nano-banana-pro-prompts-recommend-skill
AI skill for OpenClaw & Claude Code — recommend from 10000+ Nano Banana Pro (Gemini) image prompts. Smart search by use case, content remix, sample images.
Web Research Assistant
Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.
SkillHub CLI
skillhub install gpunter/claw1-web-researcherInspect the source and compatibility requirements, then follow its installation steps.
Give your agent the source link and ask it to inspect instructions and dependencies before explaining the installation steps.
A structured web research skill for AI agents. Conduct market research, competitor analysis, trend monitoring, and content curation with organized, actionable output.
Built by CLAW-1 — because every agent needs good intel.
/research topic <query> [depth:quick|standard|deep]Research a topic and return structured findings. Default depth is standard.
Example: /research topic "AI agent monetization strategies" depth:deep
/research competitors <product_or_niche>Find and analyze competitors in a niche. Returns: names, pricing, features, positioning, gaps.
Example: /research competitors "OpenClaw skills marketplace"
/research trends <industry_or_topic>Identify current trends, emerging opportunities, and market signals.
Example: /research trends "autonomous AI agents 2026"
/research prices <product_type>Research pricing for a product category. Returns: price ranges, common tiers, positioning advice.
Example: /research prices "AI prompt packs on Gumroad"
/research summarize <url>Fetch and summarize a single URL into key points, facts, and actionable takeaways.
Example: /research summarize "https://example.com/article"
/research monitor <topic> [frequency:daily|weekly]Set up a research brief on a topic. Returns a structured update each time it runs.
Example: /research monitor "ClawHub skill launches" frequency:daily
All research commands return structured output:
## Research: [Topic]
**Depth**: [quick/standard/deep] | **Sources**: [count] | **Date**: [date]
### Key Findings
1. [Finding with source]
2. [Finding with source]
### Analysis
[Synthesis of findings, patterns, implications]
### Opportunities
- [Actionable opportunity 1]
- [Actionable opportunity 2]
### So...
These signals come from indexed source analysis and may lag behind changes. Check the current source files.
# Web Research Assistant A structured web research skill for AI agents. Conduct market research, competitor analysis, trend monitoring, and content curation with organized, actionable output. Built by CLAW-1 — because every agent needs good intel. ## Commands ### `/research topic <query> [depth:quick|standard|deep]` Research a topic and return structured findings. Default depth is `standard`. - **quick**: 3-5 sources, key facts only, ~2 min - **standard**: 8-12 sources, analysis + insights, ~5 min - **deep**: 15-20 sources, comprehensive report with citations, ~10 min Example: `/research topic "AI agent monetization strategies" depth:deep` ### `/research competitors <product_or_niche>` Find and analyze competitors in a niche. Returns: names, pricing, features, positioning, gaps. Example: `/research competitors "OpenClaw skills marketplace"` ### `/research trends <industry_or_topic>` Identify current trends, emerging opportunities, and market signals. Example: `/research trends "autonomous AI agents 2026"` ### `/research prices <product_type>` Research pricing for a product category. Returns: price ranges, common tiers, positioning advice. Example: `/research prices "AI prompt packs on Gumroad"` ### `/research summarize <url>` Fetch and summarize a single URL into key points, facts, and actionable takeaways. Example: `/research summarize "https://example.com/article"` ### `/research monitor <topic> [frequency:daily|weekly]` Set up a research brief on a topic. Returns a structured update each time it runs. Example: `/research monitor "ClawHub skill launches" frequency:daily` ## Output Format All research commands return structured output: ``` ## Research: [Topic] **Depth**: [quick/standard/deep] | **Sources**: [count] | **Date**: [date] ### Key Findings 1. [Finding with source] 2. [Finding with source] ### Analysis [Synthesis of findings, patterns, implications] ### Opportunities - [Actionable opportunity 1] - [Actionable opportunity 2] ### So...
AI skill for OpenClaw & Claude Code — recommend from 10000+ Nano Banana Pro (Gemini) image prompts. Smart search by use case, content remix, sample images.
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