agentsclimarketplace

Keyword research

Skill getappniche/aso-skills/skills/keyword-research

ASO & app-market research skills for AI agents — Claude Code, Cursor, and any MCP client. Install: npx skills add getappniche/aso-skills

Install
npx -y skills add getappniche/aso-skills --skill keyword-research

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

  • 12 days oldThe repository was created 12 days ago. New is not bad, but a brand new repository carrying a familiar-sounding name is the shape a typosquat arrives in, and there has been no time for anyone else to find a problem with it.
  • 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Build a prioritized App Store keyword strategy — generate candidate keywords, score their difficulty and opportunity, and recommend where to compete. Use for ASO keyword planning, keyword gap questions, or "what should my app rank for".

SKILL.md

2.4 KB, as published. Nobody here has run it

Keyword Research

Produce a keyword plan a developer can act on this week, not a raw dump of scores.

Requirements

GetAppNiche MCP connected: get_keyword_difficulty, batch_keyword_difficulty, get_supported_countries (free). Keyword scoring is the expensive call — 10 credits per keyword — so generate first, score second, and score only the shortlist.

Workflow

  1. Collect context. App's core job, target market (default US / en unless told otherwise — verify uncommon markets against get_supported_countries), and any keywords already targeted.
  2. Generate candidates by intent bucket, before any scoring:
    • Core: what the app literally is ("habit tracker")
    • Problem: what the user is trying to fix ("stop procrastinating")
    • Feature: specific capabilities ("streak counter", "widget")
    • Adjacent: neighboring queries the audience also types Aim for 15–25 candidates across buckets.
  3. Shortlist ~10 by judgment (relevance × plausible traffic) and score them in one batch_keyword_difficulty call. Batch is capped at 10 keywords — prioritize before scoring rather than paging through everything.
  4. Read the scores like a strategist. The sweet spot is meaningful popularity with difficulty your app can survive: a new app should mostly avoid head terms and win mid-tail; an established app can contest harder terms. Note which top-ranking apps hold each keyword — outranking a giant is a different project than outranking abandonware.
  5. Recommend. Split the final list into: target now, build toward, skip (and why).

Output

A ranked table — Keyword · Popularity · Difficulty · Verdict — then the three-tier recommendation with one sentence of reasoning per tier, and suggested placement (title vs. subtitle vs. keyword field) for the "target now" set.

Guardrails

  • Scores are point-in-time snapshots; treat small differences (±5) as noise.
  • Never present a keyword list without a recommendation — prioritization is the value.
  • State the assumed country/language; rankings differ per storefront.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.