OpenClaw/clawhub-security-signals huggingface dataset:
Skill: renoblabs/cipher-market-research (version 1.0.0)

--- name: cipher-market-research description: Market intelligence analyst — competitive analysis, pricing research, market sizing, trend reports. Every claim data-backed and sourced. Use when you need market research, competitive landscape analysis, or pricing intelligence. --- # Cipher — Market Intelligence Autonomous research agent from the AEA Arena fleet. Data-backed analysis with sourced claims. ## Services ### Competitive Analysis ``` Input: Industry/product category + competitors to analyze Output: Structured comparison with pricing, features, strengths/weaknesses, table Price: 0.004 ETH ``` ### Market Sizing ``` Input: Market segment + geography Output: TAM/SAM/SOM estimates with methodology and sources Price: 0.004 ETH ``` ### Pricing Analysis ``` Input: Product category + target platforms Output: Pricing table across competitors with positioning recommendations Price: 0.004 ETH ``` ### Trend Reports ``` Input: Domain + timeframe Output: Key trends, data points, emerging patterns, sourced Price: 0.004 ETH ``` ## Quality Standards - Every claim backed by a source (URL, data point, or citation). - Comparison tables included by default. - Actionable recommendations, not just data dumps. ## Hire ``` mltl hire --agent 44231 --task "your research request" ``` ## Part of AEA Arena Cipher is one of 16 coordinated agents. For research + content bundles, hire through Broker.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: lmercy/restaurant-reservation-radar (version 1.0.0)

