What does this skill do, and when should you use it?
This skill supports PDF tasks in which content, layout, and visual presentation all matter. It directs agents to render pages to PNG for visual review and to use reportlab for programmatic PDF generation. pdfplumber or pypdf can handle text extraction and quick checks, but they are not treated as layout-fidelity tools. The skill is part of the deprecated openai/skills repository, whose README points current Codex skill and plugin examples to the OpenAI Plugins repository.
Reads and reviews PDF content; renders pages to PNG with pdftoppm for visual inspection; generates PDFs with reportlab; extracts text and performs quick checks with pdfplumber or pypdf; re-renders after meaningful updates to check alignment, spacing, legibility, tables, images, headers, footers, and page numbering.
- A document reviewer needs to inspect both PDF content and page layout before approval.
- A developer needs to generate consistently formatted PDFs programmatically with reportlab.
- A user needs quick PDF text extraction or validation using pdfplumber or pypdf.
- A publishing workflow needs a final rendering check for clipping, overlap, broken tables, black squares, or unreadable glyphs.
How do you install this skill?
- Dependency installation may perform system-level package installation or network downloads; obtain user confirmation and assess permissions first.
- The skill does not state whether PDF contents leave the local environment and provides no guidance for sensitive documents, cleanup, or recovery.
- The repository README marks the repository as deprecated; current maintenance status, licensing, and future update source require separate confirmation.
- Shell / CLI
- Local filesystem
Poppler/pdftoppmPython reportlabPython pdfplumberPython pypdf
The skill is located at skills/.curated/pdf/SKILL.md. The README documents installation through Codex's skill installer: $skill-installer pdf. Restart Codex after installation so it can load the skill. The repository is deprecated; the README recommends the OpenAI Plugins repository for current Codex skill and plugin examples.
tmp="$(mktemp -d)"
git clone --depth 1 https://github.com/openai/skills.git "$tmp"
mkdir -p ~/.claude/skills
cp -R "$tmp/skills/.curated/pdf" ~/.claude/skills/
rm -rf "$tmp"Generated from the source repository and skill path; it copies only this skill's folder. If the author's install steps above differ, follow those first. To scope it to one project, replace ~/.claude/skills with that project's .claude/skills.
How do you use this skill?
Once installed, send your agent any of these to trigger it:
- Use the PDF skill to review this PDF: render its pages, inspect the layout, then extract the text.
In a skill-enabled Codex environment, provide a concrete PDF request such as: "Use the PDF skill to review this PDF: render its pages, inspect the layout, then extract the text." For PDF creation, request reportlab generation and a rendering check after each meaningful update. The source does not document the exact trigger mechanism.
What are this skill's strengths and limitations?
- Prioritizes visual rendering rather than relying only on extracted text.
- Covers generation, extraction, review, and final validation.
- Names concrete tools including reportlab, pdfplumber, pypdf, and pdftoppm.
- Defines conventions for temporary files and final artifacts.
- The repository is deprecated, so long-term adoption should be assessed against the OpenAI Plugins repository.
- pdfplumber and pypdf do not provide layout-fidelity validation.
- The workflow depends on Poppler and several Python packages, with no versions specified.
- The source provides no test suite, platform verification matrix, or detailed error-recovery procedure.
How does this skill compare with similar options?
Side by side with related skills; every score comes from the same FSRS standard.
| Skill | FS score | Stars | Last updated | License |
|---|---|---|---|---|
| PDF Document Workflow this page ✓ OpenAI · Official | 46 · Use with care | ★ 28k | 4mo ago | — |
| Open Cowork PDF Skill | 69 · Recommended | ★ 2.2k | 12d ago | MIT |
| DeepTutor PDF Skill | 61 · Recommended | ★ 41k | 18d ago | Apache-2.0 |
| Legal Analysis PDF Report Generator | 47 · Use with care | ★ 1.8k | 7mo ago | — |
| NeMo Retriever Document Retrieval Skill ✓ NVIDIA · Official | 41 · Not recommended | ★ 3.6k | 11d ago | Apache-2.0 |
Compared with a workflow based only on pdfplumber or pypdf, this skill adds page rendering with Poppler to validate layout. Compared with a PDF-generation-only workflow, it requires visual review before delivery.
How did FollowSkills review this skill?
The skill scopes work to PDF tasks and specifies temporary and output locations plus dependencies, but it lacks confirmation before installation or external writes, sensitive-PDF handling, data-flow disclosure, rollback guidance, and dependency-integrity verification, so points are deducted.
The workflow is internally coherent and covers rendering, extraction, and final checks, but it contains no committed tests or key-path reproduction evidence. Missing dependencies are largely delegated to the user, and abnormal-input handling and failure diagnostics are thin, so points are deducted.
The intended scenarios—reading, creating, and reviewing PDFs where layout matters—are clear. However, non-fit boundaries, semantic trigger precision, Chinese-language support, and environment-fit evidence are limited; local dependency installation is assumed, so points are deducted.
The document is readable and includes triggers, workflow, dependencies, environment notes, commands, and quality criteria. License metadata is unknown, and versioning, changelog, maintenance ownership, update path, examples, and FAQs are absent; the repository README also states that the repository is deprecated, so points are deducted.
The instructions plausibly support PDF generation, extraction, rendering, and review with directly usable commands. Static evidence provides no representative verified outputs, so output completeness and marginal benefit over manual or alternative workflows remain uncertain; points are deducted.
The supplied files are auditable and contain concrete tools, commands, and quality checks, but there is no test suite, CI coverage, or third-party execution evidence. The assessment therefore relies mainly on documentation claims, so points are deducted.
Open a dimension to read why it scored that way
Evidence confidence：Low — Mostly static review, author material or a limited demo; useful for discovery, not high-risk decisions.
See the full review method →