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Iterative Retrieval

Checked
affaan-m·vlatest·other·MIT

This skill iteratively refines search queries based on feedback, delivering increasingly relevant results and saving you time.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add affaan-m/ecc --skill iterative-retrieval -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

affaan-m

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned affaan-m/everything-claude-code at d3b8a3e908.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
d3b8a3e908
Manifest
skills/iterative-retrieval/SKILL.md
Scanned
3 files, 580 lines
Copies in repo
3 — all of them scanned
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

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.