--- slug: "pdf-translate" source_type: "skill_md" source_url: "https://cdn.jsdelivr.net/gh/L27z18328742/pdf-translate@main/SKILL.md" repo: "https://github.com/L27z18328742/pdf-translate" source_file: "SKILL.md" branch: "main" --- --- name: pdf-translate description: Translate English PDF documents (especially academic papers) into Chinese using PDFMathTranslate-next (pdf2zh), producing a monolingual Chinese PDF and a bilingual side-by-side PDF with formulas, figures, and layout preserved. Automatically installs the pdf2zh_next environment via uv and reuses the model/API the user already configured in Codex or Claude Code (any OpenAI-compatible gateway), falling back to the free SiliconFlow service when no key is present. Use this skill whenever the user wants to translate a PDF to Chinese (or another language), translate a paper/论文, produce a bilingual PDF, or mentions pdf2zh / PDFMathTranslate — even if they don't name the tool explicitly. Proactively invoke this skill (do not translate manually) whenever a PDF translation task appears. --- # PDF Translate (English → Chinese) This skill translates PDF documents using **PDFMathTranslate-next** (`pdf2zh_next`), the BabelDOC-based translator that preserves formulas, charts, table of contents, and page layout. It produces two files per input: - `*-mono.pdf` — monolingual translated PDF (Chinese only) - `*-dual.pdf` — bilingual PDF (original + translation, side by side or alternating) ## Why this skill exists Translating an academic PDF well requires preserving math, figures, and layout — something plain LLM translation can't do. `pdf2zh_next` handles that, but it has a non-trivial install (Python 3.12, model assets) and many engine options. This skill automates the environment setup and picks a translation engine that reuses credentials the user already has, so translation "just works" without manual configuration. ## Workflow Run the setup script first (installs `pdf2zh_next` if missing, verifies it), then the translate script (detects the best engine and runs the translation). Both scripts are idempotent. **Subsequent runs are fast.** Once `pdf2zh_next` is installed and the model assets are downloaded (after the first successful translation), setup.sh becomes a silent sub-second no-op — it detects the install and the `~/.cache/babeldoc/models` cache and returns immediately without spawning a Python import or re-downloading anything. Re-running it on every invocation is cheap and safe; you do not need to track whether setup has happened before. ### Step 1 — Ensure the environment is ready ```bash bash ~/.claude/skills/pdf-translate/scripts/setup.sh ``` This installs `pdf2zh_next` via `uv tool install --python 3.12 pdf2zh-next` (the project requires Python ≥3.10,<3.14; 3.12 is the documented recommendation) and confirms it is on PATH. It is fast and safe to re-run. The first real translation downloads layout/translation model assets (DocLayout-YOLO + fonts, ~hundreds of MB) into `~/.cache/babeldoc/` — tell the user the first run is slow because of this one-time download. The setup and translate scripts detect this cache and skip the "slow first run" messaging once the models are present. ### Step 2 — Translate ```bash bash ~/.claude/skills/pdf-translate/scripts/translate.sh "" [options] ``` The script auto-detects a translation engine: 1. **ClaudeCode (default when `claude` CLI is available)** — detects if the user is running from Claude Code by checking for the `claude` CLI on PATH. Uses the exact model Claude Code is configured to use (resolved via `ANTHROPIC_DEFAULT_SONNET_MODEL`, etc.). This is the default when working in Claude Code, so the translation uses the same model ecosystem the user is already in. Note: it spawns one `claude -p` subprocess per text segment, so it is slower than the OpenAI engine. 2. **OpenAI-compatible (fallback when API key exists)** — used when `claude` CLI is not found but `OPENAI_API_KEY` is set. Reuses the model configured for Codex: reads `base_url` + `model` from `~/.codex/config.toml` and the API key from `OPENAI_API_KEY`. Fast (HTTP, high concurrency), ideal for full papers. 3. **SiliconFlowFree (zero-config fallback)** — used when neither `claude` CLI nor API key is detectable. The project's free GLM-4-9B service; needs no key. Note: file content is forwarded through the project maintainer's server. Override detection with flags: `--engine openai|siliconflowfree|claudecode`, `--model `, `--base-url `, `--api-key `. Common options: - `--output ` — output directory (default: an `output/` folder next to the input PDF) - `--pages "1-5,8"` — translate only some pages - `--lang-in en --lang-out zh-CN` — language codes (defaults: en → zh-CN) - `--qps N --pool-max-workers N` — concurrency tuning (see `references/advanced.md`) - `--no-dual` / `--no-mono` — suppress one of the two outputs - Pass any other `pdf2zh_next` flag through with `-- ` ### Step 3 — Report results After the script finishes, it prints the paths of the generated `*-mono.pdf` and `*-dual.pdf`. Tell the user where these files are (relative to the input), open/preview them if asked, and note the engine + model that was used. If the user wants different quality or speed, suggest overrides (see `references/advanced.md`). ## Default behavior choices (and how to change them) - **Source/target language**: English → Simplified Chinese (`en` → `zh-CN`). Override with `--lang-in` / `--lang-out`. Full language code list is in `references/advanced.md`. - **Engine**: ClaudeCode when `claude` CLI is on PATH (default in Claude Code), else OpenAI-compatible when a key is found, else SiliconFlowFree. Use `--engine openai` to force the OpenAI engine even from Claude Code. The claudecode engine reuses Claude Code's configured model; the OpenAI engine reuses the model named in `~/.codex/config.toml`. - **Model**: for the openai engine, the model from `~/.codex/config.toml`; for claudecode, `sonnet` (resolved by Claude Code to the user's configured model). Reasoning models (e.g. `gpt-5.5`, `glm-5.2`) work but are slower and use more tokens; for large papers consider a lighter chat model via `--model`. - **Concurrency**: modest defaults (`--qps 10 --pool-max-workers 30` for the OpenAI engine; `--qps 4 --pool-max-workers 8` for claudecode since each segment is a subprocess). Increase for faster translation if the gateway allows it. ## When something goes wrong - **`command not found: pdf2zh_next`** after install — the uv tool bin dir isn't on PATH. The scripts add `~/.local/bin` to PATH themselves, but if the user runs `pdf2zh_next` directly in their own shell, they need `export PATH="$HOME/.local/bin:$PATH"` (Linux/macOS). - **First translation hangs on "loading assets" / downloading** — normal one-time model download. Wait it out. - **429 / rate-limit errors** — lower `--qps` and `--pool-max-workers`. - **Gateway returns errors for the OpenAI engine** — the detected base_url may only expose the responses API, not chat-completions. Run with `--engine siliconflowfree` to bypass, or point `--base-url` at a chat-completions endpoint. - **Out-of-memory / very large PDFs** — use `--pages` to translate in chunks, or `--max-pages-per-part 50`. For the full option reference, engine configuration details, translating to other languages, glossaries, and offline/air-gapped usage, read `references/advanced.md`.