Ocr Benchmark

Multi-model OCR benchmark and comparison tool. Run OCR on images using Claude (Opus/Sonnet/Haiku via Bedrock), Gemini (Pro/Flash via Google AI Studio), and P...

This skill appears to be a legitimate OCR benchmarking tool, but note the following before installing and running: (1) The package metadata omits required env vars — you will need AWS credentials (for Bedrock) and GOOGLE_API_KEY for Gemini, and optionally a PADDLEOCR_ENDPOINT/TOKEN; verify and provide only least-privilege credentials. (2) Running the tool will upload image bytes and extracted text to external services (Anthropic/Bedrock, Google AI Studio, or whatever URL you provide for PaddleOCR). Do not use sensitive/private images unless you trust the destination. (3) Inspect requirements.txt and the two scripts locally before pip installing; consider running in an isolated virtualenv or container. (4) If you don’t want to provide credentials for a provider, use the --auto-skip flag or run only specific models. (5) The metadata in the registry is inconsistent — if you need a fully audited skill record, ask the publisher to correct required-env and README metadata. If you want me to, I can point out the exact lines in the code that send data to each external endpoint and summarize the permissions each provider needs.

Type: OpenClaw Skill Name: ocr-benchmark Version: 2.0.0 The ocr-benchmark skill bundle is a legitimate tool for comparing OCR performance across multiple AI providers (AWS Bedrock, Google Gemini, and PaddleOCR). The scripts (run_benchmark.py and make_report.py) perform standard operations such as reading local image files, making API calls to established providers using environment variables for credentials, and generating PowerPoint reports. There is no evidence of data exfiltration, malicious execution, or prompt injection; the code is well-documented and its behavior aligns strictly with the stated purpose.