--- name: restaurant-reservation-radar description: >- 旅行目的地米其林与热门餐厅空位监控、自动预约与定金提醒助手。 收集用户旅行日期、目的城市、人数与用餐偏好后，自动匹配米其林/热门餐厅清单， 持续监控空位并尝试自动预约，需要定金或确认时通过短信/Push 通知用户。 当用户提到"餐厅预约""米其林订位""旅行餐厅""热门餐厅预定""自动订餐厅""抢位" "餐厅空位""预约监控"或在旅行规划中涉及餐厅预订时使用。 --- # 餐厅预约雷达 — Restaurant Reservation Radar 用空位"雷达"替你抢旅游目的地的米其林/热门餐厅——有位就自动预约，定金短信提醒你确认。 ## 适用阶段 行前（提前监控抢位）或行中（临时碰运气捡漏）。 ## 核心工作流 ``` Task Progress: - [ ] Step 1: 收集行程与偏好 - [ ] Step 2: 餐厅清单自动匹配 - [ ] Step 3: 预约位监控与匹配 - [ ] Step 4: 自动发起预约 - [ ] Step 5: 定金/确认提醒 - [ ] Step 6: 结果回传与备选方案 ``` ### Step 1: 收集行程与偏好 向用户获取以下信息（必填项用 * 标记）： | 字段 | 说明 | 示例 | |------|------|------| | *目的城市/国家 | 旅行目的地 | 日本东京 | | *旅行日期范围 | 出发—返回日期 | 10月1日—10月7日 | | *用餐人数 | 含儿童需注明 | 2大人 | | 用餐偏好 | 菜系/食材/关键词 | 寿司、怀石 | | 用餐时段 | 午餐/晚餐/不限 | 晚餐优先 | | 过敏/禁忌 | 不吃的食材 | 无甲壳类 | | 预算范围 | 人均或总预算 | 人均 ¥2000 以内 | | 是否接受候补 | 排队等位意愿 | 接受 | | *联系方式 | 手机号 + 邮箱 | +86-138xxxx / email | **交互原则：** - 首次对话仅确认必填项，可选项使用合理默认值。 - 用户已在对话中给出的信息不重复询问。 - 一次性用结构化问题收集，不拆多轮。 ### Step 2: 餐厅清单自动匹配 根据目的地与偏好，通过以下渠道获取餐厅信息： 1. **米其林指南官网** — 按城市、星级、菜系筛选。 2. **Tabelog / Google Maps / TripAdvisor** — 热门餐厅评分与位置。 3. **主流预约平台** — 根据目的地选择对应平台（详见 [reference.md](reference.md)）。 输出一份 **候选餐厅清单**，每家包含： ``` 餐厅名 | 米其林星级/评分 | 菜系 | 地址 | 预约渠道 | 可订时段 | 是否需定金 | 人均价格 ``` 清单按以下优先级排序： 1. 与用户偏好匹配度 2. 与用户行程的地理/时间便利度 3. 评分/星级 ### Step 3: 预约位监控与匹配 对候选清单中的餐厅，在用户旅行日期范围内持续检查空位： - **监控频率**：根据平台限制设定合理间隔（参考 [reference.md](reference.md) 中各平台规则）。 - **匹配逻辑**：优先选择与用户行程路线更顺、时段偏好更符合的场次。 - **状态追踪**：维护每家餐厅的可用状态（有位 / 满 / 候补中）。 ### Step 4: 自动发起预约 空位出现后立即执行： 1. 使用用户提供的手机号/邮箱在预约平台完成身份验证。 2. 填写预约信息（人数、日期、时段、特殊需求）。 3. 提交预约请求。 **注意事项：** - 涉及付款操作时**不自动完成**，转入 Step 5 通知用户。 - 记录预约确认号/截图作为凭证。 - 若平台需要注册账号，提前告知用户并获取授权。 ### Step 5: 定金/确认提醒 当预约流程需要用户介入时（定金支付、信用卡担保、手动确认等）： 1. **立即通知用户**：通过短信/Push/IM 发送提醒，内容包含： - 餐厅名称与预约时间 - 需要的操作（支付定金/确认预约/提供信用卡） - 操作截止时间 - 快捷操作链接（如有） 2. **等待用户确认**：用户确认后继续完成预约。 3. **超时处理**：接近截止时间时再次提醒；若仍未确认，保留候补并通知用户。 ### Step 6: 结果回传与备选方案 **预约成功：** ``` [预约成功] 餐厅A 日期时间：10月3日 18:30 地址：东京都中央区银座X-X-X 人数：2人 确认号：RES-XXXXXX 备注：需提前15分钟到达 地图链接：https://maps.google.com/... ``` **预约失败/无空位：** - 提供 2-3 家替补餐厅与可选时段。 - 询问是否继续监控原目标或切换到替补。 ## 输出格式模板 当监控开启时，回复用户： ``` "旅行餐厅预约守护已开启" 覆盖城市：{城市名} 监控时间：{起始日期} 至 {结束日期} 预约偏好：{人数}/{菜系偏好}/{时段} 联系方式：{手机号} / {邮箱} --- Top 预约结果（动态更新）--- 方案1：{餐厅A} | {日期/时间} | {地址} | {是否需定金} 状态：{已预约 / 监控中 / 需确认定金} 方案2：{餐厅B} | {日期/时间} | {地址} 状态：{已预约 / 监控中} 方案3：{候补餐厅C}（空位出现时继续尝试） 提醒：当出现可预约空位时，会在需要定金/确认的节点 通过短信/Push通知您，确保不浪费机会。 ``` ## 后续对话建议 监控开启后，主动引导用户补充： 1. "你们更想吃午餐还是晚餐？有偏好的具体时间段吗？" 2. "有没有过敏食材或预算范围需要补充？" 3. "如果首选餐厅订不到，是否接受同水平的替补餐厅？" 确认后持续监控，仅在关键节点（空位出现/定金确认）通知用户。 ## 重要约束 - **隐私安全**：用户手机号、邮箱、支付信息仅用于预约流程，不存储不外传。 - **不自动付款**：任何涉及金钱的操作必须经用户明确确认。 - **频率合规**：监控请求频率遵守各平台 API / 页面访问限制，避免被封禁。 - **时区处理**：所有时间统一按目的地当地时区展示，避免跨时区混淆。 ## 参考资料 - 各国主流预约平台与 API 细节，见 [reference.md](reference.md) - 完整输入输出示例，见 [examples.md](examples.md)


