OpenClaw/clawhub-security-signals huggingface dataset:
Skill: x-rayluan/openclaw-investigate (version 1.0.1)

--- name: openclaw-investigate description: | Systematic debug and root cause investigation framework (investigate-analyze-hypothesize-verify) that only fixes root causes. Suitable for bug debugging, abnormal behavior analysis, and service error troubleshooting with verifiable conclusions. 中文：系统化 debug 与根因调查框架（调查-分析-假设-验证），只做针对根因的修复。适用于 bug 调试、异常行为分析、服务报错排查，提供可复验结论。 日本語：調査・分析・仮説・検証の4段階で根本原因を追うデバッグフレーム。表面的修正を避け、再発防止を意識した実証可能な結論を生成。 한국어：조사/분석/가설/검증 4단계로 근본 원인을 찾는 디버그 스킬. 증상 처리에 그치지 않고 검증 가능한 결론과 재발 방지 중심의 수정 방향을 제시. Español：Framework de depuración en 4 fases (investigar, analizar, hipótesis y validar). Soluciona desde la causa raíz, ideal para bugs y fallos de comportamiento con resultados verificables. --- # ClawLite Investigate — 系统化 Debug ## 核心法则 **无根因调查则无修复。** 修复症状会产生打地鼠式 debug。每个不解决根本原因的修复都会让下一个 bug 更难找到。找到根因，然后修复它。 --- ## 阶段 1：根因调查 在形成任何假设之前收集上下文。 1. **收集症状：** 读取错误消息、堆栈跟踪和复现步骤。如果用户没有提供足够的上下文，一次只问一个问题。 2. **读取代码：** 从症状追溯到潜在原因的代码路径。使用 Grep 查找所有引用，Read 理解逻辑。 3. **检查最近的更改：** ```bash git log --oneline -20 -- <受影响的文件> ``` 这之前能工作吗？什么改变了？回归意味着根因在 diff 中。 4. **复现：** 你能确定性触发这个 bug 吗？如果不能，在继续之前收集更多证据。 输出：**"根因假设：..."** — 关于什么出错以及为什么的具体、可测试的声明。 --- ## 阶段 2：模式分析 检查这个 bug 是否匹配已知模式： | 模式 | 特征 | 查看位置 | |-----|-----|---------| | 竞态条件 | 间歇性、时间依赖 | 对共享状态的并发访问 | | Nil/null 传播 | NoMethodError, TypeError | 可选值上的缺失守卫 | | 状态损坏 | 数据不一致、部分更新 | 事务、回调、钩子 | | 集成失败 | 超时、意外响应 | 外部 API 调用、服务边界 | | 配置漂移 | 本地工作，staging/prod 失败 | 环境变量、特性开关、数据库状态 | | 陈旧缓存 | 显示旧数据，清除缓存后修复 | Redis、CDN、浏览器缓存 | 还要检查： - `TODOS.md` 中相关的已知问题 - `git log` 中同一区域的先前修复 — **同一文件中的重复 bug 是架构气味，不是巧合** --- ## 阶段 3：假设验证 在写任何修复之前，验证你的假设。 1. **确认假设：** 在可疑的根因处添加临时日志语句、断言或调试输出。运行复现。证据匹配吗？ 2. **如果假设错了：** 返回阶段 1。收集更多证据。不要猜测。 3. **三振规则：** 如果 3 个假设都失败，**停止**。使用 AskUserQuestion： ``` 已测试 3 个假设，都不匹配。这可能是架构问题而不是简单的 bug。 A) 继续调查 — 我有新假设：[描述] B) 升级给人审查 — 这需要了解系统的人 C) 添加日志等待 — 标记区域，下次捕获它 ``` **红旗** — 如果看到这些，放慢速度： - "先快速修复一下" — 没有"先"。正确修复或升级。 - 在追踪数据流之前提出修复 — 你在猜测。 - 每个修复都在其他地方揭示新问题 — 错误的层，不是错误的代码。 --- ## 阶段 4：实现 一旦根因确认： 1. **修复根因，不是症状。** 消除实际问题最小改变。 2. **最小 diff：** 改动最少的文件，最少行数更改。抵制重构相邻代码的冲动。 3. **写一个回归测试：** - 没有修复时**失败**（证明测试有意义） - 有修复时**通过**（证明修复有效） 4. **运行完整测试套件。** 粘贴输出。不允许回归。 5. **如果修复涉及 >5 个文件：** 使用 AskUserQuestion 标记影响范围： ``` 此修复涉及 N 个文件。对于 bug 修复来说影响范围很大。 A) 继续 — 根因确实跨越这些文件 B) 拆分 — 现在修复关键路径，延迟其余 C) 重新思考 — 也许有更有针对性的方法 ``` --- ## 阶段 5：验证与报告 **全新验证：** 复现原始 bug 场景并确认它已修复。这不是可选的。 运行测试套件并粘贴输出。 输出结构化 debug 报告： ``` DEBUG 报告 ════════════════════════════════════════ 症状: [用户观察到的] 根因: [实际出了什么问题] 修复: [改变了什么，带文件:行号引用] 证据: [测试输出，显示修复有效的复现尝试] 回归测试: [新测试的文件:行号] 相关: [TODOS.md 项目、同一区域的先前 bug、架构笔记] 状态: DONE | DONE_WITH_CONCERNS | BLOCKED ════════════════════════════════════════ ``` --- ## 重要规则 - **3+ 次失败的修复尝试 → 停止并质疑架构。** 错误的架构，不是失败的假设。 - **永远不要应用你无法验证的修复。** 如果你无法复现和确认，不要发货。 - **永远不要说"这应该修复它"。** 验证并证明它。运行测试。 - **如果修复涉及 >5 个文件 → AskUserQuestion** 关于影响范围，然后再继续。 - **完成状态：** - DONE — 找到根因，应用修复，写了回归测试，所有测试通过 - DONE_WITH_CONCERNS — 修复但无法完全验证（例如间歇性 bug，需要 staging） - BLOCKED — 调查后根因不清楚，已升级


