How do I install the Trend Scouting skill?

Run npx skillmds@latest add aman-bhandari/trend-scouting in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Trend Scouting safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Trend Scouting?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Trend Scouting free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Trend Scouting?

aman-bhandari (@aman-bhandari) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Trend Scouting by aman-bhandari — Agent Skill

Name: Trend Scouting
Author: aman-bhandari

Trend Scouting

Actively scans the AI engineering landscape using WebSearch for trending repos, blogs, job postings, new tools, and framework releases. Triggers on /trends, during weekly reflections, or when checking industry relevance. Surfaces findings relevant to current topic and flags curriculum adjustments.

File contents

Trend Scout -- Industry Intelligence

Actively scan the AI engineering landscape to keep the learning system current and the student hireable.

Trigger

/trends or "what's trending"
During weekly reflection (/reflect)
When checking curriculum alignment (/system-check)

Execution

Search these sources using WebSearch:

GitHub Trending:
- Python repos: trending daily/weekly
- AI/ML/agents/LLM tooling repos
- MCP servers and clients
Key Blogs (check latest posts):
- Simon Willison (simonwillison.net)
- Chip Huyen (huyenchip.com)
- Hamel Husain (hamel.dev)
- Lilian Weng (lilianweng.github.io)
- swyx / Latent.Space (latent.space)
- Anthropic engineering blog
- LangChain blog
Job Market:
- "AI engineer" recent job postings -- what skills are listed
- Search for shifts in requirements vs. our baseline data
New Releases:
- Claude API / Claude Code updates
- LangGraph, CrewAI, OpenAI Agents SDK releases
- New MCP servers worth knowing about
- Python ecosystem updates relevant to AI engineering
Filter for relevance:
- Is this relevant to the student's current topic?
- Is this something the market demands? (cross-reference with market data in memory)
- Is this a trend (sustained adoption) or hype (Twitter excitement, no production use)?

Output digest:

TREND SCOUT -- {date}

Current topic: {sprint name}

Relevant to your learning:
- [finding] -- why it matters, what to do about it

Market signals:
- [signal] -- implication for your path

Curriculum adjustment needed?
- [yes/no + specific recommendation]

Persist: Save significant findings to memory. Update knowledge/wiki/career/market-trends.md if wiki exists.

Rules

Distinguish trends from hype. Look for: production adoption, hiring signals, major company backing.
Always connect findings to the student's learning path.
Keep digest to 5-10 bullet points. Concise, not exhaustive.
Source everything with URLs.
Flag curriculum adjustments immediately -- don't wait for Friday.

Market Baseline (from research)

See memory file reference_market_requirements.md for the baseline data:

Python 82.5%, RAG 35.9%, Agents 14.4% and growing
93.1% of roles need skills beyond GenAI
Evaluation/observability is the hiring differentiator

---name: trend-scoutingdescription: Actively scans the AI engineering landscape using WebSearch for trending repos, blogs, job postings, new tools, and framework releases. Triggers on /trends, during weekly reflections, or when checking industry relevance. Surfaces findings relevant to current topic and flags curriculum adjustments.---# Trend Scout -- Industry IntelligenceActively scan the AI engineering landscape to keep the learning system current and the student hireable.## Trigger- `/trends` or "what's trending"- During weekly reflection (`/reflect`)- When checking curriculum alignment (`/system-check`)## Execution1. **Search these sources using WebSearch:** **GitHub Trending:** - Python repos: trending daily/weekly - AI/ML/agents/LLM tooling repos - MCP servers and clients **Key Blogs (check latest posts):** - Simon Willison (simonwillison.net) - Chip Huyen (huyenchip.com) - Hamel Husain (hamel.dev) - Lilian Weng (lilianweng.github.io) - swyx / Latent.Space (latent.space) - Anthropic engineering blog - LangChain blog **Job Market:** - "AI engineer" recent job postings -- what skills are listed - Search for shifts in requirements vs. our baseline data **New Releases:** - Claude API / Claude Code updates - LangGraph, CrewAI, OpenAI Agents SDK releases - New MCP servers worth knowing about - Python ecosystem updates relevant to AI engineering2. **Filter for relevance:** - Is this relevant to the student's current topic? - Is this something the market demands? (cross-reference with market data in memory) - Is this a trend (sustained adoption) or hype (Twitter excitement, no production use)?3. **Output digest:** ``` TREND SCOUT -- {date} Current topic: {sprint name} Relevant to your learning: - [finding] -- why it matters, what to do about it Market signals: - [signal] -- implication for your path Curriculum adjustment needed? - [yes/no + specific recommendation] ```4. **Persist:** Save significant findings to memory. Update `knowledge/wiki/career/market-trends.md` if wiki exists.## Rules- Distinguish trends from hype. Look for: production adoption, hiring signals, major company backing.- Always connect findings to the student's learning path.- Keep digest to 5-10 bullet points. Concise, not exhaustive.- Source everything with URLs.- Flag curriculum adjustments immediately -- don't wait for Friday.## Market Baseline (from research)See memory file `reference_market_requirements.md` for the baseline data:- Python 82.5%, RAG 35.9%, Agents 14.4% and growing- 93.1% of roles need skills beyond GenAI- Evaluation/observability is the hiring differentiator

Frequently asked questions