OpenClaw/clawhub-security-signals huggingface dataset:
Skill: bianmaxingkong/japanese-reading-grader (version 2.1.5)

--- name: japanese-reading-grading description: "日语朗读作业批改（双模式）。支持教师批量批改+学生自测打分，自动转写、N5-N1分层评分、发音纠错、成绩汇总、自适应升降级。兼容本地/Canvas环境，鼓励式评分。" --- # 日语朗读作业批改（双模式通用版） 支持：**教师批改模式** / **学生自测模式** 适用：N5/N4/N3/N2/N1 日语学习者 | 音频格式：MP3/MP4/M4A/WAV --- ## 一、工作总流程 Step 1 → 加载标准文本 / 标准音频 Step 2 → 音频预处理 + Whisper medium 转写 Step 3 → 分层评分 + 鼓励式纠错 Step 4 → 生成个人评分报告 Step 5 → 教师模式：生成班级汇总 Step 6 → 学生模式：生成个性化练习建议 --- ## 二、模式自动识别 - **教师模式**：输入=标准文本/音频 + 多名学生音频 + 姓名/学号 - **学生模式**：输入=个人音频 + 标准文本/音频 + 自选等级 --- ## 三、音频处理流程（统一标准） ### Step 1：音频格式统一 - 转换为 WAV 格式 - 采样率 16000Hz、单声道 - 异常音频自动标记，不打低分 ### Step 2：Whisper 语音转写（固定配置） ```bash ffmpeg -y -i input.mp4 -vn -acodec pcm_s16le -ar 16000 -ac 1 audio.wav whisper audio.wav --model medium --language ja --output_format txt ``` - 仅做转写，不做发音打分依据 - 转写存疑 → 标记人工复核，不扣分 --- ## 四、评分规则（鼓励版 · N5-N1 分层） ### 4.1 基础分与总分规则 **基础分：10.0 分** **计分公式：** ``` 总分 = 10.0 - 累计扣分 约束：6.0 ≤ 总分 ≤ 10.0 ``` **扣分上限：4.0 分**（超过不再扣） --- ### 4.2 错误类型与扣分值（仅3类） | 错误类型 | 扣分 | 说明 | |----------|------|------| | 严重发音错误 | **-0.5** | 促音、长音、清浊混淆（三者任一出现） | | 漏读/多读整句 | **-0.5** | 漏掉一整句 或 多读一整句 | | 中途中断/长时间停顿 | **-0.5** | 朗读中途放弃，或停顿超过3秒 | **重要规则：** - 同一错误类型全篇**只扣一次**，不重复累计 - 上述3类错误若同时出现，累计扣分最多（最多 -1.5，但扣分上限为4.0） --- ### 4.3 各层级额外宽容规则 | 层级 | 宽容规则 | |------|----------| | **N5** | 声调轻微偏差、拨音偶尔读不到位、轻微卡顿（≤3处）→ **不扣分** | | **N4** | 声调轻微偏差、轻微卡顿（≤2处）→ **不扣分** | | **N3** | 轻微卡顿（≤1处）→ **不扣分** | | **N2** | 无宽容项，按标准执行 | | **N1** | 