Content Trend Analyzer

Aggregates and analyzes content trends across platforms to identify hot topics, user intent, content gaps, and generates data-driven article outlines.

Security Scan

The skill's instructions, inputs, and outputs are internally consistent with a cross‑platform trend analysis tool and it does not request credentials or install code.

Purpose & Capability

The name and description (content trend aggregation and outline generation) match the SKILL.md: it instructs the agent to run web searches and fetch public trend pages across listed platforms. No unrelated credentials, binaries, or installs are requested.

Instruction Scope

Instructions are high-level and limited to public web collection (web_search / web_fetch) and analysis (clustering, intent, gap scoring). This stays within the stated purpose, but the guidance is open‑ended (multiple searches per platform, flexible query construction), so an agent could perform broad scraping of public pages if not constrained.

Install Mechanism

No install spec and no code files — the skill is instruction-only so nothing is written to disk or downloaded during install. This is the lowest-risk install posture.

Credentials

The skill declares no required environment variables, credentials, or config paths. All requested data sources are public platforms; there are no disproportionate secret or system access demands.

Persistence & Privilege

always is false and autonomous invocation is the platform default. The skill does not request forced or persistent system presence or modification of other skills' configs.

Assessment

This is an instruction-only skill that scrapes and analyzes public content to produce topic outlines. Before installing: be aware the agent will perform broad web_search/web_fetch queries (public scraping) — set limits if you want to avoid excessive crawling or rate‑limit issues. The skill does not request API keys, so it cannot access private accounts unless you later provide credentials; if you plan to connect paid/privileged services (e.g., Baidu Index APIs, Twitter/X API), expect that additional credentials would be needed and should be scoped minimally. The skill's source/homepage is unknown — although its content is coherent, verify provenance and monitor first runs to ensure it behaves as you expect before enabling wide autonomous use.

Multi-platform content trend aggregation and analysis, producing data-driven article outlines and content strategies. Triggers when users need: content trend analysis, topic heat tracking, trending topic discovery, user intent analysis, content gap mining, competitive content research, SEO keyword trends, data-driven article outline generation, content strategy formulation.

Trigger Keywords

Trend analysis, content trends, trending topics, trend analysis, content gap, topic analysis, topic selection, content strategy, outline generation, content outline.

Workflow

1. Requirement Understanding → Determine the analysis domain, target platforms, and time range
2. Data Collection → Perform layered search by platform, aggregate trend signals
3. Intent Analysis → Identify user pain points, interest shifts, and information gaps
4. Gap Mining → Compare existing content coverage to discover untapped opportunities
5. Outline Generation → Output structured article outlines + topic scores

Step 1: Requirement Understanding

Confirm with the user (if not explicitly provided):

• Domain/Industry: Technology, Finance, Health, Education, etc.
• Target Audience: B2B/B2C, technical level, region
• Target Platforms: Platforms where content will be published (affects style and depth)
• Time Range: Real-time trending / Last 7 days / Last 30 days / Quarterly
• Analysis Depth: Quick scan / Standard report / Competitive benchmarking

Step 2: Data Collection

Collect data in layers by priority, using the corresponding tool for each layer:

Layer 1: Trend Baseline (Mandatory)

Platform	Tool	Content Collected
Google Trends	web_fetch trends.google.com	Search heat trends, related queries, geographic distribution
Reddit	web_search site:reddit.com	Popular discussions, highly upvoted answers, community pain points
YouTube	web_search site:youtube.com	Video popularity, comment sentiment, title keywords

Layer 2: In-Depth Content (On Demand)

Platform	Tool	Content Collected
Medium/Substack	web_search site:medium.com OR site:substack.com	Long-form topic selection, subscriber interaction, writing styles
Twitter/X	web_search site:x.com	Real-time discussions, hashtags, KOL perspectives
Zhihu/Weibo	web_search site:zhihu.com OR site:weibo.com	Chinese community Q&A, trending topics
Baidu Index	web_fetch index.baidu.com	Chinese search trends, audience profiles
Product Hunt	web_search site:producthunt.com	New product trends, technology directions

