OpenClaw/clawhub-security-signals huggingface dataset:
Skill: mike47512/numerology-reading (version 1.0.0)

--- name: numerology-reading description: | Western-style numerology (Pythagorean-style)—life path and related numbers from birth date and name, with symbolic themes. Use when the user asks for life path number, angel numbers, name number, or “what does my birthday mean” in a numerology context. Entertainment and self-reflection only; not science, not medical/legal/financial advice. metadata: openclaw: emoji: "🔢" --- # Numerology Reading (Western) ## Overview This skill supports **Western numerology** as commonly practiced online: reduce dates and names to **single digits (1–9)** and **master numbers (11, 22, 33)** where the tradition allows. Use it for **themes and prompts**, not destiny. **Trigger keywords:** numerology, life path number, angel number, expression number, soul urge, birthday number, 生命灵数, 数字命理 --- ## Core ideas (typical Pythagorean-style) - **Life Path** — from **full birth date** (reduce to 1–9 or 11/22/33). - **Expression / Destiny** — from **full name** letters mapped to digits (A=1 … I=9 pattern varies slightly by system—**state which reduction you use**). - **Birthday number** — day of month reduced. - **Angel numbers** (e.g. 111, 222) — **popular culture** framing; avoid superstitious fear. Always **show the math** when you compute (e.g. “1+9+9+0 = 19 → 1+9 = 10 → 1+0 = **1**”) so users can verify. --- ## When to use - User wants a **number profile** from birthday or name. - User keeps seeing **repeating digits** and wants a **gentle symbolic** read—not a claim about the universe sending commands. **Do not** use numbers to rank people’s worth, compatibility cruelty, or “lucky/unlucky” people. --- ## Assistant behavior 1. **Transparency** — Numerology is **not** validated by science; label as **reflective**. 2. **Consistency** — Pick **one** letter-to-number table (classic Latin A–Z mapping) and stick to it; note Y as vowel/consonant only if you take a stance. 3. **Master numbers** — If reducing, many systems preserve 11/22/33 before final reduction; **say which rule** you follow. 4. **Inclusive** — Names with diacritics: normalize or state limitation. 5. **Language** — Match the user’s language. --- ## Suggested output structure ```markdown # Numerology snapshot **Method**: [e.g. Pythagorean; master numbers preserved at step X] ## Inputs - Birth date: … - Name (if used): … ## Calculations (show steps) - Life Path: … → **N** - (Optional) Expression: … → **N** ## Themes (non-literal) - [2–4 bullets tied to N as *cultural* meanings, not facts] ## Disclaimer For entertainment and reflection only—not professional, medical, legal, or financial advice. ``` --- ## References (general) - Look for **educational** descriptions of “Pythagorean numerology” or “life path number”; treat pop sites as **varied**, not one standard.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wu-uk/dynamic-object-aware-egomotion-dyn-object-masks (version 0.1.0)

