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Error Analysis

Checked
hamelsmu·vlatest·testing·MIT

Analyzes AI output errors to identify patterns, root causes, and suggest targeted improvements for enhanced performance.

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 hamelsmu/evals-skills --skill error-analysis -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

hamelsmu

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 hamelsmu/evals-skills at 22418da2bf.

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

Commit
22418da2bf
Manifest
skills/error-analysis/SKILL.md
Scanned
1 file, 159 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

error-analysis

What it does

This skill enables AI agents to systematically identify, categorize, and analyze errors within data or system outputs. It helps agents move beyond simple detection to understanding the root causes of failures in complex workflows.

When to use it

  • Debugging automated pipelines where output formats deviate from expected schemas.
  • Analyzing log files to pinpoint specific failure points in software execution.
  • Validating model responses against strict constraints or business rules.
  • Troubleshooting data inconsistencies before they propagate through a larger system.

Key capabilities

  • Automated error detection across various input types.
  • Categorization of errors by severity and type.
  • Root cause analysis suggestions based on detected patterns.
  • Structured reporting of findings for human review or automated remediation.

Example prompts

  • "Analyze this batch of API responses and identify any JSON parsing errors."
  • "Review the provided system logs and categorize the errors by frequency and impact."
  • "Check these generated reports against our compliance checklist and list any violations found."

Tips & gotchas

Ensure input data is clean enough for pattern recognition; noisy inputs may lead to false positives. This skill complements but does not replace manual debugging for complex, non-deterministic issues.