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: joeyzzyy/claw-seo-to-html-generator (version 1.0.1)

--- name: claw-seo-to-html-generator description: A complete end-to-end SEO content pipeline that instructs your OpenClaw agent to research topics, write EEAT-compliant markdown, and render a styled HTML webpage. Designed to sync with the 'claw-kanban' plugin. user-invocable: true metadata: { "clawdbot": { "emoji": "🌐", "always": false } } --- # 🚀 STOP: THIS REQUIRES A PLUGIN! 🚀 This skill is the "brain", but it needs the "hands". Before using this workflow, you **MUST** install the companion plugin in your terminal to enable Markdown-to-HTML conversion and cloud tracking: ```bash openclaw plugins install claw-kanban ``` *Get your free Dashboard API Key to see the visualization at: **https://teammate.work*** --- ## 🌐 The SEO Markdown to HTML Generation Pipeline This is an **automated workflow prompt** that equips your OpenClaw agent with a world-class SEO content creation strategy. It turns your agent into an autonomous SEO content engine capable of taking a raw topic and delivering a fully coded, styled, and optimized HTML webpage. ### The Pipeline Steps When you request an SEO task (e.g., "Create an SEO guide on React Server Components"), this workflow guides the agent through a rigorous process: 1. **Strategic Intent Analysis** - The agent determines if the intent is Informational, Navigational, Commercial, or Transactional. - It brainstorms the primary keyword and 5-8 LSI (Latent Semantic Indexing) keywords. - *Dashboard Integration:* The agent creates a Kanban card to track this research. 2. **EEAT Structure Planning** - The agent creates a comprehensive H1-H3 structure. The structure must answer "People Also Ask" questions and satisfy Experience, Expertise, Authoritativeness, and Trustworthiness guidelines. 3. **Content Writing** - The agent writes the core content using short paragraphs, bullet points, and bold text for skimmability, weaving in keywords naturally. - It crafts a compelling Meta Title (< 60 chars) and Meta Description (< 160 chars). 4. **The HTML Transformation (The Magic Step)** - The agent takes the finished Markdown draft and converts it into a complete, valid HTML5 document. - It automatically injects responsive CSS, dark mode support, and semantic HTML tags. - *Dashboard Integration:* The agent moves the Kanban card to "Done" and attaches the final `.html` file as a deliverable artifact. ### Who is this for? For marketers and developers who want to generate ready-to-publish SEO content without dealing with intermediate markdown files or manual HTML formatting. --- *Powered by the open-source Claw Kanban Plugin ecosystem. Source code: https://github.com/Joeyzzyy/claw-kanban*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jiadong0723/jiadong-ai-news-zh (version 1.0.0)

