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Ai Error Analysis And Eval Design

Checked
samarv·vlatest·ai-ml

Helps with AI, analysis, design as part of agent workflows workflows.

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 samarv/shanon --skill ai-error-analysis-and-eval-design -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

samarv

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 samarv/shanon at f3a3b3f576.

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

Commit
f3a3b3f576
Manifest
.claude/skills/ai-error-analysis-and-eval-design/SKILL.md
Scanned
1 file, 61 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

This skill enables AI agents to analyze errors in their outputs, identify patterns and root causes, and design evaluation strategies. It facilitates iterative improvement of agent performance through structured error analysis and targeted adjustments. The skill can also help create robust testing frameworks for evaluating new agent versions or modifications.

When to use it

  • Debugging Agent Behavior: When an AI agent consistently produces incorrect or undesirable results, this skill helps pinpoint the underlying issues.
  • Improving Model Accuracy: After training a model, utilize this skill to systematically identify and address common error types impacting overall accuracy.
  • Designing Evaluation Metrics: Create custom evaluation frameworks tailored to specific tasks and desired outcomes by defining success criteria and failure modes.
  • Testing Agent Updates: Before deploying new versions of an agent, use the skill to proactively assess potential regressions or unexpected behavior.

Key capabilities

  • Error analysis
  • Root cause identification
  • Evaluation strategy design
  • Pattern recognition in errors
  • Robust testing framework creation

Example prompts

  • "Analyze these 10 examples of incorrect agent responses and identify common error patterns."
  • "Design an evaluation metric for assessing the quality of summaries generated by this AI agent."
  • "What are the likely root causes for the agent consistently failing to follow complex instructions?"

Tips & gotchas

This skill requires a dataset of agent outputs, including both correct and incorrect examples, for effective error analysis. The accuracy of the results depends heavily on the quality and representativeness of this data.