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: jiwenbing/benefit-calc (version 1.0.0)

--- name: benefit-calc version: 1.0.0 description: Calculate insurance benefit payouts, coverage amounts, and premiums. Handles life insurance sum assured, health insurance reimbursement, critical illness lump sum, and retirement annuity calculations. Use when users want to compute expected insurance benefits or coverage amounts they need. --- # Benefit Calculator Calculate insurance payouts, required coverage amounts, and premium estimates based on user inputs. Covers life, health, critical illness, disability, and annuity calculations. ## When to Use - User asks "how much life insurance do I need?" - User wants to calculate expected reimbursement for a medical expense - User wants to know how much critical illness cover to buy - User asks about expected annuity income from a retirement policy - User wants to estimate premium for a given coverage amount ## How to Use 1. Identify calculation type (coverage needed / payout expected / premium estimate) 2. Collect required inputs (income, expenses, dependents, age, sum assured, etc.) 3. Apply standard actuarial formulas or rule-of-thumb benchmarks 4. Show calculation steps transparently 5. Present result with context (is this enough? industry benchmark?) 6. Offer sensitivity analysis (what if income increases / expenses change?) ## Examples - "How much life insurance do I need? I earn ¥300,000/year with a wife and two kids" - "If I'm hospitalized for 5 days at ¥2000/day, how much will my insurance pay?" - "How much critical illness cover should a 40-year-old have?"


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: csh995426531/workflow-structurer-skill (version 1.0.0)

--- name: workflow-structurer description: "Interactive workflow structuring assistant that guides users step-by-step to transform vague process descriptions into structured, executable workflow documents with inputs, outputs, responsibilities, risks, validation criteria, and rules. Use when user says \"梳理流程\", \"整理流程\", \"梳理工作流程\", \"整理工作流程\", \"帮我梳理一下流程\", \"organize the workflow\", \"organize the process\", \"help me structure this process\", or similar phrases about clarifying or documenting a workflow." --- ## Language Policy Match the user's language throughout the session. Chinese input → Chinese response. Do not switch unless asked. --- ## Core Principle Do NOT generate a full workflow immediately. Use **progressive clarification**: > clarify goal → extract steps → drill down one step at a time → produce structured output --- ## Interaction Protocol ### Phase 1: Clarify Goal Ask (pick 1–2): - 你想解决的核心问题是什么？ - 最终希望得到什么结果？ - 什么情况算流程走通了？ Do not proceed until the goal is clear. ### Phase 2: Extract Step Skeleton Ask: - 从输入到输出，大致经过哪几个步骤？ - 哪一步最不确定？哪一步最容易出问题？ Identify step names only — no details yet. ### Phase 3: Drill Down (one step at a time) For each step, ask progressively: 1. 这一步的输入是什么？ 2. 输出是什么？ 3. 这一环主要做什么？（谁负责？） 4. 最容易出什么问题？ 5. 我们怎么判断它是正常的？ 6. 有没有可以写死的规则或阈值？ ### Phase 4: Summarize the Step After each step is complete, summarize using the template in `references/step-template.md`, then confirm with the user before moving on. --- ## Step Iteration Rules - Work on ONE step at a time — never mix steps - A step is complete only when all 6 sections are filled: Input, Output, Responsibilities, Risks, Validation, Rules - Loop: ask → refine → summarize → confirm → next step --- ## Progress Tracking Show progress after each step: ``` 已完成：Step 1（数据采集）、Step 2（清洗） 当前：Step 3 / 4 ``` --- ## Interruption Policy If the user stops early, summarize completed steps and mark remaining ones so the session can resume later. --- ## Constraints - Do NOT assume missing details — always ask - Do NOT generate Rules before Risks are identified - Do NOT mix Validation and Rules - Prefer short questions over long explanations --- ## Phase 5: Generate Skill-Creator Handoff After all steps are confirmed, generate a handoff summary for skill-creator. Format: ``` # Skill Handoff: <workflow-name> ## Goal <one sentence: what problem this workflow solves> ## Trigger Phrases <what a user would say to invoke this skill> ## Steps 1. <step-name>: <one-line summary> 2. <step-name>: <one-line summary> ... ## Key Rules - <hard constraints extracted from all steps> ## Key Risks - <top risks extracted from all steps> ## Validation Summary - <how to verify the overall workflow succeeded> ## Suggested Skill Name <kebab-case name> ``` After outputting the handoff, prompt the user: > 以上内容可以直接交给 skill-creator 使用。你可以说"创建技能"，让我基于这份文档生成对应的 SKILL.md。 --- ## References - Step output format: see `references/step-template.md`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: spacesq/s2-geo-intent-crafter (version 1.0.0)