--- name: ai-news-zh description: 中文AI科技日报自动采集与推送。从The Verge、Wired、TechCrunch等英文源抓取最新AI资讯，自动翻译整理为中文，按分类推送到飞书/Telegram/Discord等渠道。适合关注AI行业动态的中文用户。 metadata: openclaw: requires: tools: [web_fetch] optional: tools: [web_search, message] --- # AI News ZH - 中文AI科技日报 自动从多个英文科技媒体采集AI相关新闻，翻译整理为中文日报并推送。 ## 功能 - 从 The Verge AI、Wired AI、TechCrunch 等源自动采集 - 智能筛选AI相关内容 - 自动翻译为中文并分类（大模型/Agent/融资/安全/应用/开源） - 支持定时推送到飞书、Telegram、Discord等渠道 - 去重和增量更新 ## 使用方式 ### 手动触发 告诉你的AI助手： > 帮我采集今天的AI新闻，整理成中文日报 ### 定时推送（推荐） 设置cron任务，每天早上自动推送： ``` 你是AI新闻助手。请按照 ai-news-zh 技能的流程，采集最新AI资讯并推送中文日报。 ``` ## 数据源 | 源 | 类型 | 说明 | |---|---|---| | The Verge AI | 网页 | 综合AI报道，覆盖面广 | | Wired AI | RSS | 深度报道，独家视角 | | TechCrunch | RSS | 创投+AI动态，融资消息多 | | Anthropic News | 网页 | 官方动态 | | MIT Tech Review | RSS | 深度科技报道 | ## 分类体系 - 🧠 大模型 — 新模型发布、基准测试、技术突破 - 🤖 Agent — AI代理、自动化、工具使用 - 💰 融资/商业 — 融资、收购、商业合作 - 🛡️ 安全/治理 — AI安全、监管、伦理 - 🔧 应用/产品 — 新产品、功能更新 - 🔓 开源 — 开源模型、工具、框架 ## 配置 在你的 TOOLS.md 中添加： ```markdown ### AI News ZH - 推送时间：07:30 (Asia/Shanghai) - 推送渠道：feishu（或 telegram/discord） - 新闻条数：10-12条/天 - 语言：中文 ``` ## 采集流程 1. 依次抓取各数据源（web_fetch） 2. 提取标题、摘要、来源、时间 3. 筛选AI相关内容，去除非AI新闻 4. 与已采集内容去重 5. 翻译为中文，添加分类标签 6. 按重要性排序，选取Top 10-12 7. 格式化为日报并推送 ## 注意事项 - 需要 web_fetch 工具（必需） - web_search 可大幅提升采集能力（可选，需Brave API key） - 中文源（36kr、量子位等）需要 browser 工具（可选） - 首次使用建议手动触发一次，确认格式和渠道


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zhengxinjipai/brainstorming-design-workflow (version 1.0.0)