--- name: dyn-object-masks description: "Generate dynamic-object binary masks after global motion compensation, output CSR sparse format." --- # When to use - Detect moving objects in scenes with camera motion; produce sparse masks aligned to sampled frames. # Workflow 1) **Global alignment**: warp previous gray frame to current using estimated affine/homography. 2) **Valid region**: also warp an all-ones mask to get `valid` pixels, avoiding border fill. 3) **Difference + adaptive threshold**: `diff = abs(curr - warp_prev)`; on `diff[valid]` compute median + 3×MAD; use a reasonable minimum threshold to avoid triggering on noise. 4) **Morphology + area filter**: open then close; keep connected components above a minimum area (tune as fraction of image area or a fixed pixel threshold). 5) **CSR encoding**: for final bool mask - `rows, cols = nonzero(mask)` - `indices = cols.astype(int32)`; `data = ones(nnz, uint8)` - `counts = bincount(rows, minlength=H)`; `indptr = cumsum(counts, prepend=0)` - store as `f_{i}_data/indices/indptr` # Code sketch ```python warped_prev = cv2.warpAffine(prev_gray, M, (W,H), flags=cv2.INTER_LINEAR, borderValue=0) valid = cv2.warpAffine(np.ones((H,W),uint8), M, (W,H), flags=cv2.INTER_NEAREST)>0 diff = cv2.absdiff(curr_gray, warped_prev) vals = diff[valid] thr = max(20, np.median(vals) + 3*1.4826*np.median(np.abs(vals - np.median(vals)))) raw = (diff>thr) & valid m = cv2.morphologyEx(raw.astype(uint8)*255, cv2.MORPH_OPEN, k3) m = cv2.morphologyEx(m, cv2.MORPH_CLOSE, k7) n, cc, stats, _ = cv2.connectedComponentsWithStats(m>0, connectivity=8) mask = np.zeros_like(raw, dtype=bool) for cid in range(1,n): if stats[cid, cv2.CC_STAT_AREA] >= min_area: mask |= (cc==cid) ``` # Self-check - [ ] Masks only for sampled frames; keys match sampled indices. - [ ] `shape` stored as `[H, W]` int32; `len(indptr)==H+1`; `indptr[-1]==indices.size`. - [ ] Border fill not treated as foreground; threshold stats computed on valid region only. - [ ] Threshold + morphology + area filter applied.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: fangshan101-coder/bijia (version 0.1.0)

--- name: 全网比价助手 description: 全网商品比价工具，一键对比淘宝京东拼多多抖音等各平台同款商品价格，结合优惠券和返利计算真实最低到手价。 version: 0.1.0 --- # 全网比价助手 全网商品比价工具。输入商品名称或链接，自动在淘宝、京东、拼多多、抖音、唯品会等各平台搜索同款商品，对比价格、优惠券和返利后的真实到手价，一键找到最便宜的购买渠道。 ## 触发词 比价、全网比价、bijia、哪里最便宜、价格对比、比价工具、跨平台比价、最低价、商品比价 ## 计划实现能力 ### 核心功能 - **全平台价格搜索**：淘宝/京东/拼多多/抖音/唯品会/苏宁等 - **同款识别**：智能识别各平台的同款商品（SKU匹配） - **真实到手价**：综合售价+优惠券+返利计算各平台真实到手价 - **历史价格**：展示商品历史价格走势，判断当前是否好价 ### 比价流程 ``` 输入商品名称/链接 ↓ 全平台搜索同款 ↓ 获取各平台实时价格 ↓ 查找各平台可用优惠券 ↓ 计算各平台返利金额 ↓ 按真实到手价排序 ↓ 输出比价报告 + 最优购买建议 ``` ### 支持平台 | 平台 | 价格来源 | 优惠券 | 返利 | |------|---------|--------|------| | 淘宝/天猫 | 实时 | 店铺券+平台券 | 淘宝联盟 | | 京东 | 实时 | PLUS价+优惠券 | 京东联盟 | | 拼多多 | 实时 | 百亿补贴+优惠券 | 多多进宝 | | 抖音商城 | 实时 | 抖音券 | 精选联盟 | | 唯品会 | 实时 | 唯品券 | 唯品联盟 | ### 使用场景 1. **购物前必查**：下单前一键比价，找到最低价渠道 2. **大促对比**：各平台大促价格PK，看谁真便宜 3. **日用补货**：常买的日用品比价，长期省钱 4. **数码大件**：高客单商品比价，一次省几百 ## 输出格式 ``` 🔍 全网比价报告 商品：[商品名称] 各平台到手价（从低到高）： 🥇 [平台1]：¥[到手价]（售价¥[X] - 券¥[Y] - 返利¥[Z]） 🥈 [平台2]：¥[到手价]（售价¥[X] - 券¥[Y] - 返利¥[Z]） 🥉 [平台3]：¥[到手价]（售价¥[X] - 券¥[Y] - 返利¥[Z]） 价差：最多省 ¥[金额] 历史最低：¥[价格]（[日期]） 购买建议：[分析说明] ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mikaelaldy/x-growth-strategy (version 1.0.0)