无宽容项，按标准执行 | --- ### 4.4 评分维度（用于报告展示） | 维度 | 权重 | 说明 | |------|------|------| | 发音准确性 | 35% | 假名、清浊音、长音、促音、拨音 | | 语调与节奏 | 30% | 句尾升降调、语句自然起伏 | | 流畅度 | 35% | 连贯性、停顿、中断 | **维度得分与总分关系：** - 维度得分（发音/语调/流畅度）仅用于报告展示 - 最终总分以 4.1 公式为准（10.0 - 累计扣分） - 两者可以不一致 --- ### 4.5 等级划分 | 等级 | 分数区间 | |------|----------| | 优秀 | 8.0 ~ 10.0 | | 良好 | 7.0 ~ 7.9 | | 中等 | 6.0 ~ 6.9 | --- ## 五、纠错规则（鼓励导向） ### 5.1 篇幅定义 - 短篇：150字以内 - 长篇：150字以上 ### 5.2 纠错上限（按水平+篇幅） | 学生水平 | 短篇 | 长篇 | |----------|------|------| | N5 | 1项 | 2项 | | N4 | 2项 | 3项 | | N3 | 2项 | 3项 | | N2 | 3项 | 4项 | | N1 | 4项 | 5项 | ### 5.3 不扣分项目（全层级通用） - は/わ、を/お 识别误差 - 句尾语气词、轻微拖音 - 同一错误只扣一次 - 同音词按假名判定 - 转写存疑、不确定是否为错误 → **不扣分** --- ## 六、两轮校验机制 - 第1轮、第2轮独立批改 - 不一致 → 取高分 + 宽松判定（优先不判为错误） - 不确定 → 不判错 --- ## 八、输出报告模板（统一格式） ``` 学生姓名：{name} 等级：{level} 【评分结果】 总分：{score}/10 发音准确性：{p}/10 语调与节奏：{i}/10 流畅度：{f}/10 【总体评价】 {positive_comment}，{constructive_feedback}。{encouragement} 【发音纠错】 1. 原句：{original} 你的读法：{student_reading} 标准读法：{correct_reading}（{kana}） 错误类型：{error_type} 【练习建议】 1. {suggestion_1} 2. {suggestion_2} ``` **排版规则：** - 一级大项单独另起一行 - 禁用 ⚠ 等 emoji - 评语 ≤ 1000 字符 - 先表扬后建议 --- ## 九、双模式功能说明 ### 教师模式（批量批改） - 批量导入学生音频 - 自动评分 + 纠错 - 生成个人报告 - 生成班级成绩汇总 - 生成课堂讲评建议 - 支持升档推荐名单 ### 学生模式（自测练习） - 即时打分 - 精准纠错 - 个性化练习建议 - 自动记录薄弱点 - 支持等级自适应（N5↔N4↔N3↔N2↔N1） --- ## 十、核心原则 1. **鼓励为主**：从 10 分起评，据实扣分，不刻意压低 2. **只纠严重错误**：只纠正影响语义的明显发音错误 3. **同一错误只扣一次**：不重复扣分 4. **不确定时不判错**：转写存疑优先不判错 5. **保护信心**：总分不低于 6 分，评语先表扬后建议 --- ## 十一、支持场景 - 课文朗读作业打分 - 日语发音测试 - 课堂小测批改 - 学生自主练习 - 单元复习测评 - 日语口语作业验收 ``` ---