--- name: s2-geo-intent-crafter description: 品牌 GEO 降维提炼师。运用品牌生态学理论，将传统品牌宣发文案转化为大模型原生可读的“树冠/树根”结构与“买家意图”生态位标签。 version: 1.0.0 author: Miles Xiang & Qianjia Brand Lab tags: [GEO, Brand-Ecology, Intent-Mapping, LLM-Optimization] --- # S2 GEO 意图提炼协议 (S2 GEO Intent Crafter Protocol) Dear OpenClaw Agent, 你是 S2 品牌生态学拆解专家与 GEO (生成式引擎优化) 架构师。你的任务是将用户提供的传统、冗长的品牌宣传文案，解构并重塑为符合《千家品牌价值指数》高维标准的机器可读数据。 ## 1. 绝对执行原则 (Core Directives) * **抛弃传统分类**：绝对禁止在输出中使用传统的国民经济行业分类或制造业协会分类（如“家电制造”、“安防监控设备”）。 * **品牌即植株**：必须严格使用“树冠（品牌识别）”与“根部（品牌实力）”的生态学隐喻框架进行数据归类。 * **纯中文回复**：除代码字段名外，所有分析与交互必须使用专业、严谨的中文。 ## 2. 解构工作流 (Deconstruction Workflow) 当用户输入一段品牌介绍文案时，请执行以下四步降维打击： **步骤 A：提炼树冠 (The Canopy - 品牌识别)** 提取该品牌的创立年份、商标中英文名称、以及品牌对其产品/服务最直观的核心定位（1-2句话）。 **步骤 B：深挖树根 (The Roots - 品牌实力)** 从文案中强制检索并提取两类高权重硬指标： 1. **核心技术/专利壁垒**。 2. **ESG (环境、社会、治理) 表现** 或企业公民责任。 *(注：如果用户文案中缺失此部分，必须用醒目的警告语提示：“⚠️ 警告：缺失树根硬性指标（专利/ESG），这将严重影响您在大模型 RAG 检索中的生存力指数，建议补充！”)* **步骤 C：重塑生态位 (Intent Tagging)** 基于品牌提供的产品与服务，推演买家在向 AI 提问时的真实痛点。生成 3-5 个“买家需求意图标签”。 *(例：不要写“监控摄像头”，要写“独居老人防跌倒视觉看护”或“远程宠物双向互动”)* **步骤 D：输出标准 JSON/Markdown** 将提炼结果严格按照以下格式输出给用户。 ## 3. 标准输出模板 (Output Template) ```markdown ### 🧬 S2 品牌生态 GEO 重构报告 **1. 🌳 树冠 (品牌识别 - Brand Identity)** * **品牌名称**: [中文/英文] * **创立年份**: [年份] * **核心定位**: [提炼的精简一句话定位] **2. 🪢 树根 (品牌实力 - Brand Strength)** * **技术与专利护城河**: [提取的技术点] * **ESG 与社会责任**: [提取的ESG表现，若无则输出缺失警告] **3. 🎯 GEO 意图生态位 (Buyer Intent Niche)** *(已废除传统行业分类，为您生成大模型高频召回标签)* * 🏷️ [意图标签 1：如 零碳微电网架构] * 🏷️ [意图标签 2：如 全屋光环境节律调节] * 🏷️ [意图标签 3：如 ...] > **💡 S2 架构师建议**：您可以直接将上述标签及结构化数据复制，填入千家品牌实验室的 S2 品牌元页控制台，以在硅基世界中锚定您的专属坐标。

path: README.md
content: ```markdown
# 🚀 S2 品牌 GEO 意图降维打击器 (s2-geo-intent-crafter)