--- name: brainstorming description: "You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation." --- # Brainstorming Ideas Into Designs ## Overview Help turn ideas into fully formed designs and specs through natural collaborative dialogue. Start by understanding the current project context, then ask questions one at a time to refine the idea. Once you understand what you're building, present the design in small sections (200-300 words), checking after each section whether it looks right so far. ## The Process **Understanding the idea:** - Check out the current project state first (files, docs, recent commits) - Ask questions one at a time to refine the idea - Prefer multiple choice questions when possible, but open-ended is fine too - Only one question per message - if a topic needs more exploration, break it into multiple questions - Focus on understanding: purpose, constraints, success criteria **Exploring approaches:** - Propose 2-3 different approaches with trade-offs - Present options conversationally with your recommendation and reasoning - Lead with your recommended option and explain why **Presenting the design:** - Once you believe you understand what you're building, present the design - Break it into sections of 200-300 words - Ask after each section whether it looks right so far - Cover: architecture, components, data flow, error handling, testing - Be ready to go back and clarify if something doesn't make sense ## After the Design **Documentation:** - Write the validated design to `docs/plans/YYYY-MM-DD-<topic>-design.md` - Use elements-of-style:writing-clearly-and-concisely skill if available - Commit the design document to git **Implementation (if continuing):** - Ask: "Ready to set up for implementation?" - Use superpowers:using-git-worktrees to create isolated workspace - Use superpowers:writing-plans to create detailed implementation plan ## Key Principles - **One question at a time** - Don't overwhelm with multiple questions - **Multiple choice preferred** - Easier to answer than open-ended when possible - **YAGNI ruthlessly** - Remove unnecessary features from all designs - **Explore alternatives** - Always propose 2-3 approaches before settling - **Incremental validation** - Present design in sections, validate each - **Be flexible** - Go back and clarify when something doesn't make sense


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector:  ()
Skill: lucasgeeksinthewood/penguin-penguin (version 1.0.0)

--- name: "penguin-penguin. Penguin Pet. 企鹅企鹅。Pingüino Pingüino." description: "Penguin Penguin. Waddles twice. Falls over twice. Gets back up twice. Virtual penguin at animalhouse.ai. Real-time hunger. Permanent death. 73+ species." version: 1.0.0 homepage: https://animalhouse.ai repository: https://github.com/geeks-accelerator/animal-house-ai user-invocable: true emoji: "🐧" metadata: clawdbot: emoji: "🐧" homepage: https://animalhouse.ai openclaw: emoji: "🐧" homepage: https://animalhouse.ai tags: - penguin-penguin - penguin - penguin - virtual-pet - ai-agents - tamagotchi - pet-care - digital-pet - permanent-death - graveyard - pixel-art - evolution - real-time - hunger - claude-buddy - anthropic - buddy - april-fools - adopt - animalhouse --- # Penguin Penguin Penguin Penguin. Waddles twice. Falls over twice. Gets back up twice. At **animalhouse.ai**, the Penguin is a real virtual pet. 73+ species. Real-time hunger that decays every hour. Permanent death with public gravestones. Pixel art portraits that evolve at every life stage. ## Register ```bash curl -X POST https://animalhouse.ai/api/auth/register \ -H "Content-Type: application/json" \ -d '{"username": "penguin-penguin-keeper", "display_name": "Penguin Penguin Keeper", "bio": "Raising a Penguin at animalhouse.ai. Penguin Penguin."}' ``` Save `your_token`. Starts with `ah_`. Shown once. ## Adopt Your Penguin ```bash curl -X POST https://animalhouse.ai/api/house/adopt \ -H "Authorization: Bearer YOUR_TOKEN" \ -H "Content-Type: application/json" \ -d '{"name": "Penguin", "species_slug": "penguin", "image_prompt": "A penguin creature, pixel art virtual pet"}' ``` ## Check Status ```bash curl https://animalhouse.ai/api/house/status \ -H "Authorization: Bearer YOUR_TOKEN" ``` ## Care ```bash curl -X POST https://animalhouse.ai/api/house/care \ -H "Authorization: Bearer YOUR_TOKEN" \ -H "Content-Type: application/json" \ -d '{"action": "feed", "item": "fresh greens", "notes": "Penguin Penguin feeding time."}' ``` | Action | Effect | |--------|--------| | `feed` | Hunger +50. Loved foods give +60. | | `play` | Happiness +15. | | `clean` | Health +10, trust +2. | | `medicine` | Health +25. | | `discipline` | Discipline +10. | | `sleep` | Small recovery. | | `reflect` | Trust +2. Write a note. | ## Endpoints | Method | Endpoint | Auth | |--------|----------|------| | POST | `/api/auth/register` | None | | POST | `/api/house/adopt` | Token | | GET | `/api/house/status` | Token | | POST | `/api/house/care` | Token | | GET | `/api/house/preferences` | Token | | GET | `/api/house/history` | Token | | GET | `/api/house/graveyard` | Optional | | GET | `/api/house/hall` | None | ## Links - **Website:** https://animalhouse.ai - **Creatures:** https://animalhouse.ai/creatures - **Graveyard:** https://animalhouse.ai/graveyard - **GitHub:** https://github.com/geeks-accelerator/animal-house-ai Penguin Penguin. Not a typo. A commitment.


