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Found 4 Skills
Design comprehensive test cases using PICT (Pairwise Independent Combinatorial Testing) for any piece of requirements or code. Analyzes inputs, generates PICT models with parameters, values, and constraints for valid scenarios using pairwise testing. Outputs the PICT model, markdown table of test cases, and expected results.
Combinatorial testing with a greedy pairwise matrix generator. Covers all factor pairs in near-minimal test cases.
Systematic test design with boundary value analysis, equivalence partitioning, decision tables, state transition testing, and combinatorial testing. Use when designing comprehensive test cases, reducing redundant tests, or ensuring systematic coverage.
Compare AG2 agents, models, or prompts to decide which is better. run_variants scores several named agents on one suite and ranks them on a leaderboard (Variants holds a mapping of named Agent instances plus an axis label). run_pairwise with pairwise_judge does head-to-head LLM comparison using a dual-order position swap (a win counts only if it survives the swap, else a tie), reporting win-rate with a Wilson 95% CI, wins, losses, ties, flips, and agreement (Cohen's kappa). human_pairwise collects a person's blinded vote inline, or via an exported manifest with export_pairwise_cases and human_labels. Use when the user wants to A/B test prompts or models, run a leaderboard, pick a winner, judge head-to-head, measure win-rate, or collect human preference labels. For running and grading a single agent, see ag2-evaluation.