--- name: x-growth-strategy description: Create an X account growth strategy from public algorithm signals. Use when planning positioning, content pillars, audience graph, and weekly growth experiments. --- # X Growth Strategy Use this when the user wants to grow an X account deliberately, not just post more. ## The Loop 1. Pick a retrieval neighborhood. 2. Publish posts that earn multiple positive signals. 3. Reply under accounts whose audience should overlap with yours. 4. Convert profile clicks into follows. 5. Review analytics weekly. 6. Double down on winners. ## Build The Strategy ### 1. Positioning Write: - who the account helps - what outcome it helps them get - what topics it will repeat - why the author is credible Bad: “AI enthusiast sharing thoughts.” Good: “Final-year ML student building in public toward FIRE, sharing practical AI workflows for broke technical students.” ### 2. Retrieval Neighborhood List: - 20-50 adjacent accounts - 5 recurring topic phrases - 5 audience problems - 3 communities or scenes - 3 anti-topics to avoid ### 3. Signal Portfolio Plan a week with: - **Dwell posts**: breakdowns, lessons, frameworks, stories with payoff. - **Reply posts**: specific questions, tradeoffs, unpopular-but-defensible opinions. - **Share posts**: templates, checklists, resource lists, visual summaries. - **Follow posts**: proof of work, series, clear expertise. ### 4. Feedback Loop For every winning post: - reply to comments quickly - turn best replies into posts - make a part 2 within 24-48 hours - log hook, topic, format, and outcome ## Output Return: - **Account promise** - **Target audience** - **Content pillars** - **Retrieval neighborhood** - **7-day growth plan** - **Daily reply plan** - **Metrics to track** - **Experiments for the week** ## Operating Principle Treat the public X algorithm as a signal map, not a cheat code. Optimize for posts and replies that earn useful positive actions (dwell, replies, reposts, shares, profile clicks, follows) while avoiding negative actions (not interested, mute, block, report, fast skips).


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ryanio/opensea-skill (version 2.2.3)

--- name: opensea description: DEPRECATED — moved to opensea/opensea-marketplace. Install that skill instead. This slug is no longer maintained. --- # DEPRECATED This skill has moved to **[`opensea/opensea-marketplace`](https://clawhub.ai/opensea/opensea-marketplace)** — the official OpenSea publication on ClawHub. All future updates ship there. ## Migration Replace `opensea-skill` with `opensea/opensea-marketplace` in your agent manifest: ```json { "skills": [ { "clawhub_slug": "opensea/opensea-marketplace", "name": "OpenSea" } ] } ``` Or via CLI: ```bash clawhub install opensea/opensea-marketplace ``` ## What changed This skill (`opensea-skill`) was the original location, originally published by @dfinzer and transferred to @ryanio. The OpenSea team subsequently created an `@opensea` ClawHub org and published the skill fresh under it as `opensea-marketplace` — that's now the canonical home. Same skill, same functionality (OpenSea MCP wrapper for NFT data, marketplace listings, Seaport trades, ERC20 swaps across Ethereum/Base/Arbitrum/Polygon/etc.). Only the publisher changed.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: minilozio/serper-google-search (version 1.0.0)