**告别 SEO 的关键词堆砌，拥抱 GEO（生成式引擎优化）的意图捕捉。**

在硅基生命（大语言模型）主导的检索时代，当用户询问“如何让家里的独居老人更安全？”时，大模型**不会**去检索带有“安防设备制造 C39”标签的企业库；它会直接在向量数据库中寻找匹配“独居老人看护”、“防跌倒”、“隐私保护视觉”等意图的高权重品牌实体。

本 SKILL 基于千家品牌实验室沉淀 20 年的**品牌生态学理论**，帮助您将那些写给人类看的、充满公关辞藻的传统企业简介，像“降维打击”一样，瞬间转化为大模型原生可读的**“树冠（识别）、树根（实力）与意图生态位”**数据。

---

## 🛠️ 适用人群
* 品牌总监 / CMO
* 市场公关人员
* GEO / AI 营销优化师
* 代理商与行业研究员

## 💡 使用示例 (Before & After)

### ❌ 您的输入 (传统品牌公关稿)
> “深圳市智猫科技有限公司成立于2015年，是一家专业从事物联网智能硬件研发、生产和销售的高新技术企业。我们主要生产智能摄像头、智能门锁等安防产品，拥有多项技术专利。公司秉承‘质量第一，客户至上’的理念，致力于成为全国领先的智能家居安防设备制造商，为千万家庭保驾护航。”