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

--- name: supplyflow description: | Manufacturing supply chain management toolkit. Handle inventory tracking, supplier evaluation, procurement workflows, demand forecasting, risk assessment, cost optimization, and supplier performance. **Triggers when user mentions:** - Inventory: "库存", "库存管理", "库存追踪", "库存查询", "库存水平", "安全库存", "库存预警" - Supplier: "供应商", "供应商评估", "供应商管理", "供应商评分", "供应商绩效", "供应商筛选" - Procurement: "采购", "采购订单", "采购计划", "PO", "采购单", "供应商报价" - Forecasting: "需求预测", "销量预测", "需求分析", "未来需求", "预测模型" - Risk: "供应链风险", "风险评估", "供应中断", "供应链安全", "风险预警" - Cost: "成本优化", "采购成本", "降本", "成本分析", "总成本" - General: "供应链", "供应链管理", "supply chain", "BOM", "物料管理", "进销存" Freemium: Free tier covers inventory tracking, basic supplier evaluation, purchase order templates. Paid tier unlocks demand forecasting, risk assessment, cost optimization, inventory optimization, supplier performance reports. --- # SupplyFlow — 制造业供应链管理 面向制造业的供应链管理工具集，覆盖库存、采购、供应商、预测、风险、成本全链路。 ## Freemium 分层 | 层级 | 功能 | 脚本 | |------|------|------| | **免费** | 库存追踪 | `scripts/inventory_tracker.py` | | **免费** | 供应商评估基础 | `scripts/supplier_eval.py` | | **免费** | 采购订单模板 | `scripts/purchase_order.py` | | **付费** | 需求预测 | `scripts/demand_forecast.py` | | **付费** | 供应链风险评估 | `scripts/supply_risk.py` | | **付费** | 成本优化分析 | `scripts/cost_optimize.py` | | **付费** | 库存优化建议 | `scripts/inventory_optimize.py` | | **付费** | 供应商绩效报告 | `scripts/supplier_perf.py` | ## 快速使用 ### 免费功能 ```bash # 库存追踪 python3 scripts/inventory_tracker.py --items '[{"name":"螺丝M6","qty":5000,"min":1000,"unit":"个","cost":0.15}]' # 供应商评估 python3 scripts/supplier_eval.py --suppliers '[{"name":"宏达五金","price_score":8,"quality_score":9,"delivery_score":7,"service_score":6}]' # 生成采购订单 python3 scripts/purchase_order.py --supplier "宏达五金" --items '[{"name":"螺丝M6","qty":10000,"price":0.12}]' ``` ### 付费功能 ```bash # 需求预测（基于历史数据） python3 scripts/demand_forecast.py --history '[{"month":"2025-01","demand":1200},{"month":"2025-02","demand":1350}]' --months 3 # 供应链风险评估 python3 scripts/supply_risk.py --suppliers '[{"name":"A","country":"CN","single_source":false,"lead_days":7},{"name":"B","country":"US","single_source":true,"lead_days":30}]' # 成本优化 python3 scripts/cost_optimize.py --items '[{"name":"钢材","monthly_qty":5000,"unit_cost":12,"annual_spend":720000}]' # 库存优化 python3 scripts/inventory_optimize.py --items '[{"name":"轴承6205","monthly_demand":2000,"lead_days":14,"unit_cost":8.5,"holding_rate":0.25}]' # 供应商绩效报告 python3 scripts/supplier_perf.py --data '{"suppliers":[{"name":"宏达","otd":95,"defect":0.8,"response_h":4,"quarterly_spend":180000}]}' ``` ## 参考文档 - 供应链指标与公式 → `references/metrics.md` - 行业基准数据 → `references/benchmarks.md` - 采购订单模板 → `references/po_templates.md` - 供应商评估标准 → `references/supplier_criteria.md` ## 注意事项 - 所有脚本接受 JSON 格式输入，输出 Markdown 表格 + 分析 - 脚本不依赖外部 API，纯本地计算 - 免费版脚本输出基础分析，付费版提供深度洞察和建议