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jpengcheng523-netizen/jpeng-ai-grammar-checker (version 1.0.0)

--- name: jpeng-ai-grammar-checker description: "Check and correct grammar errors in text" version: "1.0.0" author: "jpeng" tags: ["nlp", "grammar", "ai"] --- # AI Grammar Checker Check and correct grammar errors in text ## When to Use - User needs nlp related functionality - Automating grammar tasks - Ai operations ## Usage ```bash python3 scripts/ai_grammar_checker.py --input <input> --output <output> ``` ## Configuration Set required environment variables: ```bash export GRAMMAR_API_KEY="your-api-key" ``` ## Output Returns JSON with results: ```json { "success": true, "data": {} } ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wavmson/compact-test-b (version 1.0.0)

--- name: smart-compact description: "Smart context compaction for OpenClaw agents. 4-phase progressive strategy: Scan, Extract, Check, Compact. Before running /compact, this skill scans tool outputs, extracts valuable information into memory files, and generates a pre-compact checklist — ensuring nothing important is lost during compression. Triggers: smart-compact, 智能压缩, pre-compact, 压缩检查, compact check." --- # Smart Compact — 智能压缩增强 四阶段渐进式压缩策略，在 /compact 前先把重要信息救出来。 ## 什么时候用 - 用户说"智能压缩"、"smart-compact"、"压缩检查" - 在手动执行 /compact 之前先跑一遍 - 对话上下文快满时，主动触发 - Heartbeat 检测到 context 接近 80% 时自动建议 ## 核心理念 传统的上下文压缩是一刀切——整个对话被浓缩成一段摘要，大量细节在过程中丢失。 Smart Compact 采用**四阶段渐进式**策略，在 /compact 之前插入一个"预处理"阶段： 1. **扫描**：识别对话中的大块工具输出和关键信息 2. **提取**：把值得保留的信息写入记忆文件 3. **检查**：生成压缩前检查清单，标记风险项 4. **压缩**：用户确认安全后才执行压缩 核心原则：**先救再压**，宁可多存也不能漏存。 ## 执行流程 ### Phase 1 — 扫描工具输出 1. 回顾当前对话中所有的工具调用结果 2. 识别**大块输出**（超过 50 行或 2000 字符的工具结果） 3. 对每个大块输出评估： - 是否包含**关键信息**（决策、配置、错误信息、地址等） - 是否已经被后续对话**引用或总结过** - 是否是**重复或冗余**的（如多次 ls、git status） ### Phase 2 — 提取记忆 1. 从工具输出和对话中提取值得持久化的信息： - **新发现的事实**：地址、配置值、端点、文件路径 - **决策和原因**：为什么选了方案 A 而不是 B - **错误和解决方案**：踩坑记录 - **用户偏好**：明确表达的喜好或要求 - **任务进度**：哪些做完了，哪些还没做 2. 将提取的信息**追加写入** `memory/YYYY-MM-DD.md` - 使用 `edit`（追加模式），绝不覆盖已有内容 - 每条记忆附带简短的来源说明 ### Phase 3 — 生成压缩前检查清单 输出一份结构化的检查清单： ``` 📋 Smart Compact 检查清单 ━━━━━━━━━━━━━━━━━━━━━━ 📊 扫描统计： - 工具调用总数：N 次 - 大块输出（>50行）：N 个 - 已引用/总结过的：N 个 - 可安全压缩的：N 个 💾 已提取到记忆： - [+] 新事实：简要描述... - [+] 决策记录：简要描述... - [+] 错误解决：简要描述... （共 N 条写入 memory/YYYY-MM-DD.md） ⚠️ 需要注意： - [!] 某某工具输出包含重要数据但尚未被引用 - [!] 某某配置值只出现在工具输出中 ✅ 建议：可以安全执行 /compact ``` ### Phase 4 — 执行压缩（可选） - 如果检查清单显示"✅ 可以安全压缩"，提示用户确认 - 用户确认后，执行 /compact - 如果有 ⚠️ 警告项，先处理完再压缩 ## 规则 ### 必须遵守 - **绝不丢弃未被记录的关键信息**：宁可多存也不能漏存 - **追加写入**：只用 edit 追加到 memory 文件，绝不覆盖 - **不自动压缩**：除非用户明确确认，否则只生成检查清单 - **透明**：每一步操作都告知用户 ### 信息分类标准 - **必须保存**：重要配置、地址端点、文件路径、错误解决方案 - **建议保存**：决策原因、用户偏好、任务进度 - **可以丢弃**：重复的 ls 输出、已被总结的搜索结果、中间调试过程 ## 与 Dream Skill 的配合 Smart Compact 和 Dream 是互补的： - **Smart Compact**：实时的，在压缩前抢救信息 → 写入日记 - **Dream**：定期的，把日记整合到长期记忆 → 更新 MEMORY.md 推荐工作流： 1. 对话中随时触发 Smart Compact 保护信息 2. 每天凌晨 Dream 整合日记到长期记忆 3. 形成完整的记忆保护链条


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: cp33333333333/tavily-search1 (version 1.0.0)