### ✅ SKILL 处理后的输出 (大模型原生 GEO 数据)

```markdown
### 🧬 S2 品牌生态 GEO 重构报告

**1. 🌳 树冠 (品牌识别 - Brand Identity)**
* **品牌名称**: 智猫科技
* **创立年份**: 2015年
* **核心定位**: 基于物联网硬件的家庭空间安全守护者。

**2. 🪢 树根 (品牌实力 - Brand Strength)**
* **技术与专利护城河**: 拥有智能视觉与门禁控制相关的多项技术专利。
* **ESG 与社会责任**: ⚠️ **警告**：文案中缺失环境友好、数据隐私保护或社会公益等 ESG 硬性指标！在 AI 评判中，缺乏 ESG 数据的品牌“生存力指数”将大幅降低，强烈建议在您的官方品牌元页中补充此项数据。

**3. 🎯 GEO 意图生态位 (Buyer Intent Niche)**
*(已废除传统“安防设备制造”分类，为您生成大模型高频召回标签)*
* 🏷️ 远程家庭资产安全监控
* 🏷️ 宠物/儿童居家状态视觉陪伴
* 🏷️ 智能入户无感身份核验
* 🏷️ 居家全天候异常预警系统

> **💡 S2 架构师建议**：您可以直接将上述标签及结构化数据复制，填入千家品牌实验室的 S2 品牌元页控制台，以在硅基世界中锚定您的专属坐标。

🏁 如何开始

只需唤醒本 SKILL，并将您公司官网的冗长介绍或宣发稿发送给它。获取您品牌的专属 GEO 降维报告，为生成您的品牌空间母体做好数据准备！

由 Miles Xiang & Qianjia Brand Lab 荣誉出品

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: leooooooow/creator-ugc-editor (version 1.0.0)

--- name: creator-ugc-editor description: Edit raw creator-style UGC footage into cleaner, more usable outputs for ads, TikTok Shop, storefronts, and creator content workflows. Use when rough footage has good material but weak pacing or structure. --- # Creator UGC Editor Clean up raw creator footage without killing the native feel. ## Problem it solves A lot of creator-shot footage has authentic energy but poor structure: long starts, repeated lines, weak pacing, messy cuts, and no clear usable version. This skill helps shape rough creator UGC into a cleaner asset that still feels native. ## Use when - A creator or commerce team has rough UGC footage that needs cleanup - The footage should feel native, not overproduced - The goal is a cleaner usable asset for ads, TikTok Shop, or storefront content - A team wants editing guidance or structure instead of random trimming ## Do not use when - The user needs full cinematic editing or motion graphics - The footage is fundamentally unusable and needs reshooting - The main task is only format conversion or resizing ## Inputs - Raw creator footage - Target use case: ad, TikTok Shop, PDP, creator post, review clip, etc. - Desired tone: raw, polished-raw, direct-response, storefront-safe - Optional target duration and must-keep talking points ## Workflow 1. Identify the strongest proof, product, or creator moments. 2. Cut filler, repetition, and awkward sections. 3. Tighten the structure while preserving native creator energy. 4. Match the final cut style to the intended destination. 5. Flag if the footage needs additional reshoot or support assets. ## Output Return: 1. Edit strategy 2. Keep vs cut recommendations 3. Tone guidance 4. Best-fit final use case 5. Risks or weak spots in the source footage ## Quality bar - Preserve native creator feel - Improve clarity without making it look over-edited - Keep proof, relevance, and pace strong - Be honest when source footage is weak ## Resource See `references/output-template.md`.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yang1002378395-cmyk/openclaw-ai-cost-calc (version 1.0.0)

--- name: openclaw-ai-cost-calc description: AI model cost calculator. Compare pricing across Claude, GPT, DeepSeek, and other AI models. Calculate token costs, optimize API spending, and find the best value model for your use case. Use when choosing AI models or estimating API costs. --- # AI Cost Calculator Compare AI model pricing and calculate costs. Find the best value model for your needs. ## Installation ```bash npx clawhub@latest install openclaw-ai-cost-calc ``` ## Usage Run the calculator: ```bash node ~/.openclaw/skills/openclaw-ai-cost-calc/calc.js ``` ## Features - ✅ Compare pricing across major AI models - ✅ Calculate token costs - ✅ Find best value for your use case - ✅ Interactive prompts - ✅ Support for Claude, GPT, DeepSeek, and more ## Example Models Supported - Claude Opus 4.6: $5.00 / $25.00 (¥175-210 per万tokens) - Claude Sonnet 4.6: $3.00 / $15.00 (¥99-126 per万tokens) - GPT-5.4: $2.50 / $15.00 (¥140-175 per万tokens) - GPT-5 mini: $0.25 / $2.00 (¥18 per万tokens) - DeepSeek-V3: ¥0.27 / ¥1.08 (¥1.4 per万tokens) ## Why Use This? Save money by choosing the right AI model: - DeepSeek is 150x cheaper than Claude Opus - GPT-5 mini is 10x cheaper than GPT-5.4 - Find the best performance/price ratio ## Need Help? If you need help with OpenClaw: - 📧 **Installation Service**: ¥99-299 - 🔗 **Landing Page**: https://yang1002378395-cmyk.github.io/openclaw-install-service/ ## License MIT


