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pypipkg:pypi/python-levenshtein@0.27.x
tile.json

tessl/pypi-python-levenshtein

tessl install tessl/pypi-python-levenshtein@0.27.0

Python compatibility wrapper for computing string edit distances and similarities using fast Levenshtein algorithms.

Agent Success

Agent success rate when using this tile

88%

Improvement

Agent success rate improvement when using this tile compared to baseline

1.38x

Baseline

Agent success rate without this tile

64%

rubric.jsonevals/scenario-7/

{
  "context": "This evaluation assesses how well the engineer uses the Levenshtein package's matching_blocks function to identify contiguous common subsequences between strings. The focus is on proper usage of this specific package capability rather than implementing custom algorithms.",
  "type": "weighted_checklist",
  "checklist": [
    {
      "name": "Uses matching_blocks",
      "description": "Code imports and correctly calls Levenshtein.matching_blocks() function to find common blocks between the two input strings",
      "max_score": 50
    },
    {
      "name": "Correct function signature",
      "description": "The find_common_blocks function accepts two string parameters (text1, text2) and returns a list as specified in the API",
      "max_score": 10
    },
    {
      "name": "Returns proper format",
      "description": "Function returns a list of tuples where each tuple contains (start_position_in_text1, start_position_in_text2, block_length) matching the expected output format",
      "max_score": 15
    },
    {
      "name": "Handles empty matches",
      "description": "Solution correctly handles cases where there are no common blocks between strings (returns empty list or appropriate representation)",
      "max_score": 10
    },
    {
      "name": "No custom implementation",
      "description": "Code relies on Levenshtein.matching_blocks() rather than implementing a custom algorithm for finding matching blocks",
      "max_score": 15
    }
  ]
}