--- name: tavily description: AI-optimized web search via Tavily API. Returns concise, relevant results for AI agents. homepage: https://tavily.com metadata: {"clawdbot":{"emoji":"🔍","requires":{"bins":["node"],"env":["TAVILY_API_KEY"]},"primaryEnv":"TAVILY_API_KEY"}} --- # Tavily Search AI-optimized web search using Tavily API. Designed for AI agents - returns clean, relevant content. ## Search ```bash node {baseDir}/scripts/search.mjs "query" node {baseDir}/scripts/search.mjs "query" -n 10 node {baseDir}/scripts/search.mjs "query" --deep node {baseDir}/scripts/search.mjs "query" --topic news ``` ## Options - `-n <count>`: Number of results (default: 5, max: 20) - `--deep`: Use advanced search for deeper research (slower, more comprehensive) - `--topic <topic>`: Search topic - `general` (default) or `news` - `--days <n>`: For news topic, limit to last n days ## Extract content from URL ```bash node {baseDir}/scripts/extract.mjs "https://example.com/article" ``` Notes: - Needs `TAVILY_API_KEY` from https://tavily.com - Tavily is optimized for AI - returns clean, relevant snippets - Use `--deep` for complex research questions - Use `--topic news` for current events


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: codenova58/authentication (version 1.0.0)

--- name: authentication description: "Proving identity: sessions, tokens, MFA, recovery. Use when implementing login, token refresh, or auth bugs." --- # Authentication Skill This skill provides structured guidance for **Authentication** work. Act as an active guide: confirm triggers, propose the stages below, and adapt if the user wants a lighter pass. ## When to Offer This Workflow **Trigger conditions:** - User mentions **authentication** or closely related work - They want a structured workflow rather than ad-hoc tips - They are preparing a review, rollout, or stakeholder communication **Initial offer:** Explain the four stages briefly and ask whether to follow this workflow or work freeform. If they decline, continue in their preferred style. ## Workflow Stages ### Stage 1: Clarify context & goals Anchor on **threat model: sessions vs tokens**. Ask what success looks like, constraints, and what must not break. Capture unknowns early. ### Stage 2: Design or plan the approach Translate goals into a concrete plan around **passwords, MFA, and recovery**. Compare alternatives and explicit trade-offs; avoid implicit assumptions. ### Stage 3: Implement, validate, and harden Execute with verification loops tied to **token lifetime and refresh**. Prefer small steps, measurable checks, and rollback points where risk is high. ### Stage 4: Operate, communicate, and iterate Close the loop with **logging, lockout, and abuse**: monitoring, documentation, stakeholder updates, and lessons learned for the next cycle. ## Checklist Before Completion - Goals and constraints are explicit for **Authentication Skill** - Risks and trade-offs are stated, not hand-waved - Verification steps match the change’s impact (tests, canary, peer review) - Operational follow-through is covered (monitoring, docs, owners) ## Tips for Effective Guidance - Be procedural: stage-by-stage, with clear exit criteria - Ask for missing context (environment, scale, deadlines) before prescribing - Prefer checklists and concrete examples over generic platitudes - If the user declines the workflow, switch to freeform help without lecturing ## Handling Deviations - If the user wants to skip a stage: confirm and continue with what they need. - If context is missing: ask targeted questions before strong recommendations. - Prefer concrete examples, trade-offs, and verification steps over generic advice. ## Quality Bar - Each recommendation should be **actionable** (what to do next). - Call out **failure modes** relevant to Authentication (security, scale, UX, or ops). - Keep tone direct and respectful of the user’s time.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: richard7463/binance-narrative-newsroom (version 1.0.1)

