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Evaluating Code Models

Checked
orchestra-research·vlatest·other·MIT

This skill assesses code model outputs for correctness and quality, ensuring generated code meets specified requirements and is reliable.

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 orchestra-research/ai-research-skills --skill evaluating-code-models -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

orchestra-research

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 orchestra-research/ai-research-skills at 773a52944b.

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

Commit
773a52944b
Manifest
11-evaluation/bigcode-evaluation-harness/SKILL.md
Scanned
4 files, 1,450 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 evaluates code models, providing assessments of their performance and capabilities. It can analyze model outputs against specific benchmarks or criteria, offering insights into strengths and weaknesses. The tool aims to facilitate a deeper understanding of code generation models for research and development purposes.

When to use it

  • Benchmarking new code models: Compare the performance of different code generation models on standardized datasets.
  • Identifying model limitations: Pinpoint specific areas where a code model struggles, such as particular programming languages or problem types.
  • Evaluating custom fine-tuning: Assess the impact of fine-tuning a code model on a specialized dataset.
  • Debugging and improving existing models: Analyze model failures to understand root causes and guide improvements.

Key capabilities

  • Code Model Evaluation
  • Performance Benchmarking
  • Output Analysis
  • Identification of Limitations

Example prompts

  • "Evaluate the performance of CodeGen-Multi on the HumanEval dataset."
  • "Analyze the outputs of this code model when generating Python code for data analysis tasks."
  • "Compare the accuracy and efficiency of StarCoderBase vs. Llama-3-Code-Instruct on a set of coding challenges."

Tips & gotchas

The skill's effectiveness depends on having access to appropriate datasets or benchmarks for comparison. Results should be interpreted within the context of the specific evaluation criteria used.