How to Build an AI-Powered Research Pipeline with Search Skills

A step-by-step guide to installing AI search skills, configuring them for web research, and building a repeatable research pipeline that runs entirely from your terminal.

What This Gets You#

By the end of this guide, you’ll have a working AI-powered research pipeline. You’ll be able to ask your AI agent to research any topic — gathering information from multiple sources, synthesizing findings, and producing structured output — without manually opening a browser, copying URLs, or writing one-off scripts. Total setup time: about 15 minutes.

This isn’t about one specific tool. The pipeline approach works with any search-enabled skill — AnySearch, GPT Researcher, Tavily, Crawl4AI, or custom integrations. You’ll understand the pattern well enough to adapt it to whatever skill you choose.

What You Need Before Starting#

You need three things:

1. An AI agent that supports skills — Claude Code, Cursor, Codex, or any agent that works with the npx skills toolchain. Run npx --version to confirm you have Node.js 18+.
2. A search API key — Most search skills require a key from their backend provider (Tavily, OpenAI, SerpAPI, etc.). Check the skill’s README for which one it needs.
3. Your terminal — Every step in this guide is a command you run. No GUIs.

If you already have an agent and Node.js, you’re two commands away from your first research query.

Step 1: Choose and Install a Search Skill#

The first decision is which search skill to use. Here are the most common options ranked by what they’re best at:

For this guide, I’ll use AnySearch because it supports the widest range of search modes — general web, vertical domains, parallel batch searches, and full-page content extraction — all from a single skill.

To install AnySearch, download the latest release and place it in your agent’s skill directory:

# Download the skill
curl -L -o anysearch-skill.zip https://github.com/anysearch-ai/anysearch-skill/archive/refs/tags/v2.1.0.zip

# Extract it
unzip anysearch-skill.zip

# Move to your agent's skill directory (Claude Code example)
mv anysearch-skill-2.1.0 ~/.claude/skills/anysearch

If you’re using a different agent, adjust the target directory:

• OpenCode: ~/.config/opencode/skills/anysearch
• Cursor/Windsurf: <project>/.skills/anysearch
• Shared agents: ~/.agents/skills/anysearch

Step 2: Configure Your API Key#

Most search backends require an API key. AnySearch reads this from your environment:

# Find which API key you need (check the skill docs)
export ANYSEARCH_API_KEY="sk-your-key-here"

Add this to your shell profile (~/.bashrc, ~/.zshrc, or your agent’s config) so it’s available every time you start a session:

echo 'export ANYSEARCH_API_KEY="sk-your-key-here"' >> ~/.bashrc
source ~/.bashrc

Different search skills use different environment variables. GPT Researcher uses TAVILY_API_KEY. Crawl4AI reads CRAWL4AI_API_KEY. Always check the skill’s documentation — the variable name is usually documented at the top of its README.

Step 3: Run Your First Research Query#

With the skill installed and configured, you can now run research queries through your AI agent. The exact invocation depends on your agent, but the pattern is consistent:

In Claude Code:

/search anysearch query "What are the latest developments in AI code generation tools in 2026?"

In Cursor or Codex (via natural language):

Use AnySearch to find recent articles about AI code generation tools. Look for pricing, features, and user reviews.

The skill will:

1. Route your query to the appropriate search backend
2. Fetch results from multiple sources
3. Return structured data your agent can process

What happens next is what makes this powerful. Your agent doesn’t just show you links — it reads the results, understands the context, and can answer follow-up questions about what it found.

Step 4: Build a Multi-Query Research Pipeline#

Single queries are useful, but the real power comes from running multiple research questions in parallel. This is where AnySearch’s batch mode shines.

Instead of asking one question at a time, structure your research as a set of questions:

Research topic: "AI video generation tools in 2026"

Questions:
1. What are the top AI video generation tools and their pricing?
2. How does Kling 3.0 compare to Runway Gen-3 and Pika?
3. What are the limitations of current AI video models?
4. What does the community say about video quality and consistency?
5. What are the enterprise use cases being adopted?

Hand this list to your agent with a batch research instruction. Most search skills can process 10-20 queries in the time a single sequential search takes, because they parallelize the network requests:

Run a batch research using AnySearch on these 5 questions about AI video generation.
Return the results as a structured markdown table with columns: Question, Key Findings, Sources.

The agent executes all five searches concurrently, aggregates the results, and presents a synthesized answer. This is the difference between spending 30 minutes clicking through search results and spending 30 seconds watching your agent do the work.

Step 5: Process and Store Results#

Research is only useful if you can act on it. After your agent returns results, here’s how to make them stick:

Save to a markdown file: Ask your agent to write the findings to a file:

Save the research results to docs/research-ai-video-2026.md with proper headings and source links.

Feed into a summary for later reference:

Summarize these findings in 3 bullet points and append them to my research journal.

Use as context for a new task:

Based on the research you just did, write a comparison of the top 3 AI video tools.

This is where the pipeline becomes self-reinforcing. Each research session enriches your agent’s context for the next task. Over time, you build a knowledge base without manual effort.

Troubleshooting#

Next Steps#

Once you have the basic pipeline working, here’s what to try next:

• Add a scraping skill alongside your search skill for deeper page-level extraction
• Schedule recurring research using cron or your agent’s scheduling features
• Combine with a writing skill to turn research into drafts automatically
• Chain multiple searches where the output of one query feeds the input of the next

The pipeline pattern — install, configure, query, process, store — works the same regardless of which search skill you choose. Once you’ve done it once, you can apply it to any research domain with minimal friction.