discovery

name	discovery
description	Research market demand for marketing prompts/AI automation use cases. Find what's most valuable, in-demand, and fully automatable. Store findings in Researches/discovery/.
user-invocable	false

Discovery Skill

Research what marketing prompts and AI automation use cases are most valuable right now

When to Run

Before deciding what to create or improve in the repo. Discovery informs priority — don't guess what's valuable, research it.

Research Process

1. Market Demand Research

Web search for current demand signals:

• "most useful AI marketing prompts {current_year}"
• "marketing tasks AI agents automate {current_year}"
• "ChatGPT marketing use cases most popular"
• "AI marketing automation workflows"
• "marketing prompt engineering best practices {current_year}"
• "what marketing tasks can be fully automated AI"

2. Automation Potential Research

Focus on what can run end-to-end without humans:

• "AI agent marketing no human in the loop"
• "fully automated marketing workflows AI"
• "marketing tasks AI replaces completely"
• "autonomous marketing agent use cases"

3. Competitor Analysis

Check what other prompt libraries prioritize:

• "marketing prompt library github"
• "AI marketing prompt collection best"
• "marketing AI templates most starred github"

4. Trend Detection

What's emerging and underserved:

• "AI marketing trends {current_year} new"
• "marketing AI capabilities nobody uses"
• "underrated AI marketing use cases"

What to Capture

For each finding, record:

Field	Description
Use case	What the prompt/workflow does
Demand signal	How you know it's in-demand (search volume, mentions, stars, etc.)
Automation score	1-5: can it run fully autonomous? (5 = no human needed)
Repo coverage	Does the repo already have this? Quality level?
Priority	High / Medium / Low based on demand x automation x gap

Storage

Write findings to Researches/discovery/:

• Researches/discovery/market-demand.md — Current demand signals, ranked use cases
• Researches/discovery/automation-candidates.md — Use cases scored by automation potential
• Researches/discovery/competitor-analysis.md — What other libraries do well/miss
• Researches/discovery/trends.md — Emerging opportunities

Each file should have a Last updated: YYYY-MM-DD header. Append new findings, don't overwrite — build a rolling knowledge base.

How to Use Findings

When deciding what to create/improve in the repo-improver workflow:

1. Read Researches/discovery/ first
2. Cross-reference with repo gaps (empty/sparse directories)
3. Pick the highest-impact action: high demand + high automation + low/no coverage = top priority