Iterative Retrieval
This skill iteratively refines search queries based on feedback, delivering increasingly relevant results and saving you time.
Install on your platform
We auto-selected Claude Code based on this skill’s supported platforms.
Run in terminal (recommended)
claude mcp add iterative-retrieval npx -- -y @trustedskills/iterative-retrieval
Or manually add to ~/.claude/settings.json
{
"mcpServers": {
"iterative-retrieval": {
"command": "npx",
"args": [
"-y",
"@trustedskills/iterative-retrieval"
]
}
}
}Requires Claude Code (claude CLI). Run claude --version to verify your install.
About This Skill
What it does
The iterative-retrieval skill enables AI agents to perform repeated data retrieval operations, refining search queries based on previous results. This allows for more accurate and context-aware information gathering over time by iteratively improving the search process.
When to use it
- When searching for complex or multi-faceted information that requires refinement.
- In scenarios where initial search results are incomplete or ambiguous.
- For tasks requiring continuous data updates, such as monitoring or analytics.
- When building a knowledge base incrementally from multiple sources.
Key capabilities
- Iterative query refinement based on prior retrieval outcomes
- Context-aware search improvements over successive iterations
- Support for multi-step information gathering workflows
Example prompts
- "Find the latest research papers on climate change, and refine the search to include only peer-reviewed articles."
- "Search for recent news about AI regulations in Europe, then narrow down to official policy documents."
- "Gather data on stock market trends over the past year, refining results by sector and region."
Tips & gotchas
- Ensure that your initial query is as specific as possible to avoid unnecessary iterations.
- Be mindful of potential feedback loops where iterative refinement may lead to redundant or circular searches.
Tags
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