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 graniet/kheish --skill ocr-and-documentsgit clone --depth 1 https://github.com/graniet/kheishWrote 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/graniet/kheish/ocr-and-documents)<a href="https://agentmods.dev/skills/graniet/kheish/ocr-and-documents"><img src="https://agentmods.dev/badge/skills/graniet/kheish/ocr-and-documents/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/graniet/kheish/ocr-and-documents"><img src="https://agentmods.dev/badge/skills/graniet/kheish/ocr-and-documents.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00049 | $0.01588 |
| Opus 5.5 | $0.00020 | $0.00635 |
| Sonnet 5.5 | $0.00010 | $0.00318 |
| Haiku 4.5 | $0.00005 | $0.00159 |
Grade A, and why
ocr-and-documents 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 1mo 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
84% identical to ocr-and-documents — 41 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 — 192 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
terminal=>bashweb_extract=>web_fetch, plusweb_searchwhen discovery is neededsearch_files=>grep_searchandglob_searchbrowser_*tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from ${KHEISH_SKILL_DIR}.
PDF & Document Extraction
For DOCX: use python-docx (parses actual document structure, far better than OCR).
For PPTX: see the powerpoint skill (uses python-pptx with full slide/notes support).
This skill covers PDFs and scanned documents.
Step 1: Remote URL Available?
If the document has a URL, always try web_extract first:
web_extract(urls=["https://arxiv.org/pdf/2402.03300"])
web_extract(urls=["https://example.com/report.pdf"])
This handles PDF-to-markdown conversion via Firecrawl with no local dependencies.
Only use local extraction when: the file is local, web_extract fails, or you need batch processing.
Step 2: Choose Local Extractor
| Feature | pymupdf (~25MB) | marker-pdf (~3-5GB) |
|---|---|---|
| Text-based PDF | ✅ | ✅ |
| Scanned PDF (OCR) | ❌ | ✅ (90+ languages) |
| Tables | ✅ (basic) | ✅ (high accuracy) |
| Equations / LaTeX | ❌ | ✅ |
| Code blocks | ❌ | ✅ |
| Forms | ❌ | ✅ |
| Headers/footers removal | ❌ | ✅ |
| Reading order detection | ❌ | ✅ |
| Images extraction | ✅ (embedded) | ✅ (with context) |
| Images → text (OCR) | ❌ | ✅ |
| EPUB | ✅ | ✅ |
| Markdown output | ✅ (via pymupdf4llm) | ✅ (native, higher quality) |
| Install size | ~25MB | ~3-5GB (PyTorch + models) |
| Speed | Instant | ~1-14s/page (CPU), ~0.2s/page (GPU) |
Decision: Use pymupdf unless you need OCR, equations, forms, or complex layout analysis.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 192 lines · 49 tokens per session scan A 5048c46629e2
ocr-and-documents is a skill published in the GitHub repository graniet/kheish (217 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 49 tokens to every session and 1,588 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 84% identical to ocr-and-documents, differing in 41 lines, and is treated as a copy.
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