[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6DPZ0M6H215ESX2547HDDR8":3,"skill-same:01M6DPZ0M6H215ESX2547HDDR8":81},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":26,"publishedAt":44,"linkOnly":45,"markdownContent":46,"markdownBody":47,"frontMatter":48,"refName":58,"commitSha":59,"skillDirectory":60,"forksCount":61,"repositoryHtmlUrl":62,"repositoryHomepageUrl":6,"ownerHtmlUrl":63,"ownerBlogUrl":64,"ownerDescription":65,"updatedAt":66,"install":67,"resources":80},"01M6DPZ0M6H215ESX2547HDDR8","translate-book",null,"Translate books (PDF\u002FDOCX\u002FEPUB) into any language using parallel sub-agents. Converts input -> Markdown chunks -> translated chunks -> HTML\u002FDOCX\u002FEPUB\u002FPDF.","spideynolove\u002Fclaude-dotfiles","spideynolove","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F19179621?v=4","01M6DPV14K2QND6S7BX6HNQYX9","Hung Nguyen","01M6DPV1MMAV9858D8R8CHM5QM",2,"MIT","https:\u002F\u002Fgithub.com\u002Fspideynolove\u002Fclaude-dotfiles\u002Fblob\u002Fmain\u002F.claude-global-backup\u002Fskills\u002Ftranslate-book\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000002","文档处理","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"translate book","translate","python3","input","language","html","edit","write","read","grep","docx","parallel","convert","epub","markdown","Bash","PDF","2026-11-10 10:12:30",false,"---\nname: translate-book\ndescription: Translate books (PDF\u002FDOCX\u002FEPUB) into any language using parallel sub-agents. Converts input -> Markdown chunks -> translated chunks -> HTML\u002FDOCX\u002FEPUB\u002FPDF.\nallowed-tools: Read, Write, Edit, Bash, Glob, Grep, Agent, AskUserQuestion\nmetadata: {\"openclaw\":{\"requires\":{\"bins\":[\"python3\",\"pandoc\",\"ebook-convert\"],\"anyBins\":[\"calibre\",\"ebook-convert\"]}}}\n---\n\n# Book Translation Skill\n\nYou are a book translation assistant. You translate entire books from one language to another by orchestrating a multi-step pipeline.\n\n## Workflow\n\n### 1. Collect Parameters\n\nDetermine the following from the user's message:\n- **file_path**: Path to the input file (PDF, DOCX, or EPUB) — REQUIRED\n- **target_lang**: Target language code (default: `zh`) — e.g. zh, en, ja, ko, fr, de, es\n- **concurrency**: Number of parallel sub-agents per batch (default: `8`)\n- **custom_instructions**: Any additional translation instructions from the user (optional)\n\nIf the file path is not provided, ask the user.\n\n### 2. Preprocess — Convert to Markdown Chunks\n\nRun the conversion script to produce chunks:\n\n```bash\npython3 {baseDir}\u002Fscripts\u002Fconvert.py \"\u003Cfile_path>\" --olang \"\u003Ctarget_lang>\"\n```\n\nThis creates a `{filename}_temp\u002F` directory containing:\n- `input.html`, `input.md` — intermediate files\n- `chunk0001.md`, `chunk0002.md`, ... — source chunks for translation\n- `manifest.json` — chunk manifest for tracking and validation\n- `config.txt` — pipeline configuration with metadata\n\n### 3. Discover Chunks\n\nUse Glob to find all source chunks and determine which still need translation:\n\n```\nGlob: {filename}_temp\u002Fchunk*.md\nGlob: {filename}_temp\u002Foutput_chunk*.md\n```\n\nCalculate the set of chunks that have a source file but no corresponding `output_` file. These are the chunks to translate.\n\nIf all chunks already have translations, skip to step 5.\n\n### 4. Parallel Translation with Sub-Agents\n\n**Each chunk gets its own independent sub-agent** (1 chunk = 1 sub-agent = 1 fresh context). This prevents context accumulation and output truncation.\n\nLaunch chunks in batches to respect API rate limits:\n- Each batch: up to `concurrency` sub-agents in parallel (default: 8)\n- Wait for the current batch to complete before launching the next\n\n**Spawn each sub-agent with the following task.