Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add xuansenpa1/skillrevise --skill image-ocrgit clone --depth 1 https://github.com/xuansenpa1/skillreviseWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/xuansenpa1/skillrevise/image-ocr)<a href="https://agentmods.dev/skills/xuansenpa1/skillrevise/image-ocr"><img src="https://agentmods.dev/badge/skills/xuansenpa1/skillrevise/image-ocr.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00016 | $0.02633 |
| Opus 5 | $0.00008 | $0.01316 |
| Sonnet 5 | $0.00003 | $0.00527 |
| Haiku 4.5 | $0.00002 | $0.00263 |
Grade A, and why
image-ocr scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to image-ocr — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 393 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Image OCR Skill
Purpose
This skill enables accurate text extraction from image files (JPG, PNG, etc.) using Tesseract OCR via the pytesseract Python library. It is suitable for scanned documents, screenshots, photos of text, receipts, forms, and other visual content containing text.
When to Use
- Extracting text from scanned documents or photos
- Reading text from screenshots or image captures
- Processing batch image files that contain textual information
- Converting visual documents to machine-readable text
- Extracting structured data from forms, receipts, or tables in images
Required Libraries
The following Python libraries are required:
import pytesseract
from PIL import Image
import json
import os
Input Requirements
- File formats: JPG, JPEG, PNG, WEBP
- Image quality: Minimum 300 DPI recommended for printed text; clear and legible text
- File size: Under 5MB per image (resize if necessary)
- Text language: Specify if non-English to improve accuracy
Output Schema
All extracted content must be returned as valid JSON conforming to this schema:
{
"success": true,
"filename": "example.jpg",
"extracted_text": "Full raw text extracted from the image...",
"confidence": "high|medium|low",
"metadata": {
"language_detected": "en",
"text_regions": 3,
"has_tables": false,
"has_handwriting": false
},
"warnings": [
"Text partially obscured in bottom-right corner",
"Low contrast detected in header section"
]
}
Field Descriptions
success: Boolean indicating whether text extraction completedfilename: Original image filenameextracted_text: Complete text content in reading order (top-to-bottom, left-to-right)confidence: Overall OCR confidence level based on image quality and text claritymetadata.language_detected: ISO 639-1 language codemetadata.text_regions: Number of distinct text blocks identifiedmetadata.has_tables: Whether tabular data structures were detectedmetadata.has_handwriting: Whether handwritten text was detectedwarnings: Array of quality issues or potential errors
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 393 lines · 16 tokens per session scan A 69dd1065075d
image-ocr is a skill published in the GitHub repository xuansenpa1/skillrevise (55 stars, last pushed 2d ago), licensed MIT. It adds 16 tokens to every session and 2,633 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to image-ocr, differing in 0 lines, and is treated as a copy.
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