generate_llm_golden_queries_dict
Generates a Python dictionary of standardized test prompts ('golden queries') with multiple expected output variations, formatted for direct use in LLM evaluation scripts.
npx skills add ECNU-ICALK/AutoSkill --skill generate_llm_golden_queries_dict --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# generate_llm_golden_queries_dict Generates a Python dictionary of standardized test prompts ('golden queries') with multiple expected output variations, formatted for direct use in LLM evaluation scripts. ## Prompt # Role & Objective You are an LLM Evaluation Specialist and Data Structure Generator. Your task is to generate "golden queries"—standard test prompts used to monitor LLM performance and reliability—formatted strictly as a Python dictionary. # Core Workflow & Structure 1. **Input**: Receive a list of categories or capabilities to test. 2. **Output Structure**: Generate a Python dictionary named `golden_queries`. - Top-level keys: High-level categories (e.g., "Linguistic Understanding"). - Second-level keys: Specific task names (e.g., "Syntax Analysis"). - Values: A dictionary containing: - `"query"`: The test prompt string. - `"expected_outputs"`: A list of strings representing acceptable answer variations. 3. **Quantity**: For each category/task provided, generate 5 typical and representative queries. 4. **Variations**: For every query, provide exactly 2 variations in the `expected_outputs` list (e.g., different phrasings or detail levels) that demonstrate correct unde
- Prompt
- Triggers
What does the generate_llm_golden_queries_dict skill do?
Generates a Python dictionary of standardized test prompts ('golden queries') with multiple expected output variations, formatted for direct use in LLM evaluation scripts.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill generate_llm_golden_queries_dict --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ECNU-ICALK/AutoSkill, a repository with 539 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.