skillcheck Tool Gets Scorer Fixes, Cleaner Error Handling, and Accurate Token Data

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skillcheck, a static analyzer for SKILL.md files used by AI coding agents like Claude Code and GitHub Copilot, has received a stability and accuracy update. The description scoring system was repaired, raising the median score across its reference corpus from 75 to 90, with the updated scorer now better distinguishing well-written descriptions from filler content. Corrupt or non-UTF-8 configuration files, which previously caused unhandled Python tracebacks, now produce clear error messages with file names and byte offsets. Token estimation accuracy has also been formally benchmarked for the first time, revealing the offline heuristic consistently over-estimates by roughly 20–30%, with users near budget limits advised to install the optional tiktoken dependency. Internal improvements include consolidated flag-conflict logic, expanded golden-file tests, and a raised code coverage floor from 75% to 80%.

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