Llm Challenge
CheckedProvides LLMs guidance and assistance for building AI and machine learning applications.
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llm-challenge. SKILL.md is marked internal, so the skills CLI hides it.We check each listing against its upstream repository with the rules the skills CLI uses, last on 2026-10-22. We don't show a command we know will fail.
What we know about this skill
Publisher
tailor-platform
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Install target
Live repository
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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.
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Makes no network calls.
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Pinned to a commit: passed
Scanned tailor-platform/sdk at 3feb406ec7.
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- Commit
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- Scanned
- 3 files, 212 lines
About this skill
What it does
The llm-challenge skill provides a way to test and evaluate Large Language Models (LLMs) through structured challenges. It allows users to define specific tasks or scenarios, then assesses the LLM's performance against those criteria. This facilitates benchmarking and identifying areas for improvement in LLM capabilities.
When to use it
- Benchmarking: Compare the performance of different LLMs on a standardized set of challenges.
- Debugging: Identify weaknesses in an LLM’s reasoning or problem-solving abilities by creating targeted test cases.
- Training Data Generation: Generate challenging prompts and expected responses for fine-tuning LLMs.
- Evaluating New Models: Quickly assess the capabilities of newly released LLMs before deploying them in production.
Key capabilities
- Challenge Definition: Allows users to create custom challenges with specific instructions and evaluation criteria.
- LLM Execution: Executes prompts against a specified LLM.
- Performance Assessment: Automatically evaluates LLM responses based on predefined metrics.
- Reporting: Provides detailed reports on the LLM's performance across various challenges.
Example prompts
- "Create a challenge that tests an LLM’s ability to summarize a complex legal document."
- "Run the 'logical reasoning' challenge against the GPT-4 model and report the results."
- "Generate five new challenging math problems for the 'arithmetic reasoning' skill."
Tips & gotchas
The effectiveness of this skill depends on well-defined challenges with clear evaluation criteria. Ensure that your prompts are specific and unambiguous to get meaningful performance assessments from the LLM.