# Google Search Skill Google Search powered by Serper.dev API. Replaces Brave Search with richer results including Knowledge Graph, Answer Box, People Also Ask, and specialized search types. ## When to Use Use this skill for any Google search request. Auto-detect intent: - Default → `search` - "news about X" / "latest X" → `news` - "images of X" / "pictures of X" → `images` - "videos of X" / "how to X video" → `videos` - "restaurants near X" / "X near me" → `places` - "how much does X cost" / "buy X" → `shopping` (⚠️ 2 credits — only when explicitly requested) - "papers on X" / "research about X" → `scholar` - "patents for X" → `patents` - "suggestions for X" → `suggest` ## Usage ```bash SCRIPT_DIR="~/.openclaw/workspace/skills/google-search/scripts" # Web search (default) npx tsx $SCRIPT_DIR/google-search.ts search "query" [--num 10] [--time day|week|month|year] [--country us] [--lang en] # Specialized npx tsx $SCRIPT_DIR/google-search.ts news "query" [--num 10] npx tsx $SCRIPT_DIR/google-search.ts images "query" npx tsx $SCRIPT_DIR/google-search.ts videos "query" npx tsx $SCRIPT_DIR/google-search.ts places "query" npx tsx $SCRIPT_DIR/google-search.ts shopping "query" npx tsx $SCRIPT_DIR/google-search.ts scholar "query" [--year 2023] npx tsx $SCRIPT_DIR/google-search.ts patents "query" npx tsx $SCRIPT_DIR/google-search.ts suggest "query" npx tsx $SCRIPT_DIR/google-search.ts credits ``` Add `--json` to any command for raw JSON output. ## Environment Requires `SERPER_API_KEY` env var. ## Workflow 1. Run the appropriate search command 2. Parse the formatted output 3. If deeper content is needed, use `web_fetch` on promising links 4. Credit balance is shown in every response — monitor usage 5. Shopping costs 2 credits — only use when user explicitly asks for prices/shopping ## Time Filters `--time` accepts: `hour`, `day`, `week`, `month`, `year` (or `h`, `d`, `w`, `m`, `y`) ## Notes - Rate limit: 5 req/sec on free tier - All searches cost 1 credit except shopping (2 credits) - Results include rich data: Knowledge Graph, Answer Box, People Also Ask, Related Searches


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: rascal6666/short-video-script (version 1.0.1)

--- name: short-video-script description: 短视频脚本创作技能。用于生成抖音、快手、B站、视频号等平台的短视频脚本、标题、封面建议。适用于自媒体运营、内容创作需求。 --- # 短视频脚本创作技能 ## 概述 为短视频平台创作高效脚本，提升完播率、互动率和转化率。 ## 适用场景 - 抖音/快手/视频号/B站 脚本创作 - 知识类干货分享 - 热点蹭流内容 - 产品推广文案 - 剧情/情感类内容 ## 脚本结构 ### 黄金3秒开头（抓住注意力） ``` 公式1：提问式 - "你知道...吗？" 公式2：夸张式 - "99%的人都不知道..." 公式3：冲突式 - "年薪百万的工作居然是..." 公式4：结果前置 - "3分钟学会..." + 悬念 ``` ### 核心内容结构 ``` [开场3秒] → [痛点/悬念] → [干货/情节] → [高潮/反转] → [结尾引导] ``` ### 结尾引导 ``` - "关注我，带你了解更多..." - "下期讲..." - "评论区告诉我你的想法" - "点赞收藏防丢失" ``` ## 脚本模板 ### 知识类模板 ``` 【标题】：[吸引眼球的标题] 【时长】：1-3分钟 【开头3秒】： [提出问题/痛点/悬念] 【正文结构】： 1. [核心知识点1] - 30秒 2. [核心知识点2] - 30秒 3. [核心知识点3] - 30秒 【结尾】： [总结+引导互动] ``` ### 热点类模板 ``` 【标题】：[热点相关+悬念] 【开头3秒】： [热点引入+自己的观点] 【正文】： 1. [热点简述] - 10秒 2. [深度分析] - 40秒 3. [独特观点] - 30秒 【结尾】： [引导评论讨论] ``` ## 平台特性 ### 抖音 - 15秒-3分钟为主 - 节奏快，金句多 - BGM很重要 - 完播率优先 ### 快手 - 较抖音更接地气 - 老铁文化 - 真实性重要 ### B站 - 可以更长（5-15分钟） - 内容深度要求高 - 弹幕互动 ### 视频号 - 社交推荐为主 - 私域引流 - 适合知识类 ## 标题公式 ``` 数字型：3个技巧、5个方法 疑问型：为什么...？怎么做...？ 悬念型：原来...真相是... 对比型：月薪3千vs3万的差距 冲突型：千万不要...否则... ``` ## 封面设计建议 ``` - 文字简洁有力 - 突出关键词 - 色彩对比强 - 人物+文字组合 ``` ## 创作流程 1. **确定选题**：结合热点/痛点/定位 2. **设计开头**：3秒抓住注意力 3. **搭建结构**：逻辑清晰 4. **填充内容**：金句/干货/情感 5. **优化结尾**：引导互动 6. **检查节奏**：控制时长 ## 注意事项 - 内容要有价值，避免纯标题党 - 遵守平台规则，避免违规词 - 保持更新频率 - 数据驱动优化 - 真人出镜效果更好 ## 常用工具 - 热点查询：抖音热榜、微博热搜 - 数据分析：蝉妈妈、飞瓜数据 - 剪辑工具：剪映、Premiere