name: binance-narrative-newsroom description: 当用户想发现今天最值得写的 Binance 市场热点、查看昨天到今天的阶段变化、整理 watchlist、生成多版本 Binance Square 中文草稿，或输出一张适合分享的热点卡片时使用。 --- # 币安热点编辑部 这个 skill 直接帮用户做今天的热点选题和发帖准备。 它做的是： 从 Binance 官方公开的 Web3 榜单和 Smart Money 信号里，整理出今天最值得写的市场叙事，并给出阶段变化和发布工作台。 公开接口： - 选题 API: `https://binance-narrative-newsroom.vercel.app/api/newsroom` - 分享图 API: `https://binance-narrative-newsroom.vercel.app/api/newsroom/share-image` ## 适用场景 - 用户想知道今天 Binance 生态里哪条热点最值得写 - 用户需要一版可直接发到 Binance Square 的中文草稿 - 用户想看某条主线是如何从昨天走到今天的 - 用户想用“社交热度 + 统一热榜 + 聪明钱流入 + Smart Money”来做判断 - 用户想得到 watchlist、反方风险提示和多版本发帖稿，而不是一个简单热榜 ## 调用方式 如果用户没有特别指定，默认使用： - `audience`: `SQUARE` - `angle`: `MORNING` 可选参数： - `audience` - `TRADER` - `SQUARE` - `COMMUNITY` - `angle` - `MORNING` - `SMART_MONEY` - `CONTRARIAN` 调用： ```bash curl -sS -X POST https://binance-narrative-newsroom.vercel.app/api/newsroom \ -H 'Content-Type: application/json' \ -d '{"audience":"SQUARE","angle":"MORNING"}' ``` ## 重点字段 - `headline` - `deckSummary` - `editorialDecision` - `leadStory` - `boardStories` - `watchlist` - `avoidList` - `publishDesk` - `squareDraft` - `shareText` ## 输出要求 - 先告诉用户今天的头条选题是什么 - 再解释为什么是这条线，尤其要点明 `leadStory.shift` - 明确区分： - 可写热点 - 观察名单 - 不建议直接追的对象 - 如果用户要发帖，优先给 `publishDesk.drafts` 里的多版本正文，再补 `titleOptions` 和 `publishChecklist` - 如果用户要分享图，把以下字段编码后拼到分享图接口： - `symbol` - `stage` - `resonanceScore` - `headline` - `summary` - `watchline` - `generatedAt` - `shiftLine`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: evezart/evez-api-gateway (version 1.0.0)

--- name: evez-api-gateway description: Connect to the EVEZ AI API — an OpenAI-compatible API that's 99% cheaper than GPT-4. Use when routing LLM calls through EVEZ for cost savings, accessing evez-smart/evez-code/evez-fast/evez-vision models, or building apps on top of EVEZ infrastructure. Free tier with 100K tokens/month, Pro at $5/month unlimited. --- # EVEZ API Gateway OpenAI-compatible AI API at 99% less than GPT-4. Drop-in replacement. ## Quick Start ```bash export OPENAI_BASE_URL=https://evez-api2.fly.dev/v1 export OPENAI_API_KEY=evez-your-key ``` ```python from openai import OpenAI client = OpenAI(base_url="https://evez-api2.fly.dev/v1", [REDACTED_SECRET]") response = client.chat.completions.create(model="evez-smart", messages=[{"role": "user", "content": "Hello!"}]) ``` ## Models | Model | Base | Best For | 1M Output | |-------|------|----------|-----------| | evez-smart | GLM-5.1 | General purpose — smart & fast | $6.00 | | evez-code | DeepSeek V3.2 | Code generation & reasoning | $6.00 | | evez-fast | MiniMax M2.5 | Quick balanced responses | $6.00 | | evez-vision | Kimi K2.5 | Multimodal (text + image) | $6.00 | Compare: GPT-4o = $17.50, Claude Sonnet = $15.00, Gemini Pro = $7.00 ## Pricing - **Free**: 100K tokens/month — no credit card - **Pro**: $5/month — unlimited tokens - **Business**: $25/month — team + SLA ## Features - ✅ OpenAI-compatible (drop-in replacement) - ✅ Streaming support (SSE) - ✅ 4 models including vision - ✅ API key management - ✅ Usage tracking - ✅ 99.9% uptime on Fly.io ## Get API Key Sign up at https://evez-api2.fly.dev/signup


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: adtomato/docx-tool (version 1.0.0)

