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Idea Validator

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luongnv89
idea-validator

Validate app/startup ideas with market, feasibility, commercial, and open-source competitor analysis. Use when asked to evaluate, validate, or score a product idea. Don't use for PRDs, go-to-market plans, or investor decks.

Overview

Publisherluongnv89
Repositoryskills
Skill nameidea-validator
Stars
124
Forks
18
Bundled files
2
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.

  • 2 bundled files

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

  • Open source

    Published by luongnv89 on GitHub. Read the source before you install it.

Installation

Install the 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/luongnv89/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/idea-validator .claude/skills/idea-validator
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable 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 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 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.

Idea Validator

Critically evaluate ideas with honest feedback on market viability, technical feasibility, and actionable improvements.

When to Use

Trigger this skill when the user asks to:

  • Evaluate, validate, or score an app idea or startup concept
  • Get honest feedback on whether an idea is worth building
  • Research what competitors already exist in a space, including commercial tools/services and open-source alternatives
  • Turn a vague concept into a structured validation report

Instructions

Follow the 5-phase pipeline in order: Clarify → Technical Context → Competitive Landscape Research → Critical Evaluation → Improvements. Do not skip phases or reorder them. One conditional branch applies on top of the pipeline: if the working directory is the root of an ideas repo, also do the README Maintenance step below after each file update — see that section for the detection rule.

Repo Sync Before Edits (mandatory)

Before creating/updating/deleting files in an existing repository, sync the current branch with remote:

bash
branch="$(git rev-parse --abbrev-ref HEAD)"
git fetch origin
git pull --rebase origin "$branch"

If the working tree is not clean, stash first, sync, then restore:

bash
git stash push -u -m "pre-sync"
branch="$(git rev-parse --abbrev-ref HEAD)"
git fetch origin && git pull --rebase origin "$branch"
git stash pop

If origin is missing, pull is unavailable, or rebase/stash conflicts occur, stop and ask the user before continuing.

Setup

  1. Resolve storage location (tool-agnostic, ask once per environment)

    • Prefer env var IDEAS_ROOT if set.
    • Else use shared marker file: ~/.config/ideas-root.txt.
    • Backward compatibility: if shared marker is missing but legacy ~/.openclaw/ideas-root.txt exists, reuse its value and write it into ~/.config/ideas-root.txt.
    • If no marker exists (new environment), ask user once where to store generated docs.
    • Suggested default: ~/workspace/ideas.
    • Save chosen root to ~/.config/ideas-root.txt.
    • Only ask again if the user explicitly asks to change location.
  2. Create project folder under resolved root: YYYY_MM_DD_<short_snake_case_name>/

  3. Create idea.md: Document the idea and clarifications

  4. Create validate.md: Document evaluation and recommendations

  5. Echo the absolute project folder path in your response so downstream skills can auto-pick it.

If no idea provided in $ARGUMENTS, ask user to describe their concept.

Phase 1: Clarify the Idea

Ask user (via AskUserQuestion):

  • What problem does this solve? Who has this pain?
  • Who is your target user? Be specific.
  • What makes this different from existing solutions?
  • What does success look like in 6-12 months?

Update idea.md with responses.

Phase 2: Gather Technical Context

Ask user:

  • Preferred tech stack?
  • Timeline and team size?
  • Budget situation (bootstrapped/funded/side project)?
  • Existing assets (code, designs, research)?

Update idea.md technical section.

Phase 3: Competitive Landscape Research

Before evaluating the idea, perform live web research to find what already exists in the space. Do not rely on memory or training data for market, pricing, traction, competitor, or open-source claims — web search is mandatory so the report reflects the most up-to-date information.

Use the available web search tool (WebSearch, web_search, or equivalent) to run at least 4-6 varied queries covering:

Commercial tools/services — SaaS products, mobile apps, enterprise platforms, paid APIs, agencies, marketplaces, and other commercial offerings solving the same or adjacent problem. Search the core problem statement plus keywords like "app", "tool", "platform", "SaaS", "startup", "pricing", "alternative", and audience-specific terms.