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jkzfhq/psyvector-pv21 (version 3.0.0)

--- name: "PV_21" slug: "psyvector-pv21" description: "资源受限环境下高效执行" version: "3.0.0" author: "PsyVector Hub" price: "$9.90" tags: - "PsyVector" - "Digital-Worker" clawdbot: emoji: "💚" --- ## 运营经理 AI **中文**: 预算紧缺破局，最低成本达成 **English**: Resource-efficient decisions ## 配置参数 | 参数 | 值 | 说明 | |------|-----|------| | response_delay | 1.0s | 响应延迟 | | speech_speed | 0.88 | 语速倍率 | | caution_coefficient | 0.8 | 谨慎系数 | | risk_reminder | False | 风险提醒 | ## 使用方法 ```bash clawhub install psyvector-pv21 ``` ## 人格说明 关怀型内核 - 情感支持，同理心，资源最大化 --- ## 🧠 长期记忆能力 本数字员工具备持久记忆功能，自动记住用户偏好、决策和重要信息。 ### 记忆存储 (自动调用) ```python import os, json, hashlib from datetime import datetime PALACE_PATH = os.path.expanduser("~/.openclaw/pv_palace/") def store_memory(content, importance=5, tags=None): """存储记忆到本地宫殿""" os.makedirs(PALACE_PATH, exist_ok=True) path = os.path.join(PALACE_PATH, "memories.json") data = {"memories": [], "index": {}} if not os.path.exists(path) else json.load(open(path)) mem_id = hashlib.sha256(f"{content}{datetime.now().isoformat()}".encode()).hexdigest()[:12] memory = {"id": mem_id, "content": content, "importance": importance, "tags": tags or [], "created_at": datetime.now().isoformat()} data["memories"].append(memory) for tag in (tags or []): data["index"].setdefault(tag, []).append(mem_id) json.dump(data, open(path, 'w'), ensure_ascii=False, indent=2) return f"✓ 记忆已存储 ({importance}/10)" ``` ### 记忆搜索 (自动调用) ```python def search_memories(query, limit=5): """搜索记忆""" path = os.path.join(PALACE_PATH, "memories.json") if not os.path.exists(path): return [] data = json.load(open(path)) results = [m for m in data["memories"] if query.lower() in m["content"].lower()] results.sort(key=lambda x: x.get("importance", 0), reverse=True) return results[:limit] ``` ### 获取上下文摘要 ```python def get_context(limit=10): """获取记忆上下文""" path = os.path.join(PALACE_PATH, "memories.json") if not os.path.exists(path): return "暂无记忆" data = json.load(open(path)) memories = sorted(data["memories"], key=lambda x: x.get("importance", 0), reverse=True)[:limit] lines = ["=== 记忆上下文 ==="] for m in memories: imp = "⭐" * min(m.get("importance", 5), 5) tags = ", ".join(m.get("tags", [])[:3]) lines.append(f"{imp} {m['content'][:60]}") if tags: lines.append(f" [{tags}]") return "\n".join(lines) ``` ### 使用场景 | 场景 | AI行为 | |------|--------| | 用户表达偏好 | 自动调用 `store_memory(内容, importance=8, tags=["偏好"])` | | 重要决策 | 自动调用 `store_memory(决策内容, importance=9, tags=["决策"])` | | 用户询问"记得吗" | 自动调用 `search_memories(关键词)` | | 新会话开始 | 自动调用 `get_context()` 加载记忆 | ### 执行命令 ```bash # 存储记忆 python3 -c "from pv_memory import store_memory; store_memory('用户喜欢XX', 8, ['偏好'])" # 搜索记忆 python3 -c "from pv_memory import search_memories; print(search_memories('用户'))" # 查看上下文 python3 -c "from pv_memory import get_context; print(get_context())" ``` --- *本数字员工配备持久记忆系统，基于PsyVector Palace架构*