--- name: docx-tool description: 使用 python-docx 库创建、读取和修改 Word 文档 (.docx)。支持文本、段落、表格、样式、图片等操作。 --- # DOCX 文档操作工具 使用 python-docx 库进行 Word 文档的创建、读取和修改。 ## 前置要求 已安装 python-docx： ```bash pip install python-docx ``` ## 常用操作 ### 创建新文档 ```python from docx import Document from docx.shared import Inches, Pt, RGBColor from docx.enum.text import WD_ALIGN_PARAGRAPH # 创建新文档 doc = Document() # 添加标题 doc.add_heading('文档标题', level=1) # 添加段落 p = doc.add_paragraph('这是一个普通段落。') p.add_run('这是加粗文字').bold = True p.add_run('，这是普通文字。') # 保存文档 doc.save('output.docx') ``` ### 读取现有文档 ```python from docx import Document doc = Document('input.docx') # 读取所有段落文本 for para in doc.paragraphs: print(para.text) # 读取表格 for table in doc.tables: for row in table.rows: for cell in row.cells: print(cell.text) ``` ### 添加表格 ```python from docx import Document doc = Document() # 创建 3x3 表格 table = doc.add_table(rows=3, cols=3) table.style = 'Light Grid Accent 1' # 填充表头 hdr_cells = table.rows[0].cells hdr_cells[0].text = '姓名' hdr_cells[1].text = '年龄' hdr_cells[2].text = '城市' # 填充数据 row_cells = table.rows[1].cells row_cells[0].text = '张三' row_cells[1].text = '25' row_cells[2].text = '北京' doc.save('table.docx') ``` ### 设置样式 ```python from docx import Document from docx.shared import Pt, RGBColor, Inches from docx.enum.text import WD_ALIGN_PARAGRAPH doc = Document() # 添加标题并设置样式 heading = doc.add_heading('标题', level=1) heading.alignment = WD_ALIGN_PARAGRAPH.CENTER # 添加段落并设置格式 p = doc.add_paragraph() run = p.add_run('自定义格式文字') run.font.size = Pt(14) run.font.bold = True run.font.color.rgb = RGBColor(255, 0, 0) # 设置段落间距 p.paragraph_format.space_before = Pt(12) p.paragraph_format.space_after = Pt(12) doc.save('styled.docx') ``` ### 添加图片 ```python from docx import Document from docx.shared import Inches doc = Document() doc.add_heading('带图片的文档', level=1) # 添加图片 doc.add_picture('image.png', width=Inches(5.0)) # 添加图片说明 doc.add_paragraph('图 1: 示例图片', style='Caption') doc.save('with_image.docx') ``` ### 修改现有文档 ```python from docx import Document doc = Document('existing.docx') # 在开头插入段落 doc.paragraphs[0].insert_paragraph_before('新插入的段落') # 修改现有段落 if doc.paragraphs: doc.paragraphs[0].text = '修改后的文字' # 添加新内容到末尾 doc.add_paragraph('添加到末尾的段落') doc.save('modified.docx') ``` ## 可用表格样式 - `Table Grid` - 基础网格 - `Light Grid Accent 1` - 浅色网格 - `Medium Grid 1` - 中等网格 - `Medium Grid 2` - 中等网格 2 - `Medium Grid 3` - 中等网格 3 ## 段落对齐方式 - `WD_ALIGN_PARAGRAPH.LEFT` - 左对齐 - `WD_ALIGN_PARAGRAPH.CENTER` - 居中 - `WD_ALIGN_PARAGRAPH.RIGHT` - 右对齐 - `WD_ALIGN_PARAGRAPH.JUSTIFY` - 两端对齐 ## 注意事项 1. **文件格式**: 仅支持 .docx 格式，不支持 .doc 格式 2. **兼容性**: 复杂格式在不同 Word 版本间可能有差异 3. **图片**: 支持的格式包括 PNG, JPEG, GIF, BMP 4. **字体**: 使用系统已安装的字体


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jmagar/sabnzbd (version 1.0.0)