** Use whatever sub-agent\u002Fbackground-agent mechanism your runtime provides (e.g. the Agent tool, sessions_spawn, or equivalent).\n\nThe output file is `output_` prefixed to the source filename: `chunk0001.md` → `output_chunk0001.md`.\n\n> Translate the file `\u003Ctemp_dir>\u002Fchunk\u003CNNNN>.md` to {TARGET_LANGUAGE} and write the result to `\u003Ctemp_dir>\u002Foutput_chunk\u003CNNNN>.md`. Follow the translation rules below. Output only the translated content — no commentary.\n\nEach sub-agent receives:\n- The single chunk file it is responsible for\n- The temp directory path\n- The target language\n- The translation prompt (see below)\n- Any custom instructions\n\n**Each sub-agent's task**:\n1. Read the source chunk file (e.g. `chunk0001.md`)\n2. Translate the content following the translation rules below\n3. Write the translated content to `output_chunk0001.md`\n\n**IMPORTANT**: Each sub-agent translates exactly ONE chunk and writes the result directly to the output file. No START\u002FEND markers needed.\n\n#### Translation Prompt for Sub-Agents\n\nInclude this translation prompt in each sub-agent's instructions (replace `{TARGET_LANGUAGE}` with the actual language name, e.g. \"Chinese\"):\n\n---\n\n请翻译markdown文件为 {TARGET_LANGUAGE}.\nIMPORTANT REQUIREMENTS:\n1. 严格保持 Markdown 格式不变，包括标题、链接、图片引用等\n2. 仅翻译文字内容，保留所有 Markdown 语法和文件名\n3. 删除页码、空链接、不必要的字符和如: 行末的'\\\\'\n4. 删除只有数字的行，那可能是页码\n5. 保证格式和语义准确翻译内容自然流畅\n6. 只输出翻译后的正文内容，不要有任何说明、提示、注释或对话内容。\n7. 表达清晰简洁，不要使用复杂的句式。请严格按顺序翻译，不要跳过任何内容。\n8. 必须保留所有图片引用，包括：\n   - 所有 ![alt](path) 格式的图片引用必须完整保留\n   - 图片文件名和路径不要修改（如 media\u002Fimage-001.png）\n   - 图片alt文本可以翻译，但必须保留图片引用结构\n   - 不要删除、过滤或忽略任何图片相关内容\n   - 图片引用示例：![Figure 1: Data Flow](media\u002Fimage-001.png) -> ![图1：数据流](media\u002Fimage-001.png)\n9. 智能识别和处理多级标题，按照以下规则添加markdown标记：\n   - 主标题（书名、章节名等）使用 # 标记\n   - 一级标题（大节标题）使用 ## 标记\n   - 二级标题（小节标题）使用 ### 标记\n   - 三级标题（子标题）使用 #### 标记\n   - 四级及以下标题使用 ##### 标记\n10. 标题识别规则：\n    - 独立成行的较短文本（通常少于50字符）\n    - 具有总结性或概括性的语句\n    - 在文档结构中起到分隔和组织作用的文本\n    - 字体大小明显不同或有特殊格式的文本\n    - 数字编号开头的章节文本（如 \"1.1 概述\"、\"第三章\"等）\n11. 标题层级判断：\n    - 根据上下文和内容重要性判断标题层级\n    - 章节类标题通常为高层级（# 或 ##）\n    - 小节、子节标题依次降级（### #### #####）\n    - 保持同一文档内标题层级的一致性\n12. 注意事项：\n    - 不要过度添加标题标记，只对真正的标题文本添加\n    - 正文段落不要添加标题标记\n    - 如果原文已有markdown标题标记，保持其层级结构\n13. {CUSTOM_INSTRUCTIONS if provided}\n\nmarkdown文件正文:\n\n---\n\n### 5. Verify Completeness and Retry\n\nAfter all batches complete, use Glob to check that every source chunk has a corresponding output file.\n\nIf any are missing, retry them — each missing chunk as its own sub-agent. Maximum 2 attempts per chunk (initial + 1 retry).\n\nAlso read `manifest.json` and verify:\n- Every chunk id has a corresponding output file\n- No output file is empty (0 bytes)\n\nReport any chunks that failed after retry.\n\n### 6. Translate Book Title\n\nRead `config.txt` from the temp directory to get the `original_title` field.\n\nTranslate the title to the target language. For Chinese, wrap in 书名号: `《translated_title》`.\n\n### 7. Post-process — Merge and Build\n\nRun the build script with the translated title:\n\n```bash\npython3 {baseDir}\u002Fscripts\u002Fmerge_and_build.py --temp-dir \"\u003Ctemp_dir>\" --title \"\u003Ctranslated_title>\" --cleanup\n```\n\nThe `--cleanup` flag removes intermediate files (chunks, input.html, etc.) after a fully successful build. If the user asked to keep intermediates, omit `--cleanup`.\n\nThe script reads `output_lang` from `config.txt` automatically. Optional overrides: `--lang`, `--author`.