Evaluating Llms Harness
CheckedThis skill assesses LLM harnesses for performance and quality, streamlining evaluation & optimizing your AI workflows effectively.
Install on your platform
Showing Claude Code. One command installs this skill into any of the agents below.
- 1
Run this in your project
terminal$ npx skills add ovachiever/droid-tings --skill evaluating-llms-harness -a claude-code Uses the open-source
skillsCLI and needs Node.js. Add-gto install it for your user instead of this project.
What we know about this skill
Publisher
ovachiever
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Install target
Live repository
Not pinned. Installing fetches whatever the repository holds at the time you run the command.
Automated safety checks
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 ovachiever/droid-tings at 7acd12a754.
The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.
- Commit
- 7acd12a754
- Scanned
- 5 files, 2,272 lines
About this skill
What it does
This skill, "Evaluating LLMs Harness," provides a framework for systematically evaluating Large Language Models (LLMs). It allows users to define evaluation criteria and run tests against different models, providing structured feedback. The harness facilitates comparing model performance across various benchmarks and tasks, enabling informed decisions about which LLM best suits specific needs.
When to use it
- Model Selection: Compare the performance of several LLMs on a defined set of tasks before choosing one for your application.
- Benchmark Tracking: Monitor the progress of an LLM over time as it's fine-tuned or updated.
- Custom Evaluation: Assess LLMs against custom evaluation criteria specific to your use case, beyond standard benchmarks.
- Performance Troubleshooting: Identify weaknesses in a particular LLM by running targeted evaluations.
Key capabilities
- Defines evaluation criteria.
- Runs tests against different LLMs.
- Provides structured feedback on model performance.
- Facilitates comparison of models across tasks.
Example prompts
- "Evaluate GPT-4 and Claude 3 Opus on summarizing news articles."
- "Run the 'creative writing' benchmark against Llama 3 70B."
- "Compare Gemini 1.5 Pro and Mistral Large on answering questions about historical events."
Tips & gotchas
The skill requires a clear definition of evaluation criteria for meaningful results. Ensure you have access to the LLMs you intend to evaluate, as this skill acts as an evaluator, not a provider of models themselves.