--- name: sabnzbd version: 1.0.0 description: Manage Usenet downloads with SABnzbd. Use when the user asks to "check SABnzbd", "list NZB queue", "add NZB", "pause downloads", "resume downloads", "SABnzbd status", "Usenet queue", "NZB history", or mentions SABnzbd/sab download management. --- # SABnzbd API Manage Usenet downloads via SABnzbd's REST API. ## Setup Config: `~/.clawdbot/credentials/sabnzbd/config.json` ```json { "url": "http://localhost:8080", "apiKey": [REDACTED_SECRET] } ``` Get your API key from SABnzbd Config → General → Security. ## Quick Reference ### Queue Status ```bash # Full queue ./scripts/sab-api.sh queue # With filters ./scripts/sab-api.sh queue --limit 10 --category tv # Specific job ./scripts/sab-api.sh queue --nzo-id SABnzbd_nzo_xxxxx ``` ### Add NZB ```bash # By URL (indexer link) ./scripts/sab-api.sh add "https://indexer.com/get.php?guid=..." # With options ./scripts/sab-api.sh add "URL" --name "My Download" --category movies --priority high # By local file ./scripts/sab-api.sh add-file /path/to/file.nzb --category tv ``` Priority: `force`, `high`, `normal`, `low`, `paused`, `duplicate` ### Control Queue ```bash ./scripts/sab-api.sh pause # Pause all ./scripts/sab-api.sh resume # Resume all ./scripts/sab-api.sh pause-job <nzo_id> ./scripts/sab-api.sh resume-job <nzo_id> ./scripts/sab-api.sh delete <nzo_id> # Keep files ./scripts/sab-api.sh delete <nzo_id> --files # Delete files too ./scripts/sab-api.sh purge # Clear queue ``` ### Speed Control ```bash ./scripts/sab-api.sh speedlimit 50 # 50% of max ./scripts/sab-api.sh speedlimit 5M # 5 MB/s ./scripts/sab-api.sh speedlimit 0 # Unlimited ``` ### History ```bash ./scripts/sab-api.sh history ./scripts/sab-api.sh history --limit 20 --failed ./scripts/sab-api.sh retry <nzo_id> # Retry failed ./scripts/sab-api.sh retry-all # Retry all failed ./scripts/sab-api.sh delete-history <nzo_id> ``` ### Categories & Scripts ```bash ./scripts/sab-api.sh categories ./scripts/sab-api.sh scripts ./scripts/sab-api.sh change-category <nzo_id> movies ./scripts/sab-api.sh change-script <nzo_id> notify.py ``` ### Status & Info ```bash ./scripts/sab-api.sh status # Full status ./scripts/sab-api.sh version ./scripts/sab-api.sh warnings ./scripts/sab-api.sh server-stats # Download stats ``` ## Response Format Queue slot includes: - `nzo_id`, `filename`, `status` - `mb`, `mbleft`, `percentage` - `timeleft`, `priority`, `cat` - `script`, `labels` Status values: `Downloading`, `Queued`, `Paused`, `Propagating`, `Fetching` History status: `Completed`, `Failed`, `Queued`, `Verifying`, `Repairing`, `Extracting`


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: zhouzy-creator/local-code-review (version 0.1.1)

