- Home
- Skills
- Spring Ai Alibaba
- Examples
- Web Search
web-search_skill
- Java
2.2k
GitHub Stars
1
Bundled Files
2 months ago
Catalog Refreshed
4 months ago
First Indexed
Readme & install
Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.
Installation
Preview and clipboard use veilstrat where the catalogue uses aiagentskills.
npx veilstrat add skill spring-ai-alibaba/examples --skill web-search- SKILL.md4.2 KB
Overview
This skill provides a repeatable, file-driven workflow for conducting comprehensive web research by spawning focused subagents. It emphasizes upfront planning, controlled delegation, and a systematic synthesis step to produce well-sourced answers and optional final reports. The process keeps all artifacts organized in a local research folder and limits web searches to avoid over-researching.
How this skill works
First, create a dedicated research folder and write a short research plan defining the main question and 2–5 distinct subtopics. Next, spawn research subagents (tasks) for each subtopic; each subagent performs a small number of web searches and writes findings to local files. Finally, read the saved findings, synthesize a unified answer with citations and identified gaps, and optionally write a final report back to the research folder.
When to use it
- Research complex topics that require multiple distinct information threads
- Gather current, web-based evidence and synthesize it into a single report
- Perform comparative analysis across technologies, vendors, or methods
- Produce well-cited research deliverables for stakeholders
- Break down a large investigation into parallel, bounded subtasks
Best practices
- Always create research_[topic_name] and a research_plan.md before spawning subagents
- Define 2–5 non-overlapping subtopics; keep each subagent’s scope narrow and specific
- Limit each subagent to 3–5 web searches to maintain focus and efficiency
- Require subagents to write findings to files; use local read_file to collect results
- Synthesize by reading all findings files, cite URLs from those files, and note limitations
Example use cases
- Compare pricing, features, and user reviews for three cloud AI providers
- Compile recent academic and industry findings on a niche machine learning technique
- Create a vendor-neutral report on security best practices for Java web apps
- Survey regulatory developments across multiple countries for compliance planning
- Produce a concise, sourced briefing for product management or executive teams
FAQ
Yes. The workflow relies on subagents saving findings to local files so you can read and synthesize them reliably.
How many subagents should I run in parallel?
You can run up to three subagents in parallel for efficiency; choose parallelism based on available attention for synthesis.