Diagnoses PDF résumé skills extraction failures via routin and header analysis.
npx skills add https://github.com/offlinecv/OfflineCV --skill probe-skills
Please help me install this Agent Skill. Skill: probe-skills Source: https://github.com/offlinecv/OfflineCV/tree/main/.claude/skills/probe-skills Command: npx skills add https://github.com/offlinecv/OfflineCV --skill probe-skills
This skill resolves issues where résumé skills sections are missing or incorrectly parsed due to PDF formatting, header recognition failures, or layout complexities.
Run the probe-skills skill by executing the vitest command with the absolute path to the target resume PDF assigned to the RL_SKILLS_PDF environment variable.
High-intent search queries and answers about installing and using this skill.
Missing skills sections during résumé PDF parsing typically result from header recognition failures or layout-based text extraction errors. This diagnostic skill isolates whether the parser missed the header or failed to capture the content beneath it.
Debug résumé PDF parser heuristics by running a vitest environment with the target PDF assigned to the RL_SKILLS_PDF environment variable. This executes parser heuristics against the file and reports rejected headers with detailed failure analysis.
Decorative glyphs or non-standard wording in section headers cause the parser to reject the skills section. This tool identifies these header misrecognition failures and reports them so you can adjust extraction heuristics accordingly.
Yes, a local vitest environment is required to execute parser heuristics against the target résumé PDF. The diagnostic relies on vitest to test section routing logic and text extraction performance for specific files.
Section routing logic fails when the parser misclassifies headers due to formatting complexities or decorative elements, preventing the text extraction layer from capturing the associated content. This skill analyzes routing to pinpoint the exact failure mode.
Yes, you can verify the cause of résumé PDF data loss by running this diagnostic tool. It distinguishes between header misrecognition, where the parser drops the section entirely, and extraction failures, where the header is found but content is lost.
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