Create a Fresh App (Research → Plan → Approve)
SkillDocs & knowledgeResearch and plan a brand-new app before scaffolding it on the KMP Starter Template. Gather project info, research the market (stores, Reddit, ASO tools), finalize features + value proposition, get user approval, and persist everything to memory.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Create a Fresh App (Research → Plan → Approve) skill
What this skill tells your AI
The instructions your AI receives, as published by devatrii/kmp-starter-template in .agents/skills/kmp-starter/create-fresh-app/SKILL.md and read by ahel’s review.
Use when the user wants to create a brand-new app (or just research/validate an app idea). Do the thinking and market research before scaffolding with the CLI or writing code.
Do not jump straight into starter create or feature building. Research first, get approval, then proceed.
When to use
- User says "create an app for X", "I want to build a … app", "make me a …".
- User asks to research/validate an app idea or a niche.
- User asks "is this app idea good?" / "what features should my app have?".
Phase 0 — Collect project information
Build this profile. If the user already gave some of it, fill it in; ask for the rest.
## Project Information
**App Name:** {{App Name}}
**Short Description:** {{Short Description}}
**Primary Feature:** {{Primary Feature}}
**Sub Features:** {{Sub Features}}
**Detailed Description:** {{Detailed Description}}
**Target Audience:** {{Target Audience}}
Ask the user for anything missing (name, what it does, core feature, audience). Keep it lightweight — don't interrogate; a rough one-liner is enough to start.
Phase 1 — Research (if info is thin)
If the idea is vague or you need to validate direction, research before proposing features. Use the tools available:
| Source | Tool | What it gives you |
|---|---|---|
| Web search | WebSearch / WebFetch | Market size, trends, competitors, user complaints |
| Play Store / App Store | browser tool, WebFetch store pages | Competitor features, reviews, ratings, screenshots |
| Reddit / forums | WebSearch / WebFetch | Real user pain points, complaints, feature requests |
| ASO / keyword research | ASO MCP tools (inspect_keyword, run_niche_analysis, run_autocomplete, top_charts, list_competitors) | Search demand, keyword difficulty, niche opportunity, competitor rankings |
| Deep research | research MCP tools (e.g. Gemini deep research) | Structured synthesis |
Always research competitors and user pain points — those drive the value proposition more than the raw idea.
ASO tools (if available)
If the user has an ASO provider (e.g. applyra) connected, use it:
run_niche_analysis(topic=..., store, country, lang)— discover keyword clusters + opportunity scores + an app concept suggestion.inspect_keyword(keyword, store, country, lang)— difficulty, traffic, KEI, top-20 ranking apps, related keywords.run_autocomplete(prefix, ...)— what users actually type in store search.top_charts(store, country, category, collection)— see the leaderboard + gaps.list_competitors/add_competitor— track and compare against rivals.
Save the extracted keywords + ASO findings into memory (see Phase 3) so they're available when the app is built and published.
Phase 2 — Reason & propose
From the research, produce a clear proposal. Reason about:
- Problem — what pain does it solve?
- Target audience — who, concretely (not "everyone").
- Value — why is this better than what exists? The differentiator.
- Features — finalized list, split into:
- MVP / primary (must ship first)
- Secondary / later (sub-features, growth)
- Monetization (if relevant) — subscription, one-time, ads, freemium.
Present the final answer to the user as:
- What you researched (sources, competitors, pain points, ASO keywords).
- Features finalized (primary vs sub).
- Value it brings (problem → solution → why it wins).
Stop and wait for approval. Do not scaffold or write code until the user confirms.
Phase 3 — Persist to memory
After approval (or even before, once research is done), save everything using the memory system under {skill}/.skill-storage/{project}/:
memory.md— the "Project Information" profile, goal, target audience, finalized features, value proposition.decisions.md— feature priority decisions + why.- If ASO was run, save a dedicated section/file (e.g.
aso.md) with keywords, difficulty/traffic scores, niche clusters, competitor list, and the chosen app concept.
Derive the {project} slug from the app/package name (see the memory skill). Never store secrets/keys.
Then, when the user is ready to build, return to the parent skill flow: starter create → then "Phase 1 — Understand the project".
CLI vs latest source: if the installed @devatrii/starter CLI cannot generate from the newest starter source, ask yes/no first (Yes recommended) to update:
npm update -g @devatrii/starter
If yes: run that command yourself immediately, then continue project initialization. If no: continue with the version this CLI supports.
Rules
- Never scaffold/write code before approval.
- Don't invent competitor facts — research, don't assume.
- Keep the target audience concrete and the MVP scoped small.
- Save all research + ASO output to memory; don't redo it later.
Reference
- Parent flow:
../SKILL.md(scaffold + build phases). - Memory system:
../memory/SKILL.md. - ASO (if connected): the user's ASO MCP tools.
Signals
- GitHub stars
- 162
- Forks
- 28
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
kmp-starter-create-fresh-app- Source
- github.com/devatrii/kmp-starter-template