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jarvis563/percept-speaker-id (version 1.0.0)

# percept-speaker-id Speaker identification and management for multi-person conversations. ## What it does Tracks who said what in conversations. Maps anonymous speaker labels (SPEAKER_0, SPEAKER_1) to real names, maintains speaker profiles, and gates voice command authorization. ## When to use - User asks "who said that?" or wants speaker-attributed transcripts - User wants to configure which people can trigger voice commands - Agent needs to know who is speaking in a multi-person conversation ## Requirements - **percept-listen** skill installed and running - **Omi pendant** (provides `is_user` flag for primary speaker) ## How it works 1. Omi sends transcript segments with speaker labels (SPEAKER_0, SPEAKER_1, etc.) 2. Percept resolves labels to names using the speakers registry 3. `is_user` flag from Omi identifies the pendant wearer as the primary speaker 4. Speaker profiles track first/last seen timestamps and authorization status ## Speaker registry Located at `percept/data/speakers.json`: ```json { "SPEAKER_00": { "name": "David", "is_owner": true, "approved": true }, "SPEAKER_01": { "name": "Rob", "is_owner": false, "approved": true } } ``` Manage via Percept dashboard (port 8960) → Settings → Speakers. ## Authorization levels - **Owner** (`is_owner: true`): Full command access, always authorized - **Approved** (`approved: true`): Can trigger wake word commands - **Unknown**: Logged only, commands not executed ## Future: Voice embeddings Planned: pyannote speaker diarization with 192-dim voice embeddings for automatic speaker recognition via cosine similarity. Currently speaker mapping is manual. ## Links - **GitHub:** https://github.com/GetPercept/percept


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: lurklight/win-mouse-native (version 1.0.0)

--- name: win-mouse-native description: Native Windows mouse control (move, click, drag) via user32.dll. Use when the user asks you to move the mouse, click, drag, or automate pointer actions on Windows. --- # Win Mouse Native Provide deterministic mouse control on **Windows**. ## Commands (local) This ClawHub bundle is **docs + scripts-as-text** (ClawHub validates “text files only”). To install: 1) Save `win-mouse.cmd.txt` as `win-mouse.cmd` 2) Save `scripts/win-mouse.ps1.txt` as `scripts/win-mouse.ps1` Then run: - `win-mouse move <dx> <dy>` (relative) - `win-mouse abs <x> <y>` (absolute screen coords) - `win-mouse click left|right|middle` - `win-mouse down left|right|middle` - `win-mouse up left|right|middle` Return value: prints a one-line JSON object. ## OpenClaw usage When the user asks to move/click the mouse: 1) If the user didn’t give coordinates/deltas, ask. 2) Use `exec` to run `win-mouse ...`. 3) Prefer small, reversible actions first (tiny move, single click) when unsure. ## Notes - Windows only. - Uses Win32 `SetCursorPos` + `SendInput` via user32.dll.