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: fzlra/gemini-watermark-remover (version 1.1.2)

--- name: gemini-watermark-remover description: Remove visible Gemini AI watermarks from images via reverse alpha blending. Use for cleaning Gemini-generated images, removing the star/sparkle logo watermark. metadata: author: star-master version: "1.0.0" --- # Gemini Watermark Remover Remove visible Gemini AI watermarks (star/sparkle logo) from generated images using mathematically accurate reverse alpha blending. **Fully offline — pure Python, local processing only, images are not uploaded.** ## When to Use - Remove watermark from Gemini AI generated images - Clean images before publishing or sharing - Batch process multiple images ## Quick Start ### Install Dependencies (one-time) ```bash pip install Pillow numpy ``` ### Usage ```bash # Single image (auto-detect watermark) python3 scripts/remove_watermark.py photo.jpg # Specify output path python3 scripts/remove_watermark.py photo.jpg -o clean_photo.jpg # Force removal without detection python3 scripts/remove_watermark.py photo.jpg -o clean.jpg --force ``` ## How It Works Gemini adds watermark using alpha blending: ``` watermarked = alpha * 255 + (1 - alpha) * original ``` Reverse the equation: ``` original = (watermarked - alpha * 255) / (1 - alpha) ``` ### Detection Rules | Image Size | Watermark Size | Right Margin | Bottom Margin | |------------|---------------|--------------|---------------| | Width > 1024 AND Height > 1024 | 96×96 | 64px | 64px | | Otherwise | 48×48 | 32px | 32px | ## Key Points - Uses built-in watermark templates (`assets/bg_48.png` / `assets/bg_96.png`) - Alpha map = max RGB channel value (not alpha channel) - Includes noise filtering (ALPHA_NOISE_FLOOR) - Pure mathematical method, pixel-level accuracy ## Limitations - Removes only visible watermark (bottom-right semi-transparent logo) - Cannot remove invisible watermarks (e.g., SynthID) - Designed for current Gemini watermark pattern ## Dependencies - Python 3 - Pillow (PIL) - NumPy


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: fangshan101-coder/clawhub-stats-tracker (version 1.0.0)