Layer 3: Competitive Benchmarking (For In-Depth Reports)

Platform	Tool	Content Collected
Competitor Blogs/Official Accounts	web_fetch + web_search	Existing content coverage, publishing frequency, engagement data
GitHub Trending	web_search site:github.com/trending	Developer technology trends

Collection Strategy:

• Execute 2-3 targeted searches per platform (from different angles)
• Search query combinations: "{domain} + {time-related term}", "{domain} + pain point term", "{domain} + how/why/what"
• Record for each finding: source, popularity metric, core topic, user sentiment

Step 3: Intent Analysis

Perform the following analysis on the collected data:

1. Topic Clustering: Group similar topics; identify 3-5 core themes
2. Intent Classification:
   • 🎯 Learning (how-to, tutorials, guides)
   • 🤔 Exploratory (comparisons, reviews, analysis)
   • 😤 Pain Points (errors, problems, complaints)
   • 🚀 Forward-Looking (trend forecasts, new tools, best practices)
3. Sentiment Tendency: Positive/Negative/Neutral; identify controversial topics
4. User Personas: Infer technical level and role identity from discussion language

Step 4: Gap Mining

Compare existing content with user needs:

Existing Content Coverage Matrix:
  Topic A: ████░░░░ 50% (Lacks advanced content)
  Topic B: ██░░░░░░ 25% (Significant gaps)
  Topic C: ████████ 90% (Saturated; difficult to differentiate)
  Topic D: ░░░░░░░░  0% (Blue ocean opportunity)

Scoring Dimensions:

• Demand Intensity (search volume + discussion heat) → Scale of 1-5
• Content Gap (insufficient existing coverage) → Scale of 1-5
• Differentiation Potential (likelihood of a unique angle) → Scale of 1-5
• Timeliness (current heat window) → Scale of 1-5
• Composite Recommendation Score = Weighted average

Step 5: Outline Generation

See references/outline-templates.md for output format.

Generate for each high-scoring topic:

Article Outline Structure

## [Topic Title]
- Recommendation Score: X.X/5.0
- Target Platform: [Platform]
- Estimated Word Count: [Word Count]
- Difficulty: [Beginner/Intermediate/Expert]

### Core Value Proposition
[One sentence explaining what the reader will gain]

### Outline
1. [Introduction hook - based on real user pain points]
   - Data Support: [Cite trend data]
2. [Core Argument 1]
   - Sub-points + Examples/Data
3. [Core Argument 2]
   - Sub-points + Examples/Data
4. [Core Argument 3]
   - Sub-points + Examples/Data
5. [Conclusion + Call to Action]

### SEO Recommendations
- Primary Keyword: [Keyword]
- Long-Tail Keywords: [KW1], [KW2], [KW3]
- Title Alternatives: [Alt Title 1], [Alt Title 2]

Topic Ranking Report

Generate a comparison table of all candidate topics:

| Rank | Topic | Rec. Score | Demand Intensity | Content Gap | Differentiation | Timeliness |
|------|-------|-----------|-----------------|-------------|----------------|------------|
| 1    | ...   | 4.5       | 5               | 4           | 4              | 5          |

Output Format Selection

• Quick Scan: Concise table + Top 3 outlines
• Standard Report: Full analysis + Top 5 outlines + Gap matrix
• In-Depth Report: Full dataset + Competitive benchmarking + Top 10 outlines + Monthly recommendations

Notes

• Annotate all data points with source URLs to ensure traceability
• Distinguish between "noise topics" (short-term hype) and "trend topics" (sustained growth)
• For Chinese content, prioritize data from Zhihu, Weibo, and Baidu Index
• For English content, prioritize Google Trends, Reddit, and Hacker News
• For tech topics, additionally check GitHub Trending and Stack Overflow