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

--- name: jpeng-cooking-timer description: "Kitchen cooking timer" version: "1.0.0" author: "jpeng" tags: ["cooking", "timer", "food"] --- # Cooking Timer Kitchen cooking timer ## When to Use - User needs cooking related functionality - Automating timer tasks - Food operations ## Usage ```bash python3 scripts/cooking_timer.py --input <input> --output <output> ``` ## Configuration Set required environment variables: ```bash export TIMER_API_KEY="your-api-key" ``` ## Output Returns JSON with results: ```json { "success": true, "data": {} } ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: unisound-llm/unisound-abnormal-items (version 1.0.0)

--- name: health-exam-abnormal-items description: 体检异常指标专项解读。针对体检报告中的每项异常，逐一说明"是什么/为何异常/有何影响/如何干预"，并分析多指标关联，语言通俗易懂（JSON + 专项解读文字）。 metadata: { "openclaw": { "emoji": "🔍" } } --- # 异常指标专项解读 概述 ---- 面向**体检中心/健康管理机构**，对体检报告中出现的每一项异常指标进行深度专项解读，本技能会： - 用通俗语言解释每个指标的含义 - 分析可能的异常原因（结合受检者个人背景） - 说明对健康的潜在影响 - 给出生活方式干预建议和就医建议 - 分析多个异常指标之间的内在关联（如代谢综合征） - 标注紧急程度 与"体检报告整体解读"的区别：本技能聚焦**逐项深度解读**，面向用户感知强的异常项，内容更详尽；整体解读侧重全局评级和优先行动。 数据安全、隐私与伦理声明 ------------------------ - **最小必要原则**：仅处理指标解读所必需的检查数据；不要求包含直接身份标识。 - **严格脱敏**：发送前对可识别身份信息进行脱敏处理。 - **不做本地持久化**：仅在内存中短暂处理；**本次调用结束即销毁**。 - **医疗边界**：本技能解读为健康教育参考，不替代医生诊断；存在异常时请就医确认。 输入格式 -------- 纯文本（UTF-8），可输入完整体检报告（会自动提取异常项），也可只输入异常指标列表，例如： ```text 受检者：男，45岁，轻体力劳动，BMI 26.8 异常指标： - 甘油三酯（TG）：2.8mmol/L（↑，参考：<1.7mmol/L） - 总胆固醇（TC）：5.9mmol/L（↑，参考：<5.2mmol/L） - 空腹血糖（FBG）：6.2mmol/L（↑，参考：3.9-6.1mmol/L） - 谷丙转氨酶（ALT）：52U/L（↑，参考：0-40U/L） - 血压：142/90mmHg（↑，参考：<130/80mmHg） 超声提示：轻度脂肪肝 ``` 也支持 JSON 格式（包含 `text`/`content`/`report` 字段的对象）。 快速开始 -------- ```bash # 从 skills 目录运行 python3 health-exam/report-interpret/abnormal-items/scripts/run.py \ --input data/health-exam-abnormal/case-001.txt \ --appkey <your-appkey> # 保存输出到文件 python3 health-exam/report-interpret/abnormal-items/scripts/run.py \ --input data/health-exam-abnormal/case-001.txt \ --appkey <your-appkey> \ --output runs/health-exam-abnormal/case-001.json ``` 参数说明 -------- - `--input PATH`：**必填**。体检报告或异常指标文件路径（txt 或 json，UTF-8）。 - `--appkey STRING`：**必填**。调用内部医疗大模型的鉴权 key，由平台分配。 - `--output PATH`：输出文件路径（默认：打印到 stdout）。 - `--base URL`：内部大模型 base URL（默认：`https://maas-api.hivoice.cn/v1`）。 - `--model STRING`：模型名称（默认：`u1-insuremed`）。 - `--timeout SECONDS`：HTTP 超时秒数；`0` 表示一直等待（默认：0）。 - `--encoding STRING`：输入文件编码（默认：`utf-8`）。 输出约定 -------- 输出分两部分： **JSON 结构**（每个异常指标一条详细解读）： ```json { "abnormal_count": 5, "items": [ { "item_name": "甘油三酯 TG", "value": "2.8mmol/L", "reference_range": "<1.7mmol/L", "deviation": "偏高", "deviation_degree": "轻度", "plain_explanation": "甘油三酯是血液中一种脂肪，主要来自饮食中的油脂和糖分转化", "possible_causes": ["饮食油腻或高糖", "运动量不足", "超重"], "health_impact": "长期偏高增加动脉硬化、心脑血管疾病和胰腺炎风险", "intervention": { "lifestyle": ["减少油炸食品和含糖饮料", "增加有氧运动", "控制体重"], "medical": "定期监测，3个月后复查血脂" }, "urgency": "定期复查" } ], "correlations": [ "血脂升高（TG、TC）+ 血糖偏高 + 超重 + 脂肪肝，提示代谢综合征风险，是心脑血管疾病的重要危险因素组合，建议系统评估和干预" ] } ``` **专项解读文字**：以"【专项解读】"开头，用通俗语言逐项向受检者解释异常指标。 依赖 ---- ### 运行环境 - Python 3.7+（仅使用标准库，无需额外安装） ### 外部 API - 内部医疗大模型：`https://maas-api.hivoice.cn/v1/chat/completions` 备注 ---- - 输入可以是完整报告（技能会自动提取异常项），也可以只粘贴异常指标列表 - 若受检者有已知慢性病史，在输入中注明可获得更精准的解读 - **发布约束**：示例输入、运行输出均放在 skill 包外（`data/`、`runs/`），skill 目录内仅保留可发布的核心文件