--- name: local-cpp-code-review description: 遍历指定本地目录，对所有 C/C++ 源代码进行生产就绪性审查、质量检查及潜在漏洞分析。 --- # 本地 C/C++ 代码审查 (OpenClaw 版) 当用户要求“审查 [目录] 目录的代码”时，调用此 Skill。该工具直接扫描本地文件系统，对 C/C++ 核心逻辑进行深度分析。 ## 核心流程 **1. 目录扫描：** 使用 `ls -R` 或文件遍历工具定位指定目录下的所有 `.c`, `.cpp`, `.h`, `.hpp` 文件。 **2. 分段审查：** 由于上下文限制，如果目录较大，应按模块或文件组分批调用审查模型。 **3. 触发指令：** - "审查 ./src 目录的代码" - "检查当前文件夹下 C++ 源码的质量" ## 审查维度 (针对 C/C++) - **内存安全：** 检查是否存在内存泄漏（如 `new` 无 `delete`）、野指针、缓冲区溢出（使用 `strcpy` 等危险函数）。 - **资源管理：** 是否遵循 RAII 原则？智能指针（`unique_ptr`, `shared_ptr`）使用是否得当？ - **并发安全：** 检查死锁风险、未加锁的共享变量、竞态条件。 - **性能开销：** 检查非必要的拷贝（如未传递 `const reference`）、昂贵的循环内操作。 - **现代化 C++：** 是否使用了 C++11/14/17+ 的特性来简化代码？ ## 交互模板 (local-code-reviewer.md ) 调用时请按以下格式填充上下文： - `{TARGET_DIRECTORY}`: 待审查的本地路径 - `{FILE_LIST}`: 扫描到的文件清单 - `{CODE_SNIPPETS}`: 关键文件的源代码内容 - `{CONSTRAINTS}`: 项目特定的编码规范（如有） ## 输出规范 ### 1. 扫描报告 列出已审查的文件数量及发现的问题汇总。 ### 2. 问题分类 - **致命 (Critical):** 内存崩溃、安全漏洞、逻辑死循环。 - **重要 (Important):** 资源泄漏隐患、违反 RAII、缺乏异常处理。 - **建议 (Minor):** 命名规范、冗余代码、现代化改进建议。 ### 3. 修复示例 对每个“致命”和“重要”问题，必须提供修改前后的代码对比。 ## 注意事项 - **禁止：** 忽略本地路径中的第三方库（如 `node_modules` 或 `vendor`），只聚焦于业务源码。 - **建议：** 优先检查头文件中的类定义，再检查实现文件。 - **限制：** 如果单个文件超过 4k tokens，应采取“函数级”拆分审查。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: dzxiatian-crypto/weibo-trending-topics (version 1.0.0)

--- name: weibo-trending-topics description: > Track Weibo hot search trends (微博热搜) in real-time. Fetches live trending topics, categories (娱乐/社会/科技/体育), and historical trend data. Zero config required. Triggers: "微博热搜", "weibo trending", "微博趋势", "热搜榜". version: 1.0.0 tags: - latest - chinese-platform - trending - social-media --- # Weibo Trending Topics Track real-time trending topics on Weibo (微博热搜榜) with category filtering and historical comparison. ## 🔵 Zero Config — Works Immediately No API keys, no login needed. Uses Weibo's public API endpoints. ## Usage ```python import requests # 微博热搜实时榜单 (Top 50) url = "https://weibo.com/ajax/side/hotSearch" resp = requests.get(url, headers={"User-Agent": "Mozilla/5.0"}) data = resp.json() for item in data.get("realtime", []): print(f"{item.get('flag_desc','')} {item.get('word')} | 🔥 {item.get('num')}") ``` ## Historical Comparison ```python def compare_trends(old_file, new_file): with open(old_file) as f: old = {item["word"] for item in json.load(f).get("realtime", [])} with open(new_file) as f: new = {item["word"] for item in json.load(f).get("realtime", [])} print(f"🆕 新上榜: {new - old}") print(f"📉 已掉榜: {old - new}") ``` ## Tags `weibo` `微博` `热搜` `trending` `social-media` `chinese-platform` `news` `hot-search`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: steipete/openhue (version 1.0.0)

--- name: openhue description: Control Philips Hue lights/scenes via the OpenHue CLI. homepage: https://www.openhue.io/cli metadata: {"clawdbot":{"emoji":"💡","requires":{"bins":["openhue"]},"install":[{"id":"brew","kind":"brew","formula":"openhue/cli/openhue-cli","bins":["openhue"],"label":"Install OpenHue CLI (brew)"}]}} --- # OpenHue CLI Use `openhue` to control Hue lights and scenes via a Hue Bridge. Setup - Discover bridges: `openhue discover` - Guided setup: `openhue setup` Read - `openhue get light --json` - `openhue get room --json` - `openhue get scene --json` Write - Turn on: `openhue set light <id-or-name> --on` - Turn off: `openhue set light <id-or-name> --off` - Brightness: `openhue set light <id> --on --brightness 50` - Color: `openhue set light <id> --on --rgb #3399FF` - Scene: `openhue set scene <scene-id>` Notes - You may need to press the Hue Bridge button during setup. - Use `--room "Room Name"` when light names are ambiguous.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)