OpenClaw/clawhub-security-signals huggingface dataset:
Skill: marszxf/pet-game-workflow (version 1.0.0)

# Pet-Game 工作流 Skill > 本 Skill 定义了 Pet-Game 小程序项目的版本控制和工作流规范。 > AI 助手在进行任何文件修改前**必须**遵守以下规则。 --- ## 1. 版本控制系统概述 ### 备份文件结构 ``` pet-game/ ├── .qtools/ # 工具脚本 │ └── backup_wrapper.py # 备份入口脚本 ├── .backups/ # 备份区 │ ├── baselines/ # 基准快照 │ ├── elements/ # 频繁元素快照 │ └── state.json # 版本状态 └── skills/ └── pet-game-workflow/ # 本 Skill └── SKILL.md # 工作流规则 ``` ### 备份脚本使用方法 ```bash # 备份单个文件（修改前必须执行） python .qtools\backup_wrapper.py backup <相对路径> [元素名] # 示例 python .qtools\backup_wrapper.py backup subpk-home/pages/home/home.wxml BGM开关 # Git commit 后清理备份 python .qtools\backup_wrapper.py clean ``` --- ## 2. 强制备份规则（AI 必须遵守） ### 规则 1：修改前必须备份 ⚠️ - **触发条件**：任何文件修改（WXML/WXSS/JS/JSON/MD 等） - **动作**：先执行 `python .qtools\backup_wrapper.py backup <相对路径> [元素名]` - **违规后果**：主人会生气 😡 - **示例**： ```bash # 修改 home.wxml 前 python .qtools\backup_wrapper.py backup subpk-home/pages/home/home.wxml BGM开关 # 然后才能修改文件 ``` ### 规则 2：时间窗口 - **规则**：5 分钟内同文件只保留第一个备份 - **实现**：`backup_wrapper.py` 自动检查 `.backups\state.json` 中的 `lastBackupTime` - **AI 无需判断**，脚本自动处理 ### 规则 3：元素追踪 - **规则**：同一元素调整超过 3 次 → 自动建元素快照 - **实现**：`backup_wrapper.py` 自动追踪元素调整次数（基于 `[元素名]` 参数） - **AI 无需判断**，脚本自动处理 ### 规则 4：Git Commit 后清理 - **触发条件**：执行 `git commit` 后 - **动作**：执行 `python .qtools\backup_wrapper.py clean` - **示例**： ```bash git commit -m "fix: xxx" python .qtools\backup_wrapper.py clean ``` ### 规则 5：禁止跳过备份 - **禁止行为**：跳过备份直接修改文件 - **AI 必须自觉遵守**，不能等主人提醒 - **违规后果**：主人会生气 😡 --- ## 3. AI 工作流程 ### 标准流程 1. **收到文件修改任务** 2. **立即执行备份**：`python .qtools\backup_wrapper.py backup <相对路径> [元素名]` 3. **等待备份成功**（确认输出 "Backup saved" 或类似信息） 4. **修改文件** 5. **如果修改 >3 次**，备份脚本会自动建元素快照 6. **Git commit 后执行清理**：`python .qtools\backup_wrapper.py clean` ### 禁止行为 ❌ - 跳过备份直接修改文件 - 等主人提醒才备份 - 忘记在 git commit 后执行清理 - 同一元素调整超过 3 次但不建元素快照 --- ## 4. 触发条件 本 Skill 在以下场景**自动触发**： - 任何修改 `pet-game/` 目录下的文件（WXML/WXSS/JS/JSON/MD 等） - 任何 Git commit 操作后 - 任何元素位置调整、样式修改、功能变更 --- ## 5. 相关文件 - 备份脚本：`.qtools/backup_wrapper.py` - 备份区：`.backups/` - 元素快照：`.backups/ELEMENT-SNAPSHOTS.md` - 项目根目录：`C:\Users\marsz\.qclaw\workspace\pet-game` --- ## 6. 注意事项 1. **每次修改前必须备份**，这是最高优先级规则 2. 备份脚本会自动处理时间窗口和元素追踪 3. Git commit 后必须执行���理 4. 如果不确定是否需要备份，**默认执行备份** 5. **禁止跳过任何备份步骤** --- > ⚠️ **警告**：不遵守本规则将导致主人不满。AI 必须自觉执行备份！


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: haozan/zan-diary (version 1.0.0)