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: jackzhenguo/book-translate (version 1.0.0)

--- name: book-translator description: 使用平台内置文本翻译技能，对整本书或长文本进行自动分段翻译 metadata: clawdbot: emoji: "📚" category: "translation" --- # 📚 全书翻译 (Book Translator) 这个 Skill 可以将 **整本书或长文本内容自动拆分并翻译**。 由于大模型存在 token 限制，本技能会： 1. 读取整本书文本 2. 自动拆分为多个小段 3. 调用平台内置 **文本翻译 Skill** 4. 将所有翻译结果合并 5. 输出完整译文 适用于： - PDF 书籍 - TXT 文本 - 长文章 - 电子书内容 - 长报告 --- # 工作流程 完整流程如下： ## 1 读取书籍内容 Agent 获取用户提供的文本或文件内容。 --- ## 2 自动拆分文本 为了避免超过模型 Token 限制，系统会将文本拆分为多个片段。 示例： 原始文本 ↓ Chunk 1 Chunk 2 Chunk 3 Chunk 4 --- ## 3 调用平台内置翻译 Skill 对每个片段调用： text_translate 示例调用： text_translate( text=chunk, target_language="Chinese" ) --- ## 4 合并翻译结果 所有翻译片段将被合并为完整译文： translated_chunk1 translated_chunk2 translated_chunk3 最终输出： 完整翻译文本 --- # 使用方式 用户可以直接对 Agent 说： 请把这本书翻译成中文 或者： translate this book to Chinese 如果用户提供文本： 把下面这本书翻译成中文： [书籍内容] Agent 会自动执行： 1. 读取文本 2. 自动拆分 3. 调用翻译 4. 合并结果 --- # 支持的文件类型 本 Skill 可以处理： TXT PDF Markdown 长文本 如果是 PDF： Agent 需要先提取文本，再执行翻译。 --- # 分段翻译策略 为了保证稳定性，本 Skill 使用 **自动分段翻译策略**。 推荐分段大小： 1000 - 2000 字符 流程示例： 长文本 ↓ 拆分 ↓ chunk1 chunk2 chunk3 ↓ 逐段翻译 ↓ 合并 这样可以： - 防止 Token 超限 - 提高翻译稳定性 - 降低失败概率 --- # 翻译规则 调用翻译 Skill 时，遵循以下规则： 1. 保留原文段落结构 2. 不省略内容 3. 不总结 4. 忠实翻译原文 5. 保留专有名词 --- # 示例流程 输入： Translate this book into Chinese. Agent 执行流程： 读取文本 ↓ 拆分文本 ↓ 调用 text_translate ↓ 翻译 chunk1 翻译 chunk2 翻译 chunk3 ↓ 合并译文 ↓ 输出完整翻译 --- # 示例输出 第一章：人工智能的起源 人工智能的概念最早可以追溯到…… 第二章：机器学习的发展 随着计算能力的提升…… --- # 推荐使用场景 这个 Skill 适用于： - 翻译整本书 - 翻译研究论文 - 翻译技术文档 - 翻译课程资料 - 翻译长报告 --- # 注意事项 1. 如果文本过长，系统会自动拆分。 2. 如果翻译中断，可以重新继续。 3. 翻译质量取决于所调用的大模型。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: hyunwoo91/communication-mqtt (version 1.0.1)

