trend-to-product-mapper

name	trend-to-product-mapper
description	Maps viral social content and trending topics to concrete app opportunities by extracting the underlying problem and validating monetization fit.

Skill: trend-to-product-mapper

Purpose

Surface app ideas from real-world signals rather than speculation. The pipeline is: viral content → extract problem → map to app → validate monetization. This skill bridges social listening and product ideation.

User Interaction

Before executing, clarify which niche to analyze. Use available context to suggest options — don't ask blindly.

Step 1 — Infer candidates from context:

Check in this order:

1. memory/market_insights/ — list any existing trend-analysis files; extract niche names from filenames (e.g., nutrition-tiktok-2026-04.md → "nutrition")
2. memory/user_profile.md — check for domain, interests, or background fields
3. The current conversation — did the user mention a topic or market earlier?

Step 2 — Present options:

If candidates were found from context:

Which niche would you like to map to a product opportunity?

Based on available research:

• [inferred niche] (trend data: [platform] [period])
• [other inferred niches if any]

Or describe your own — e.g., "productivity tools for freelancers", "pet care", "language learning"

If no candidates were found:

What niche or market category would you like to explore? A few starting points:

• Fitness / nutrition / weight loss
• Personal finance / investing
• Mental health / mindfulness
• Productivity / focus
• Or describe your own in a few words

Step 3 — Confirm:

Once the user selects or describes a niche, confirm it back and proceed to Process.

Input

• Target niche (e.g., "nutrition", "fitness", "personal finance")
• memory/market_insights/<niche>-<platform>-<YYYY>-<MM>.md — one or more trend-analysis output files for the niche. Read the full narrative (Part 2) from each file; do not rely solely on the YAML frontmatter.
• memory/user_profile.md to filter for user's domain fit and constraints

Pipeline

trend-analysis output → scan for distinct opportunities → extract problem per opportunity → validate monetization → write up to 10 idea.md files

What to Read from Trend-Analysis Output

Section in trend-analysis file	What to extract
Emerging / Rising Trends	Fastest-moving problems and content angles
Financial Opportunities	Willingness-to-pay evidence and market size estimates
Key Hashtags / Subreddits / Keyword Clusters	Vocabulary the audience uses for the problem
Strategic Insights	Creator gaps and underserved segments
monetization_evidence (YAML frontmatter)	Quick-scan: is anyone already paying?

Prefer signals that appear across multiple platforms — cross-platform resonance is a stronger product signal than single-platform virality.

Process

1. Read available trend-analysis files relevant for the niche from memory/market_insights/.
2. Scan the full narrative of each file and identify distinct product opportunities — different underlying problems, different audience segments, or different app categories count as distinct. Do not list variations of the same idea.
3. Rank candidates by signal strength: weight cross-platform resonance and willingness-to-pay evidence most heavily. Drop candidates with no monetization signal.
4. Take the top 5–10 candidates (only include as many as have genuine signal — do not pad to reach 10).
5. For each candidate: extract the underlying problem (frustration or desire, not content topic), emotional trigger, audience vocabulary, app category, key features, key differentiator, and monetization evidence.
6. Assign a slug to each idea (kebab-case, max 40 chars, derived from the app concept).
7. Write one idea.md per idea to its own directory: memory/ideas/<slug>/idea.md.

Output

For each identified opportunity, write to memory/ideas/<slug>/idea.md.

The file uses YAML frontmatter for machine-readable metadata and a full narrative body for human readability and downstream skill consumption.

Frontmatter

---
idea_slug: <slug>
status: candidate
created_at: <ISO date>
source_niche: <niche>
source_files: []        # memory/market_insights/ filenames read
platforms_covered: []   # e.g. ["tiktok", "reddit"]
trend_velocity: rising-fast | rising | stable | declining
cross_platform_resonance: true | false
monetization_validated: true | false
confidence: high | medium | low
---

Body

Write the following sections in full prose or structured lists — no abbreviation:

# <App Concept Name>

## The Problem
What specific frustration or unmet desire is this idea addressing? Describe it from the user's perspective — the emotional experience, not the feature gap. Include the exact vocabulary the audience uses.

**Emotional trigger:** <the core feeling driving the behavior — anxiety, FOMO, shame, aspiration, etc.>

**Audience vocabulary:** <3–5 exact phrases pulled from hashtags, post titles, or search queries>

## Market Signal Evidence
What trend data supports this? For each platform covered, cite the specific signal:

- **TikTok:** <hashtag, view count, content angle>
- **Reddit:** <subreddit, recurring post type, upvote pattern>
- **App Store:** <category trend, review complaint pattern, new entrant activity>
- **Web Search:** <rising query, search volume indicator>

**Trend velocity:** <rising-fast |  |  | >
 


What is the app? Describe it in 2–3 sentences as if pitching to a user, not an investor. Focus on what it does and who it's for.

 


The 3–5 core features that directly address the problem. Each feature should map to a specific pain point or desire from the Market Signal Evidence section.

  — 
 ...


What makes this meaningfully different from what already exists? Reference the saturation assessment from the trend-analysis Financial Opportunities section. One clear wedge — not a feature list.


What proof exists that people pay for solutions to this problem?

 
 ...

 


 

Reasoning: <1–2 sentences explaining the confidence level — what's strong, what's uncertain>

After writing all files, present a summary table to the user:

#	Slug	App Concept	Confidence	Cross-Platform	Monetization
1	<slug>	...	high/medium/low	yes/no	validated/unvalidated
...

Notes