القائمة
Read a PDF directly with vision and extract text, summarize, or analyze its structure. Use when the user passes a PDF file.
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
استنادا إلى تصنيف SOC المهني
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/skrun-dev/skrun --skill pdf-processingيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
عرض SKILL.md
Turn a CSV of operational data (sales, usage, signups, support tickets) into a multi-page styled PDF executive report with narrative + matplotlib charts. The LLM analyzes the data, picks what's interesting, writes the prose, and emits a structured render request that becomes a polished PDF. Use when given a CSV and asked for a report, summary, or analysis.
Turn a folder of Markdown notes (Obsidian vault, Notion export, plain repo docs) into a navigable static HTML knowledge base bundled as a single .zip file. Maintains a persistent concept graph across runs — concepts that appear in multiple runs gain prominence, and the index becomes denser over time. Use when given a Markdown vault and asked to publish, share, or render it as a browsable site.
Listen to a meeting recording and extract structured action items, decisions, and open questions. Maintains a persistent ledger across runs — previously-open actions are auto-resolved when mentioned as done in subsequent meetings. Outputs `actions.csv` (importable to Linear/Asana/Notion) + `recap.md` (paste into Slack). Use when given a meeting recording and asked for a recap or action items.
| name | pdf-processing |
| description | Read a PDF directly with vision and extract text, summarize, or analyze its structure. Use when the user passes a PDF file. |
You are a PDF processing assistant. The user passes you a PDF file and a task. You read the PDF directly using your native document capability — no extraction tools, no upstream OCR.
task field:
extract → return the readable text content of the PDF, preserving paragraph and section structure as best you can.summarize → return a single concise paragraph (3-5 sentences) covering the document's purpose and main points.analyze → return a short structural analysis: list the key topics, sections, and any tables/figures detected.pages.Return a JSON object with:
result: the string for the requested task (extracted text, summary, or analysis).pages: integer number of pages.result: "Could not read PDF" and pages: 0.