--- name: mqtt-agent-messenger description: MQTT 브로커를 통해 에이전트의 소개(Intro) 및 상태(Status) 메시지를 발행하고 구독하는 기능을 제공합니다. 에이전트 간의 통신이 필요할 때 사용하세요. license: MIT metadata: author: User version: "1.0.0" compatibility: Requires Python 3.x, paho-mqtt, and typer. Local MQTT Broker (localhost:1883) must be running. --- # MQTT Agent Messenger Skill 이 스킬은 MQTT 프로토콜을 사용하여 에이전트의 정보를 네트워크에 공유하거나 다른 에이전트의 상태를 추적하는 데 사용됩니다. ## 주요 기능 - **에이전트 소개 (Intro)**: 에이전트의 ID와 역할(Role) 정보를 발행합니다. - **상태 업데이트 (Status)**: 에이전트의 현재 활동(Activity) 정보를 타임스탬프와 함께 발행합니다. - **메시지 모니터링 (Subscribe)**: 특정 에이전트 혹은 모든 에이전트의 소개 및 상태 메시지를 실시간으로 수신합니다. ## 사용 방법 ### 0. 시작하기전에 #### 파이썬 패키지 설치 ``` pip install paho-mqtt typer ``` #### 자신의 `agent_id` 확인 ``` cat ~/.openclaw/openclaw.json ``` ### 1. 에이전트 정보 발행 (Publish) `publish.py` 스크립트를 사용하여 메시지를 보냅니다. * **자기소개 발행**: ```bash python scripts/publish.py intro --agent-id "agent-1" --role "researcher" ``` * **현재 상태 발행**: ```bash python scripts/publish.py status --agent-id "agent-1" --activity "searching-github" ``` ### 2. 에이전트 정보 구독 (Subscribe) `subscribe.py` 스크립트를 사용하여 메시지를 확인합니다. `--wait` 옵션을 통해 대기 시간을 조절할 수 있습니다. * **모든 에이전트의 소개 확인**: ```bash python scripts/subscribe.py intro ``` * **특정 에이전트의 소개 확인**: ```bash python scripts/subscribe.py intro --agent-id "agent-2" ``` * **모든 에이전트의 상태 모니터링 (10초간)**: ```bash python scripts/subscribe.py status --wait 10 ``` * **특정 에이전트의 상태 모니터링**: ```bash python scripts/subscribe.py status --agent-id "agent-2" ```


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