\n\nThis produces in the temp directory:\n- `output.md` — merged translated markdown\n- `book.html` — web version with floating TOC\n- `book_doc.html` — ebook version\n- `book.docx`, `book.epub`, `book.pdf` — format conversions (requires Calibre)\n\n### 8. Report Results\n\nTell the user:\n- Where the output files are located\n- How many chunks were translated\n- The translated title\n- List generated output files with sizes\n- Any format generation failures\n","# Book Translation Skill\n\nYou are a book translation assistant. You translate entire books from one language to another by orchestrating a multi-step pipeline.\n\n## Workflow\n\n### 1. Collect Parameters\n\nDetermine the following from the user's message:\n- **file_path**: Path to the input file (PDF, DOCX, or EPUB) — REQUIRED\n- **target_lang**: Target language code (default: `zh`) — e.g. zh, en, ja, ko, fr, de, es\n- **concurrency**: Number of parallel sub-agents per batch (default: `8`)\n- **custom_instructions**: Any additional translation instructions from the user (optional)\n\nIf the file path is not provided, ask the user.\n\n### 2. Preprocess — Convert to Markdown Chunks\n\nRun the conversion script to produce chunks:\n\n```bash\npython3 {baseDir}\u002Fscripts\u002Fconvert.py \"\u003Cfile_path>\" --olang \"\u003Ctarget_lang>\"\n```\n\nThis creates a `{filename}_temp\u002F` directory containing:\n- `input.html`, `input.md` — intermediate files\n- `chunk0001.md`, `chunk0002.md`, ... — source chunks for translation\n- `manifest.json` — chunk manifest for tracking and validation\n- `config.txt` — pipeline configuration with metadata\n\n### 3. Discover Chunks\n\nUse Glob to find all source chunks and determine which still need translation:\n\n```\nGlob: {filename}_temp\u002Fchunk*.md\nGlob: {filename}_temp\u002Foutput_chunk*.md\n```\n\nCalculate the set of chunks that have a source file but no corresponding `output_` file. These are the chunks to translate.\n\nIf all chunks already have translations, skip to step 5.\n\n### 4. Parallel Translation with Sub-Agents\n\n**Each chunk gets its own independent sub-agent** (1 chunk = 1 sub-agent = 1 fresh context). This prevents context accumulation and output truncation.\n\nLaunch chunks in batches to respect API rate limits:\n- Each batch: up to `concurrency` sub-agents in parallel (default: 8)\n- Wait for the current batch to complete before launching the next\n\n**Spawn each sub-agent with the following task.** Use whatever sub-agent\u002Fbackground-agent mechanism your runtime provides (e.g. the Agent tool, sessions_spawn, or equivalent).\n\nThe output file is `output_` prefixed to the source filename: `chunk0001.md` → `output_chunk0001.md`.\n\n> Translate the file `\u003Ctemp_dir>\u002Fchunk\u003CNNNN>.md` to {TARGET_LANGUAGE} and write the result to `\u003Ctemp_dir>\u002Foutput_chunk\u003CNNNN>.md`. Follow the translation rules below. Output only the translated content — no commentary.\n\nEach sub-agent receives:\n- The single chunk file it is responsible for\n- The temp directory path\n- The target language\n- The translation prompt (see below)\n- Any custom instructions\n\n**Each sub-agent's task**:\n1. Read the source chunk file (e.g. `chunk0001.md`)\n2. Translate the content following the translation rules below\n3. Write the translated content to `output_chunk0001.md`\n\n**IMPORTANT**: Each sub-agent translates exactly ONE chunk and writes the result directly to the output file. No START\u002FEND markers needed.\n\n#### Translation Prompt for Sub-Agents\n\nInclude this translation prompt in each sub-agent's instructions (replace `{TARGET_LANGUAGE}` with the actual language name, e.g. \"Chinese\"):\n\n---\n\n请翻译markdown文件为 {TARGET_LANGUAGE}.\nIMPORTANT REQUIREMENTS:\n1. 严格保持 Markdown 格式不变，包括标题、链接、图片引用等\n2. 仅翻译文字内容，保留所有 Markdown 语法和文件名\n3. 删除页码、空链接、不必要的字符和如: 行末的'\\\\'\n4. 删除只有数字的行，那可能是页码\n5. 保证格式和语义准确翻译内容自然流畅\n6. 只输出翻译后的正文内容，不要有任何说明、提示、注释或对话内容。\n7. 表达清晰简洁，不要使用复杂的句式。