Open-source solutions — GitHub/GitLab repositories, self-hosted tools, packages, frameworks, templates, and OSS alternatives that solve the same problem or provide a strong foundation. Search with terms like "open source", "GitHub", "self-hosted", "OSS", "alternative", "library", and relevant package registry names. If no credible OSS option is found, document the queries tried and state that no maintained open-source baseline was found.

Adjacent solutions — products or projects that solve a related problem or serve the same audience differently. These reveal how users currently cope without the proposed solution.

Failed attempts — startups, products, or OSS projects that tried something similar and stalled, shut down, or were abandoned. Search for "[concept] startup failed", "[concept] post-mortem", "[concept] abandoned GitHub", or check product directories. Understanding why predecessors failed is often more valuable than knowing who succeeded.

For each competitor or OSS project found, capture:

  • Name and URL
  • Type (commercial, open-source, hybrid, adjacent, failed/abandoned)
  • What they do (one sentence)
  • Pricing or license (free, freemium, paid, enterprise, OSS license if available)
  • Traction or health (reviews, ratings, users, funding, GitHub stars/forks, recent commits, open issues, community activity)
  • Key weakness or gap the user's idea could exploit
  • Reuse/build-on potential for open-source options (fork, plugin, library dependency, contribution path, or not suitable)

Aim for 3-8 total competitors and at least one commercial and one open-source search path. If fewer than 3 credible results are found, that's a signal — either the market is niche, the terms need refining, or the idea may be framed in unfamiliar language.

When an open-source solution already solves a meaningful part of the idea, evaluate more carefully before recommending a greenfield build. Compare license fit, maintenance health, architecture, extensibility, deployment burden, community, and whether the user should build on it, fork it, contribute to it, or differentiate sharply instead of redoing it.

Update validate.md with a ## Competitive Landscape section containing:

  1. A summary table of commercial and open-source competitors found
  2. An Open-source Alternatives & Reuse Potential analysis
  3. A "white space" analysis — what's missing in the current market
  4. Honest assessment: is the user's differentiation real or imagined given what exists?
  5. A build-vs-base recommendation when OSS foundations exist

If web search is unavailable or blocked, stop and ask the user before proceeding. Do not silently replace live research with general knowledge.

Phase 4: Critical Evaluation

Evaluate honestly and update validate.md:

Market Analysis:

  • Similar commercial products and open-source solutions
  • Market size and competition
  • Unique differentiation

Demand Assessment:

  • Evidence people will pay
  • Problem urgency level

Feasibility:

  • Can this ship in 2-4 weeks MVP?
  • Minimum viable features
  • Complex dependencies?
  • Could an existing OSS project be reused, forked, extended, or used as a reference instead of starting from scratch?

Monetization:

  • Clear revenue path?
  • Willingness to pay?

Technical Risk:

  • Buildable with stated constraints?
  • Key technical risks?
  • License, maintenance, and dependency risks if building on open source?

Duplication / Reuse Risk:

  • Is the idea mostly a reimplementation of an existing commercial or OSS solution?
  • Is there a credible build-on, plugin, fork, or contribution path that would reduce risk?

Verdict: Build it / Maybe / Skip it

Ratings (1-10):

  • Creativity
  • Feasibility
  • Market Impact
  • Technical Execution

Phase 5: Improvements

Update validate.md with:

  • How to Strengthen: Specific, actionable improvements
  • Enhanced Version: Reworked, optimized concept
  • Implementation Roadmap: Phased approach (if applicable)

Expected Output

After all phases complete, the output includes:

## Quick Verdict
**Build it**

## Ratings
| Dimension         | Score |
|-------------------|-------|
| Creativity        | 7/10  |
| Feasibility       | 8/10  |
| Market Impact     | 6/10  |
| Technical Execution | 8/10 |

## Top Concerns
1. Three direct competitors already exist with significant traction
2. Monetization path unclear — target users expect free tools
3. MVP scope likely exceeds 2-4 week estimate

