it's been done before. Triggers on phrases like "validate my idea", "has this been built", "is this already a thing", "what exists for X", "should I build this", "is this idea original", "check if my project exists", "what are the alternatives to what I want to build", "is the market saturated for X", or any request to research the competitive landsc...

Why use Project Idea Validator on TypingMind?

Because you install it once and use it with any model. Project Idea Validator 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 Project Idea Validator in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/tinyfish-io/tinyfish-cookbook/tree/main/skills/project-idea-validator-skill. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Project Idea Validator?

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 Project Idea Validator?

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

Is the Project Idea Validator AI skill free?

Yes. It is published on GitHub by tinyfish-io 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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