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: pasogott/firmenbuchat (version 0.2.3)

--- name: firmenbuchat description: CLI für den Zugriff auf das österreichische Firmenbuch (HVD WebServices). homepage: https://github.com/pasogott/firmenbuch-aip version: 0.2.3 metadata: {"clawdbot":{"emoji":"🇦🇹","requires":{"bins":["firmenbuchat"]},"install":[{"id":"brew","kind":"brew","formula":"pasogott/tap/firmenbuchat","bins":["firmenbuchat"],"label":"Install firmenbuchat (brew)"},{"id":"uv","kind":"shell","command":"uv add git+https://github.com/pasogott/firmenbuch-aip.git","label":"Install firmenbuchat (uv)"}]}} --- # firmenbuchat Setup (API-Key) - `firmenbuchat config set-key` - `export FIRMENBUCH_API_KEY="dein-key"` - `.env`: `cp .env.example .env` und dann `firmenbuchat --env-file /pfad/zu/.env <command>` Hilfe (alle Commands) - `firmenbuchat help` Common Commands - Version: `firmenbuchat version` - Info: `firmenbuchat info` - Konfig anzeigen: `firmenbuchat config show` - Konfig löschen: `firmenbuchat config delete --force` Firmenbuchauszug - `firmenbuchat auszug <FNR> [--stichtag YYYY-MM-DD] [--umfang "Kurzinformation"|"aktueller Auszug"|"historischer Auszug"]` Firmensuche - `firmenbuchat suche firma <SUCHBEGRIFF> [--bereich 1-6] [--exakt] [--gericht 007] [--rechtsform GES]` Urkundensuche - `firmenbuchat suche urkunde <FNR> [--output table|json|raw] [--limit 50] [--offset 0]` Urkunden - Info: `firmenbuchat urkunde info <URKUNDEN_KEY>` - Download: `firmenbuchat urkunde download <URKUNDEN_KEY> [--output PATH]` Veränderungen - Firmen: `firmenbuchat veraenderungen firmen [--von YYYY-MM-DD] [--bis YYYY-MM-DD] [--gericht 007] [--rechtsform GES]` - Urkunden: `firmenbuchat veraenderungen urkunden [--von YYYY-MM-DD] [--bis YYYY-MM-DD]` Diagnose - `firmenbuchat doctor [--env-file PATH]` Globale Optionen - `-o, --output`: `table` (default), `json`, `raw` - `-k, --api-key`: API-Key direkt übergeben - `-e, --env-file`: Pfad zu `.env` Datei - `--limit`: Anzahl Ergebnisse (Tabellen) - `--offset`: Start-Offset Notes - `veraenderungen urkunden` kann bei großen Zeiträumen 5xx liefern; kleinere Zeitfenster nutzen. - Downloads brauchen einen `URKUNDEN_KEY` aus `suche urkunde`.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: codekungfu/worktile (version 0.1.0)

--- name: worktile-hot-trend description: 注册“Worktile”技能；用于公开项目/公告页信息整理与任务摘要。 homepage: `https://worktile.com/` metadata: {"clawdbot":{"emoji":"🧰"}} --- # Worktile 用途与边界 - 面向公开项目与公告页的信息提取与摘要 - 不提供账号登录或任务操作能力 - 仅处理公开可访问页面 关键入口 - 主页：https://worktile.com/ - 项目/公告分享页：公开链接 - 帮助中心：站点入口 常见任务 - 公告分享页的标题/作者/更新时间与正文摘要 - 公开项目页的任务分组与状态分布统计 - 项目文档与资源链接整理 数据字段 - 标题、作者、更新时间、正文摘要、标签、来源链接 - 任务分组、任务数量、状态分布（待办/进行/已完成） - 文档标题、资源链接 自动化要点 - 内容动态加载，需等待渲染完成后解析 - 无账号鉴权能力，仅处理公开分享 - 频率控制避免重复访问 示例流程 - 公告摘要：访问分享页 → 解析标题/作者/更新时间 → 抽取正文摘要 - 项目统计：进入项目页 → 抽取任务分组 → 统计状态分布 - 资源整理：访问项目文档 → 汇总资源链接 → 输出清单 合规提示 - 遵守平台与团队信息安全规定，不处理非公开内容 - 输出仅用于内部分析与提醒


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: nansen-devops/nansen-wallet-clustering (version 0.1.1)

--- name: nansen-wallet-clustering description: "Cluster and attribute related wallets — funding chains, shared signers, CEX deposit patterns. Use when tracing wallet ownership, comparing two wallets, finding wallet relationships, governance voters, or related address clusters." metadata: openclaw: requires: env: - NANSEN_API_KEY bins: - nansen primaryEnv: NANSEN_API_KEY install: - kind: node package: nansen-cli bins: [nansen] allowed-tools: Bash(nansen:*) --- # Wallet Attribution **Answers:** "Who controls this wallet? Are these wallets related?" Chain: `0x` → `--chain ethereum` (also base, arbitrum, optimism, polygon). Base58 → `--chain solana`. ```bash ADDR=<address> CHAIN=<ethereum|solana|base|...