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🦀 ClawHub

Dividend Growth Pullback Screener

by @veeramanikandanr48

Use this skill to find high-quality dividend growth stocks (12%+ annual dividend growth, 1.5%+ yield) that are experiencing temporary pullbacks, identified by RSI oversold conditions (RSI ≤40). This skill combines fundamental dividend analysis with technical timing indicators to identify buying opportunities in strong dividend growers during short-term weakness.

Versionv0.1.0

#clawhub

Source

Use this skill with your agent

Most visitors already have an agent. Pick your environment, install or copy the workflow, then run the smoke-test prompt above.

Manusinvite

Task-oriented agent. Great for testing AI skills end-to-end.

Try Manus →

OpenClaw

Local-first agent. Install skills via ClawHub CLI.

Set up OpenClaw →

Claude Code

Anthropic's coding agent. Paste the prompt or SKILL.md into your session.

Claude Code docs →

Cursor

AI-powered IDE. Use the smoke-test prompt in Cursor Agent.

Open Cursor →

Continue.dev

Open-source AI code assistant. Add SKILL.md as a custom tool.

Continue docs →

Windsurf

Agentic IDE by Codeium. Paste the prompt into Cascade.

Try Windsurf →

Cline

VS Code extension for autonomous coding with MCP tools.

Cline on GitHub →

Copilot Workspace

GitHub's AI dev environment. Suitable for code-generation skills.

Copilot Workspace →

What to do next

Skills are meant to be used inside your own AI agent. Install it, run a quick smoke test, then ask your agent to apply it to your real task.

Install into your agentCopy the ClawHub install command and run it where your OpenClaw/agent environment is configured.

Run a smoke testUse the test prompt below to confirm the skill loads and understands the workflow before relying on it.

Use it in your own agentPaste your actual task into Manus, OpenClaw, Claude Code, Cursor, or another agent that supports skills.

I just installed the Dividend Growth Pullback Screener skill. Please run a quick smoke test: explain what this skill can do, ask me for the minimum input it needs, then produce one small sample output for a realistic task.