请严格按顺序翻译，不要跳过任何内容。\n8. 必须保留所有图片引用，包括：\n   - 所有 ![alt](path) 格式的图片引用必须完整保留\n   - 图片文件名和路径不要修改（如 media\u002Fimage-001.png）\n   - 图片alt文本可以翻译，但必须保留图片引用结构\n   - 不要删除、过滤或忽略任何图片相关内容\n   - 图片引用示例：![Figure 1: Data Flow](media\u002Fimage-001.png) -> ![图1：数据流](media\u002Fimage-001.png)\n9. 智能识别和处理多级标题，按照以下规则添加markdown标记：\n   - 主标题（书名、章节名等）使用 # 标记\n   - 一级标题（大节标题）使用 ## 标记\n   - 二级标题（小节标题）使用 ### 标记\n   - 三级标题（子标题）使用 #### 标记\n   - 四级及以下标题使用 ##### 标记\n10. 标题识别规则：\n    - 独立成行的较短文本（通常少于50字符）\n    - 具有总结性或概括性的语句\n    - 在文档结构中起到分隔和组织作用的文本\n    - 字体大小明显不同或有特殊格式的文本\n    - 数字编号开头的章节文本（如 \"1.1 概述\"、\"第三章\"等）\n11. 标题层级判断：\n    - 根据上下文和内容重要性判断标题层级\n    - 章节类标题通常为高层级（# 或 ##）\n    - 小节、子节标题依次降级（### #### #####）\n    - 保持同一文档内标题层级的一致性\n12. 注意事项：\n    - 不要过度添加标题标记，只对真正的标题文本添加\n    - 正文段落不要添加标题标记\n    - 如果原文已有markdown标题标记，保持其层级结构\n13. {CUSTOM_INSTRUCTIONS if provided}\n\nmarkdown文件正文:\n\n---\n\n### 5. Verify Completeness and Retry\n\nAfter all batches complete, use Glob to check that every source chunk has a corresponding output file.\n\nIf any are missing, retry them — each missing chunk as its own sub-agent. Maximum 2 attempts per chunk (initial + 1 retry).\n\nAlso read `manifest.json` and verify:\n- Every chunk id has a corresponding output file\n- No output file is empty (0 bytes)\n\nReport any chunks that failed after retry.\n\n### 6. Translate Book Title\n\nRead `config.txt` from the temp directory to get the `original_title` field.\n\nTranslate the title to the target language. For Chinese, wrap in 书名号: `《translated_title》`.\n\n### 7. Post-process — Merge and Build\n\nRun the build script with the translated title:\n\n```bash\npython3 {baseDir}\u002Fscripts\u002Fmerge_and_build.py --temp-dir \"\u003Ctemp_dir>\" --title \"\u003Ctranslated_title>\" --cleanup\n```\n\nThe `--cleanup` flag removes intermediate files (chunks, input.html, etc.) after a fully successful build. If the user asked to keep intermediates, omit `--cleanup`.\n\nThe script reads `output_lang` from `config.txt` automatically. Optional overrides: `--lang`, `--author`.\n\nThis produces in the temp directory:\n- `output.md` — merged translated markdown\n- `book.html` — web version with floating TOC\n- `book_doc.html` — ebook version\n- `book.docx`, `book.epub`, `book.pdf` — format conversions (requires Calibre)\n\n### 8. Report Results\n\nTell the user:\n- Where the output files are located\n- How many chunks were translated\n- The translated title\n- List generated output files with sizes\n- Any format generation failures\n",{"name":5,"metadata":49,"description":7,"allowed-tools":57},{"openclaw":50},{"requires":51},{"bins":52,"anyBins":55},[29,53,54],"pandoc","ebook-convert",[56,54],"calibre","Read, Write, Edit, Bash, Glob, Grep, Agent, AskUserQuestion","main","be1d5a0a3cc6cb822d2451790132cda53e497525",".claude-global-backup\u002Fskills\u002Ftranslate-book",3,"https:\u002F\u002Fgithub.com\u002Fspideynolove\u002Fclaude-dotfiles","https:\u002F\u002Fgithub.com\u002Fspideynolove","https:\u002F\u002Fspideynolove.github.io","Data Engineer","2026-05-28 09:03:32",{"source":8,"skillName":5,"commands":68,"prompt":78,"zipAvailable":79},[69,72,75],{"tool":70,"command":71},"npx","npx skills add spideynolove\u002Fclaude-dotfiles --skill translate-book",{"tool":73,"command":74},"bunx","bunx skills add spideynolove\u002Fclaude-dotfiles --skill translate-book",{"tool":76,"command":77},"pnpm","pnpm dlx skills add spideynolove\u002Fclaude-dotfiles --skill translate-book","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fspideynolove\u002Fclaude-dotfiles\u002Fblob\u002Fmain\u002F.claude-global-backup\u002Fskills\u002Ftranslate-book\u002FSKILL.md",true,[],[82,100,113,131,146,161],{"skillNo":83,"name":84,"nameZh":6,"description":85,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":86,"categories":87,"keywords":88,"publishedAt":44},"01M6DQ1GDQVE5XE06RAQ408JGJ","xia","Borrow