Score breakdown
Popularity is tracked separately. Support, ads, sponsorships, and tips never affect these signals.
Why it matters
Document conversion is still a recurring bottleneck for AI ingestion pipelines, especially when the source is a scanned or messy PDF. This matters because it offers a local path that preserves structure instead of stopping at plain text, which is directly useful for retrieval, review, and editorial workflows.
Who should use it
Who should skip it
Skip oomol-lab/pdf-craft if the source link, documentation, or setup requirements do not align with your current workflow or stack.
About this signal
oomol-lab/pdf-craft is tracked by RepoRadar as a code repository in the Document parsing section. It was first seen on 2026-07-19 and last updated on 2026-07-19. The current verdict is 'try now' with a Gold tier and moderate setup difficulty. Across RepoRadar's eight signals, oomol-lab/pdf-craft is strongest on workflow potential (9.1) and maturity (8.8) and weakest on setup ease (6.9) — a profile worth weighing against your own priorities. This page summarizes the evidence RepoRadar captured from https://github.com/oomol-lab/pdf-craft. The score, tier, risk label, and verdict on this page are never influenced by sponsorship, ads, or tips — they reflect only the usefulness, popularity, novelty, momentum, maturity, and evidence signals described in the RepoRadar methodology.
How this item is evaluated
RepoRadar assigned oomol-lab/pdf-craft a composite score of 8.0 out of 10, placing it in the Gold tier. This score combines weighted sub-signals: usefulness (35%), novelty (18%), momentum (14%), maturity (10%), open-source/build quality (7%), evidence quality (6%), workflow potential (6%), and setup ease (4%). Popularity is tracked separately at 100.0 and never affects the composite score or tier. The risk label of 'conditional' reflects inherent user-impacting hazards, not generic novelty. Items with no risk flag may still require normal code review before production use.
Putting this into practice? Read How to evaluate an AI tool before you adopt it for the checklist behind this score.
Risk explanation
The tool processes full documents, so teams should review how extracted text, images, and intermediate files are stored; Optional external LLM use for TOC extraction changes the privacy model compared with the default local OCR path.