| 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:
memory/market_insights/ — list any existing trend-analysis files; extract niche names from filenames (e.g., nutrition-tiktok-2026-04.md → "nutrition")memory/user_profile.md — check for domain, interests, or background fields- 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
- Read available trend-analysis files relevant for the niche from
memory/market_insights/.
- 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.
- Rank candidates by signal strength: weight cross-platform resonance and willingness-to-pay evidence most heavily. Drop candidates with no monetization signal.
- Take the top 5–10 candidates (only include as many as have genuine signal — do not pad to reach 10).
- 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.
- Assign a slug to each idea (kebab-case, max 40 chars, derived from the app concept).
- 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: []
platforms_covered: []
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