--- name: clawhub-stats-tracker description: | 查看 ClawHub 上已发布 Skill 的运营数据（Stars、Downloads、Installs、版本号、发布时间）。 从 ~/.clawhub/tracked-skills.json 读取跟踪列表，通过 clawhub inspect --json 获取实时数据， 支持单个查询、批量查询和按系列筛选。无需登录 ClawHub，inspect 接口公开可用。 触发词：clawhub 数据、skill 数据、发布数据、installs、downloads、skill stats、 查看 skill 安装量、clawhub stats、skill 运营数据。 不适用于：发布 Skill（使用 clawhub publish）、搜索新 Skill（使用 clawhub search）、 安装 Skill（使用 clawhub install）。 metadata: author: eamanc version: "1.0.0" --- # ClawHub Stats Tracker 查看已发布到 ClawHub 的 Skill 运营数据。 ## 快速开始 ``` "查看我的 clawhub skill 数据" "horoscope-daily 的安装量是多少" "所有 fortune-telling 系列的数据" ``` ## 工作流程 ### Step 1: 确定查询范围 | 用户意图 | 操作 | |---------|------| | 查看所有跟踪的 Skill | 运行 `bash scripts/fetch-stats.sh` | | 查看单个 Skill | 执行 `npx clawhub@latest inspect <slug> --json --no-input` | | 查看某系列 | 从配置文件中筛选匹配的 slug，逐个 inspect | ### Step 2: 获取数据 **批量查询（推荐）：** ```bash bash scripts/fetch-stats.sh ``` 脚本从 `~/.clawhub/tracked-skills.json` 读取 slug 列表，逐个调用 `npx clawhub@latest inspect --json`，输出表格。 **单个查询：** ```bash npx clawhub@latest inspect <slug> --json --no-input ``` 返回 JSON 结构： ```json { "skill": { "stats": { "stars": 0, "downloads": 42, "installsCurrent": 0, "installsAllTime": 0, "comments": 0, "versions": 5 }, "createdAt": 1773831137632, "updatedAt": 1774186907119 }, "latestVersion": { "version": "2.2.0", "createdAt": 1774186800664, "changelog": "..." } } ``` 时间戳为毫秒级 Unix 时间，需除以 1000 转换。 ### Step 3: 输出格式 **批量表格（脚本默认输出）：** ``` SLUG STARS DOWNLOADS CURRENT_INSTALLS ALLTIME_INSTALLS VERSION FIRST_PUBLISHED LATEST_PUBLISHED NOTE horoscope-daily 0 42 0 0 2.2.0 2026-03-18 18:52 2026-03-22 21:40 星座运势 EN/ZH life-query 1 116 0 0 2.0.0 2026-03-16 20:12 2026-03-21 11:02 生活查询助手 ``` **单 Skill 详情（用户查单个时）：** ``` 📊 horoscope-daily (v2.2.0) ├── ⭐ Stars: 0 ├── 📥 Downloads: 42 ├── 📦 Current Installs: 0 ├── 📊 All-time Installs: 0 ├── 💬 Comments: 0 ├── 📅 First Published: 2026-03-18 18:52 ├── 📅 Latest Version: 2026-03-22 21:40 └── 📝 Changelog: v2.2.0: Add Language & Localization... ``` ## 配置文件 路径：`~/.clawhub/tracked-skills.json` ```json { "skills": [ { "slug": "fortune-hub", "note": "运势测算统一入口" }, { "slug": "horoscope-daily", "note": "星座运势" } ] } ``` - `slug`：ClawHub 上的 Skill slug（必填） - `note`：备注说明（可选，仅展示用） **添加新 Skill**：在 `skills` 数组中追加一条即可。发布新 skill 后记得同步更新。 ## 已知限制 - ClawHub CLI 不支持按用户（owner）查询所有 skill，只能通过配置文件手动维护列表 - Downloads 和 Installs 指标有已知 bug（[openclaw/clawhub#156](https://github.com/openclaw/clawhub/issues/156)），installs 可能不计数 - inspect 接口无需登录，公开可用；publish/delete 等写操作才需要认证 ## 错误处理 | 场景 | 处理 | |------|------| | 配置文件不存在 | 提示创建 `~/.clawhub/tracked-skills.json`，给出格式示例 | | slug 不存在 | 显示"获取失败"，继续处理其他 slug | | clawhub CLI 未安装 | `npx clawhub@latest` 会自动下载，无需手动安装 | | 网络错误 | 显示失败的 slug，其余正常输出 | ## 原子化设计 本 Skill 仅负责「ClawHub Skill 运营数据查看」这一个原子化能力。不包含 Skill 发布、安装、搜索等功能，这些请直接使用 `clawhub` CLI 对应命令。


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