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 zhixuli0406/DuDuClaw --skill pdfgit clone --depth 1 https://github.com/zhixuli0406/DuDuClawWrote 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/zhixuli0406/duduclaw/pdf)<a href="https://agentmods.dev/skills/zhixuli0406/duduclaw/pdf"><img src="https://agentmods.dev/badge/skills/zhixuli0406/duduclaw/pdf.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00034 | $0.00996 |
| Opus 5 | $0.00017 | $0.00498 |
| Sonnet 5 | $0.00007 | $0.00199 |
| Haiku 4.5 | $0.00003 | $0.00100 |
Grade A, and why
pdf 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 8d 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.
What it actually says
PDF 文件處理
處理 PDF 的兩件事：讀取抽取與建立。腳本用 uv run 執行，依賴以 PEP 723
inline metadata 宣告（讀取用 pypdf、建立用 reportlab）；uv 不存在時改用
pip install pypdf reportlab 後 python3 執行。
何時使用
- 收到
.pdf附件，需要讀出文字來彙總、擷取、分析。 - 要把整理好的文字/報告直接產成一份 PDF 回傳。
腳本
腳本位於本技能的 scripts/ 目錄。兩種執行路徑，依你有沒有 Bash 工具擇一：
-
有 Bash / shell 工具 → 直接跑
uv run scripts/<script>.py ...（見下方各節）。 -
沒有 Bash / shell 工具（API 模式後端，如 Grok / DeepSeek / MiniMax） → 不要只回文字， 改用
office_scriptMCP 工具在伺服器端跑同一支腳本：skill：pdfscript：create/extract（不含路徑，.py可省略）args：字串陣列，等同uv run後面那串參數；任何路徑須落在你的 agent 目錄或其attachments/。
例（把整理好的文字產成 PDF）：
{"skill": "pdf", "script": "create", "args": ["report.md", "--out", "/你的agent目錄/attachments/report.pdf"]}工具以
uv run（uv 不存在時退回python3）在你的 agent 目錄內執行並回傳腳本 stdout； 產出檔案後務必依下方 📎DELIVER 協定交付。
1. 讀取抽取 — extract.py
uv run scripts/extract.py <input.pdf> --format json # {"pages": ["p1 text", ...]}
uv run scripts/extract.py <input.pdf> --format md # 逐頁文字，以 --- 分隔
2. 建立 — create.py
把 markdown 或純文字產成 PDF（#/## 標題會用較大字級，其餘為內文；CJK 以內建
字型排版）：
來源型別依副檔名判定（.txt → 純文字，其餘 → markdown）：
uv run scripts/create.py report.md --out /abs/out.pdf
uv run scripts/create.py notes.txt --out /abs/out.pdf
若需要把 Word/Excel/PPT 轉成 PDF，請改用對應的
docx/xlsx/pptx技能的to_pdf.py（LibreOffice headless）。
交付檔案給使用者（📎DELIVER 協定）
產出後在回覆最後另起一行：
📎DELIVER:/絕對路徑/report.pdf
路徑須為絕對路徑且位於你的 agent 工作目錄（或其 attachments/）下；標記行不顯示給
使用者，請另用文字說明。
API 模式同樣適用：用 office_script 產出 .pdf 後，一樣在最後一行輸出
📎DELIVER:<絕對路徑>——只回文字不算完成。
What ships with it
2 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.
- 8d ago First seen · 82 lines · 34 tokens per session scan A 6348b51cab90
pdf is a skill published in the GitHub repository zhixuli0406/DuDuClaw (47 stars, last pushed yesterday), licensed Apache-2.0. It adds 34 tokens to every session and 996 once invoked, about $0.0002 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 2026-08-30.
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