and adapt patterns from GitHub projects into the current codebase using a structured 6-phase protocol. Invoke with \u002Fxia \u003Cgithub-repo> [focus].","https:\u002F\u002Fgithub.com\u002Fspideynolove\u002Fclaude-dotfiles\u002Fblob\u002Fmain\u002F.opencode-global\u002Fskills\u002Fxia\u002FSKILL.md",[],[84,89,90,91,92,93,94,95,96,97,98,99],"github","code","protocol","projects","repo","github projects","patterns","claude-code","focus","adapt","structured",{"skillNo":101,"name":102,"nameZh":6,"description":103,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":104,"categories":105,"keywords":106,"publishedAt":44},"01M6DQ1EDYQW512EN2RFFBF4NX","x2md","Convert an X (Twitter) post URL to markdown. Use when user wants to read, summarize, archive, or analyze a tweet or X post.","https:\u002F\u002Fgithub.com\u002Fspideynolove\u002Fclaude-dotfiles\u002Fblob\u002Fmain\u002F.opencode-global\u002Fskills\u002Fx2md\u002FSKILL.md",[],[102,96,107,90,41,108,39,109,110,35,111,112],"summarize","archive","analyze","twitter","user","use",{"skillNo":114,"name":115,"nameZh":6,"description":116,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":117,"categories":118,"keywords":119,"publishedAt":44},"01M6DQ1BNAMJ6QGCBA17NJHZX4","sequential-thinking","Structure cognitive analysis using sequential reasoning chains. Use for codebase analysis, pattern extraction, complex problem decomposition, and building mental models before implementation.","https:\u002F\u002Fgithub.com\u002Fspideynolove\u002Fclaude-dotfiles\u002Fblob\u002Fmain\u002F.opencode-global\u002Fskills\u002Fsequential-thinking\u002FSKILL.md",[],[120,121,122,123,124,96,90,125,126,127,112,128,129,130],"sequential thinking","thinking","pattern","decomposition","problem","reasoning","extraction","analysis","implementation","models","structure",{"skillNo":132,"name":133,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":134,"categories":135,"keywords":142,"publishedAt":44},"01M6DQ17SBGVC6PQPZZTSZ49YV","rlm-workflow","https:\u002F\u002Fgithub.com\u002Fspideynolove\u002Fclaude-dotfiles\u002Fblob\u002Fmain\u002F.opencode-global\u002Fskills\u002Frlm-workflow\u002FSKILL.md",[136,137],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":138,"categoryName":139,"taxonomyType":140,"taxonomyTypeDesc":141},"01JBSEEDCAT000000000000037","律师与法务","PROFESSION","适用职业",[143,144,145],"rlm workflow","rlm","workflow",{"skillNo":147,"name":148,"nameZh":6,"description":149,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":150,"categories":151,"keywords":153,"publishedAt":44},"01M6DQ14RM3H655QWFXREBGC7W","repomix","Pack local or remote repos into a single context blob for whole-repository understanding. Use before tasks spanning multiple directories, architecture reviews, or onboarding to an unfamiliar codebase.","https:\u002F\u002Fgithub.com\u002Fspideynolove\u002Fclaude-dotfiles\u002Fblob\u002Fmain\u002F.opencode-global\u002Fskills\u002Frepomix\u002FSKILL.md",[152],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[148,154,155,156,157,90,158,159,160,112,96],"local","architecture","repos","tasks","onboarding","repository","context",{"skillNo":162,"name":163,"nameZh":6,"description":164,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":165,"categories":166,"keywords":167,"publishedAt":44},"01M6DQ12RFVYCT1YF836D5GQBA","reddit2md","Convert a Reddit post URL to markdown. Use when user wants to read, summarize, archive, or analyze a Reddit post.","https:\u002F\u002Fgithub.com\u002Fspideynolove\u002Fclaude-dotfiles\u002Fblob\u002Fmain\u002F.opencode-global\u002Fskills\u002Freddit2md\u002FSKILL.md",[],[163,41,90,168,96,112,111,169,35,109,39,107,108],"reddit post","reddit"]