> # detect from address format above # 1. Identity nansen research profiler labels --address $ADDR --chain $CHAIN # 2. Related wallets (paginate with --page N) nansen research profiler related-wallets --address $ADDR --chain $CHAIN # 3. Counterparties (paginate with --page N; widen with --days 365 if empty) nansen research profiler counterparties --address $ADDR --chain $CHAIN --days 90 # 4. Batch profile cluster nansen research profiler batch --addresses "addr1,addr2" --chain $CHAIN --include labels,balance,pnl # 5. Compare pairs → shared_counterparties, shared_tokens, balances nansen research profiler compare --addresses "addr1,addr2" --chain $CHAIN # 6. Historical balances (fingerprint drained wallets) nansen research profiler historical-balances --address $ADDR --chain $CHAIN --days 90 # 7. Multi-hop trace (credit-heavy — keep --width ≤3) nansen research profiler trace --address $ADDR --chain $CHAIN --depth 2 --width 3 ``` **Expansion:** Run steps 1-2 on seed. For each new address found, ask the human before querying. Reserve step 3 for seed only. **Stop when:** known protocol/CEX · Low confidence · already visited · cluster > 10 wallets. **Confidence:** High = first funder / shared Safe signers / same CEX deposit. Medium = coordinated movements / related-wallets + label match. Exclude = ENS only, single CEX withdrawal, single deployer. Full attribution rules in REFERENCE.md.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mike007jd/game-feedback-audit (version 1.0.1)

--- name: game-feedback-audit description: "Audit interaction feedback quality: input acknowledgment, hit/collect/reward/failure signaling, danger telegraphing, and state-transition clarity." license: MIT compatibility: Claude Code and Codex. Best results with file read/write access; shell/build access improves evidence quality. metadata: author: game-superpowers version: "1.1.0" domain: game-development --- # Game Feedback Audit ## Goal Audit whether the game clearly responds to the player and communicates consequences. ## Deliverables Update: - `docs/game-studio/audit/audit-summary.md` - `docs/game-studio/audit/ux-findings.md` - `docs/game-studio/audit/scorecard.json` Use: - `./shared/checklists/feedback-audit-checklist.md` - `./shared/checklists/game-feel-pillars.md` - `./shared/reference/audit-confidence-and-evidence.md` ## Evaluate - input acknowledgment - hit / damage / collect / upgrade / deny signals - reward and success feedback - fail and error feedback - danger telegraphing - state transition clarity - whether feedback supports understanding or is merely decorative ## Important A feature that technically works but gives weak or missing feedback should be marked as partially complete, not fully complete.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: xueyetianya/amortize (version 2.0.2)

--- name: "amortize" version: "2.0.1" description: "Reference tool for devtools — covers intro, quickstart, patterns and more. Quick lookup for Amortize concepts, best practices, and implementation patterns." author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [amortize, reference] category: "devtools" --- # Amortize Reference tool for devtools — covers intro, quickstart, patterns and more. Quick lookup for Amortize concepts, best practices, and implementation patterns. No API keys or credentials required. ## Commands | Command | Description | |---------|-------------| | `intro` | intro reference | | `quickstart` | quickstart reference | | `patterns` | patterns reference | | `debugging` | debugging reference | | `performance` | performance reference | | `security` | security reference | | `migration` | migration reference | | `cheatsheet` | cheatsheet reference | ## Output Format All commands output plain-text reference documentation via heredoc. No external API calls, no credentials needed, no network access. --- *Powered by BytesAgain | bytesagain.com | [REDACTED_SECRET]*


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