Trend Scout

trend-scout

Purpose

Market intelligence and technology trend analysis utility. Receives a topic or market segment, executes targeted searches across trend sources, analyzes competitor activity and community sentiment, and returns structured market intelligence. Stateless — no memory between calls.

Calls (outbound)

None — pure L3 utility using WebSearch tools directly.

Called By (inbound)

• brainstorm (L2): market context for product ideation
• marketing (L2): trend data for positioning and content
• autopsy (L2): identify if tech stack is outdated
• autopsy (L2): check if legacy tech is still maintained

Execution

Input

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topic: string           — market segment or technology to analyze (e.g., "AI coding assistants", "SvelteKit")
timeframe: string       — (optional) period of interest, defaults to "2026"
focus: string           — (optional) narrow the lens: "competitors" | "technology" | "community" | "all"

Step 1 — Define Scope

Parse the input topic and determine the analysis angle:

• Product/market: focus on competitors, pricing, user adoption
• Technology: focus on GitHub activity, npm/pypi downloads, framework adoption
• Community: focus on Reddit, HN, X/Twitter sentiment

Step 2 — Search Trends

Execute WebSearch with these query patterns:

• "[topic] 2026 trends"
• "[topic] vs alternatives 2026"
• "[topic] market share growth"
• "[topic] GitHub trending" or "[topic] npm downloads stats"

Collect results. Identify the most evidence-rich URLs per query.

Step 3 — Competitor Analysis

Execute WebSearch with:

• "[topic] competitors comparison"
• "best [topic] tools 2026"
• "[topic] alternative"

From results, extract:

• Top 3-5 competitors or alternative solutions
• Key differentiating features
• Pricing model if visible
• User sentiment signals (e.g., "users are switching from X to Y because...")

Step 4 — Community Sentiment

Execute WebSearch with:

• "site:reddit.com [topic]" or "[topic] reddit discussion"
• "[topic] site:news.ycombinator.com"
• "[topic] GitHub stars" or "[topic] downloads per week"

Extract:

• Community perception (positive/negative/mixed)
• Frequently cited pain points
• Frequently praised features
• Adoption velocity indicators (star growth, download counts)

Step 5 — Report

Synthesize all gathered data into the output format below. Note where data is sparse or conflicting.

Constraints

• Use WebSearch only — do not call WebFetch unless a specific page has critical data not in snippets
• Label all data points with their source
• Do not infer trends from a single data point — note confidence level
• If the topic is too broad, report what was analyzed and suggest narrowing

Output Format

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## Trend Report: [Topic]
- **Period**: [timeframe]
- **Confidence**: high | medium | low

### Trending Now
- [trend] — evidence: [source/stat]
- [trend] — evidence: [source/stat]

### Competitors
| Name | Key Differentiator | Sentiment |
|------|--------------------|-----------|
| [A]  | [feature]          | positive / mixed / negative |
| [B]  | [feature]          | positive / mixed / negative |

### Community Sentiment
- **Reddit/HN**: [summary]
- **GitHub activity**: [stars/downloads/issues signal]
- **Pain points**: [what users complain about]

### Emerging Patterns
- [pattern] — implication: [what this means for callers]

### Recommendations
- [actionable insight for the calling skill]

Sharp Edges

Known failure modes for this skill. Check these before declaring done.

Done When

• Topic scope defined (product/technology/community angle)
• Trend searches executed with 2026 timeframe
• Competitor analysis completed (top 3-5 players with differentiators)
• Community sentiment captured (Reddit/HN/GitHub signals)
• Confidence level assigned based on evidence quality
• Trend Report emitted with source citations for every data point

Cost Profile

~300-600 tokens input, ~200-400 tokens output. Haiku.