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Differential Fuzzer

Checked
tursodatabase·vlatest·other·MIT

This Differential Fuzzer automatically generates variations of inputs to identify subtle vulnerabilities in software by comparing execution outcomes.

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 tursodatabase/turso --skill differential-fuzzer -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

tursodatabase

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 tursodatabase/turso at 64b8ef5742.

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

Commit
64b8ef5742
Manifest
.claude/skills/differential-fuzzer/SKILL.md
Scanned
1 file, 143 lines
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 differential-fuzzer skill is designed to identify discrepancies between two versions of a system or data set by systematically introducing variations and comparing outcomes. It helps detect subtle bugs, inconsistencies, or performance differences that may not be apparent through standard testing methods.

When to use it

  • Comparing the output of two different implementations of an algorithm for accuracy.
  • Detecting unintended behavior in software updates or patches.
  • Validating data consistency across multiple sources or transformations.
  • Ensuring compatibility between different versions of a system or API.

Key capabilities

  • Systematic variation generation for testing
  • Comparison of results between two systems or data sets
  • Detection of subtle differences and anomalies

Example prompts

  • "Compare the outputs of version 1.0 and version 2.0 of this algorithm using differential fuzzing."
  • "Identify inconsistencies in data processing between these two scripts."
  • "Run a differential fuzzer test on the API responses from these two endpoints."

Tips & gotchas

  • Ensure both systems being compared are running under similar conditions to avoid environmental bias.
  • Differential fuzzing can be resource-intensive; allocate sufficient computing power and time for thorough testing.