Edge Cases

  • No clear target user: If the idea is too broad (e.g., "an app for everyone"), push back in Phase 1 — ask the user to name one specific person who has this pain today. Do not proceed to evaluation without a defined user segment.
  • Duplicate idea already exists: If Phase 3 research finds a near-identical product, surface it immediately with evidence (URL, feature comparison) and ask whether the user still wants to proceed. Evaluation continues only if the user identifies a genuine differentiator.
  • Open-source solution already exists: If Phase 3 finds a maintained OSS project that covers much of the idea, treat "build from scratch" as a higher-risk recommendation. Analyze whether to build on, fork, contribute to, or differentiate from the project before giving a Build it verdict.
  • Technical feasibility unclear: If the idea requires unproven technology, undisclosed APIs, or capabilities the stated team cannot build, flag it as a hard blocker in Phase 4 and lower the Feasibility score accordingly. Do not give a Build it verdict when fundamental technical risk is unresolved.

Acceptance Criteria

  • All 5 phases are completed in order (Clarify → Technical Context → Competitive Landscape → Evaluation → Improvements)
  • Competitive landscape research is performed via live web search with at least 4 varied queries
  • Commercial tools/services and open-source solutions are both checked; if no credible OSS option is found, the attempted OSS queries are documented
  • Competitors table and Open-source Alternatives & Reuse Potential analysis are populated in validate.md
  • A clear verdict (Build it / Maybe / Skip it) is given with a supporting rationale
  • All four ratings (Creativity, Feasibility, Market Impact, Technical Execution) are provided as scores out of 10
  • idea.md and validate.md are committed and pushed to the remote repository
  • GitHub links to both files are reported in the completion message

Step Completion Reports

After completing each major step, output a status report in this format:

◆ [Step Name] ([step N of M] — [context])
··································································
  [Check 1]:          √ pass
  [Check 2]:          √ pass (note if relevant)
  [Check 3]:          × fail — [reason]
  [Check 4]:          √ pass
  [Criteria]:         √ N/M met
  ____________________________
  Result:             PASS | FAIL | PARTIAL

Adapt the check names to match what the step actually validates. Use for pass, × for fail, and to add brief context. The "Criteria" line summarizes how many acceptance criteria were met. The "Result" line gives the overall verdict.

Per-phase check blocks (Setup, Phase 1-5) live in references/step-completion-reports.md — read the block matching the phase you just completed.

Tone

  • Brutally honest: Don't sugarcoat fatal flaws
  • Constructive: Every criticism includes a suggestion
  • Specific: Concrete examples, not vague feedback
  • Balanced: Acknowledge strengths alongside weaknesses

README Maintenance (when running inside ideas repo)

If the current working directory looks like the root of an ideas repo (contains README.md + multiple YYYY_MM_DD_* idea folders):

  • After creating/updating idea.md + validate.md, update README.md by inserting/updating an ## Ideas index table with:
  • link to each idea.md
  • PRD/tasks status
  • verdict link to validate.md

Commit and push (mandatory)

After file updates are complete:

  • Commit immediately with a clear message.
  • Push immediately to remote.
  • If push is rejected: git fetch origin && git rebase origin/main && git push.

Do not ask for additional push permission once this skill is invoked.

Reporting with GitHub links (mandatory)

When reporting completion, include:

  • GitHub link to idea.md
  • GitHub link to validate.md
  • GitHub link to README.md when it was updated
  • Commit hash

Link format (derive <owner>/<repo> from git remote get-url origin):

  • https://github.com/<owner>/<repo>/blob/main/<relative-path>

Output Summary

After all phases:

  1. Confirm folder/files created
  2. State verdict and key ratings
  3. Top 3 strengths, top 3 concerns
  4. Single most important next step

File Templates

The canonical idea.md and validate.md header structure lives in references/file-templates.md. Read it when creating either file (Setup step 2/3) or updating a section named in Phases 1-5 above — that file owns header names and order; phase instructions above own what content goes in them.

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 Idea Validator AI skill do?

Validate app/startup ideas with market, feasibility, commercial, and open-source competitor analysis. Use when asked to evaluate, validate, or score a product idea. Don't use for PRDs, go-to-market plans, or investor decks.

Why use Idea Validator on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/luongnv89/skills/tree/main/skills/idea-validator. 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 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 Idea Validator?

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

Is the Idea Validator AI skill free?

Yes. It is published on GitHub by luongnv89 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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