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 jseook11/codex-pdf-ocr-to-markdown-skill --skill pdf-ocr-to-markdowngit clone --depth 1 https://github.com/jseook11/codex-pdf-ocr-to-markdown-skillWrote 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/jseook11/codex-pdf-ocr-to-markdown-skill/pdf-ocr-to-markdown)<a href="https://agentmods.dev/skills/jseook11/codex-pdf-ocr-to-markdown-skill/pdf-ocr-to-markdown"><img src="https://agentmods.dev/badge/skills/jseook11/codex-pdf-ocr-to-markdown-skill/pdf-ocr-to-markdown/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/jseook11/codex-pdf-ocr-to-markdown-skill/pdf-ocr-to-markdown"><img src="https://agentmods.dev/badge/skills/jseook11/codex-pdf-ocr-to-markdown-skill/pdf-ocr-to-markdown.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 | $0.00050 | $0.04430 |
| Opus 5 | $0.00025 | $0.02215 |
| Sonnet 5 | $0.00010 | $0.00886 |
| Haiku 4.5 | $0.00005 | $0.00443 |
Grade A, and why
pdf-ocr-to-markdown 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 10d 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.
How it starts
The opening of the file, as written. The whole thing — 373 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PDF OCR to Markdown
Use this skill when the user provides PDFs or images and wants Codex to handle OCR, routing, visual-structure analysis, and final Markdown deliverables with internal JSON/quality diagnostics.
This skill does not modify the source PDF, add an OCR layer to the PDF, or create a searchable replacement PDF. "OCR" here means reading visible or embedded page content and writing separate structured outputs such as final.md, final.json, page-level Markdown, page-level JSON, and quality reports.
The default user experience should be: user gives files, Codex runs the bundled pipeline from the user's working directory, Codex decides the route per page, Codex performs the visual pass when it can actually inspect rendered images, and Codex reports the Markdown sidecar path next to each source file. Internal JSON, quality reports, and run artifacts stay under .ocr_work/ so Finder-visible output is limited to the final Markdown sidecar unless the user asks otherwise.
Quick Start
If the user asks Codex to OCR/analyze files, do not make them choose pipeline steps. From the user's current working directory, run the bundled script by absolute path:
python3 /path/to/pdf-ocr-to-markdown/scripts/batch_ocr.py file1.pdf file2.pdf image1.png
Dependencies should be installed once during skill installation with scripts/setup_dependencies.py --install. For Korean/English documents, the default language hint is already kor+eng. Only add options when the user asks for custom output locations, DPI, dependency repair, or work retention. The default internal output root is .ocr_work/ocr_output; the user-facing Markdown sidecar is copied next to the source file.
Dry-run for skill verification:
python scripts/batch_ocr.py \
--file-list examples/example_input.md \
--out-dir /tmp/ocr_output_test \
--work-dir /tmp/ocr_work_test \
--dry-run
Use --keep-work or --no-cleanup internally when pages need a surrounding Codex visual pass, because rendered page images and generated prompt files live under the work directory.
What ships with it
15 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.
- agents/openai.yaml 191 B
- examples/example_input.md 125 B
- examples/expected_output_tree.md 984 B
- examples/sample_text.pdf 663 B
- prompts/full_vision_ocr.md 2.5 KB
- prompts/text_only.md 1.1 KB
- prompts/verify_text_and_visual.md 2.3 KB
- prompts/visual_focus.md 2.7 KB
- scripts/batch_ocr.py 30 KB runs code
- scripts/classify_pages.py 8.0 KB runs code
- scripts/cleanup_workdir.py 4.1 KB runs code
- scripts/extract_pdf_text.py 6.0 KB runs code
- scripts/merge_outputs.py 22 KB runs code
- scripts/render_pages.py 4.5 KB runs code
- scripts/setup_dependencies.py 3.0 KB runs code
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.
- 10d ago First seen · 373 lines · 50 tokens per session scan A 426a2dd93eb9
pdf-ocr-to-markdown is a skill published in the GitHub repository jseook11/codex-pdf-ocr-to-markdown-skill (2 stars, last pushed 4mo ago), licensed MIT. It adds 50 tokens to every session and 4,430 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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