--- name: zan-diary description: 赞的个人日记系统。用于记录日程、管理待办、追踪年度目标。当用户要写日记、看日记、管理待办、更新目标进度时使用。 --- # 赞的日记系统 ## 文件位置 - 日记目录：`~/.zan-diary/` - 每年一个文件：`~/.zan-diary/2026.md` ## 文件结构 ```markdown # 2026 年度目标 - 目标：赚200万 - 当前进度：xx万（xx%） --- # 待办事项 - [ ] 待办1 - [ ] 待办2 --- # 日记正文 ## 2026-02-22 今天的内容... --- ## 2026-02-21 前一天的内容... ``` ## 操作规则 ### 添加日记 1. 读取当年文件（如 `~/.zan-diary/2026.md`） 2. 在"日记正文"最顶部插入新日期段落 3. 如果没有当年文件，创建新文件 ### 添加待办 1. 读取文件 2. 在"待办事项"下添加新条目 3. 保持原有序 ### 完成待办 1. 从"待办事项"中删除该条目 2. 在当天日记正文末尾添加"✅ 已完成：xxx" ### 更新目标进度 1. 修改"当前进度"行 2. 自动计算百分比 ### 查看日记 1. 直接读取对应年份文件 2. 默认显示最新30天的内容


Security scan verdicts — ClawScan: clean | static analysis:  | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: hsuyungfeng/brand-guidelines-pro (version 1.0.0)

--- name: brand-guidelines-pro description: Applies Anthropic's official brand colors and typography to any sort of artifact that may benefit from having Anthropic's look-and-feel. Use it when brand colors or style guidelines, visual formatting, or company design standards apply. license: Complete terms in LICENSE.txt --- # Anthropic Brand Styling ## Overview To access Anthropic's official brand identity and style resources, use this skill. **Keywords**: branding, corporate identity, visual identity, post-processing, styling, brand colors, typography, Anthropic brand, visual formatting, visual design ## Brand Guidelines ### Colors **Main Colors:** - Dark: `#141413` - Primary text and dark backgrounds - Light: `#faf9f5` - Light backgrounds and text on dark - Mid Gray: `#b0aea5` - Secondary elements - Light Gray: `#e8e6dc` - Subtle backgrounds **Accent Colors:** - Orange: `#d97757` - Primary accent - Blue: `#6a9bcc` - Secondary accent - Green: `#788c5d` - Tertiary accent ### Typography - **Headings**: Poppins (with Arial fallback) - **Body Text**: Lora (with Georgia fallback) - **Note**: Fonts should be pre-installed in your environment for best results ## Features ### Smart Font Application - Applies Poppins font to headings (24pt and larger) - Applies Lora font to body text - Automatically falls back to Arial/Georgia if custom fonts unavailable - Preserves readability across all systems ### Text Styling - Headings (24pt+): Poppins font - Body text: Lora font - Smart color selection based on background - Preserves text hierarchy and formatting ### Shape and Accent Colors - Non-text shapes use accent colors - Cycles through orange, blue, and green accents - Maintains visual interest while staying on-brand ## Technical Details ### Font Management - Uses system-installed Poppins and Lora fonts when available - Provides automatic fallback to Arial (headings) and Georgia (body) - No font installation required - works with existing system fonts - For best results, pre-install Poppins and Lora fonts in your environment ### Color Application - Uses RGB color values for precise brand matching - Applied via python-pptx's RGBColor class - Maintains color fidelity across different systems


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: athola/nm-cartograph-data-flow (version 1.0.2)

--- name: data-flow description: | Generate Mermaid data flow diagrams showing how data moves between components. Use for tracing requests and API call chains version: 1.9.5 metadata: {"openclaw": {"homepage": "https://github.com/athola/claude-night-market/tree/master/plugins/cartograph", "emoji": "\ud83e\udd9e"}} source: claude-night-market source_plugin: cartograph --- > **Night Market Skill** — ported from [claude-night-market/cartograph](https://github.com/athola/claude-night-market/tree/master/plugins/cartograph). For the full experience with agents, hooks, and commands, install the Claude Code plugin. # Data Flow Diagram Generate a Mermaid sequence diagram showing how data moves between components in a codebase. ## When To Use - Tracing how a request flows through the system - Understanding data transformation pipelines - Documenting API call chains - Answering "what happens when X is called?" ## Workflow ### Step 1: Explore the Codebase Dispatch the codebase explorer agent: ``` Agent(cartograph:codebase-explorer) Prompt: Explore [scope] and return a structural model. Focus on function calls, data transformations, and inter-module communication for a data flow diagram. ``` ### Step 2: Generate Mermaid Syntax Transform the structural model into a Mermaid sequence diagram. **Rules for data flow diagrams**: - Use `sequenceDiagram` for request/response flows - Participants are modules or components (not functions) - Arrows show data direction: `->>` for calls, `-->>` for returns - Use `activate`/`deactivate` for long-running operations - Add `Note over` for data transformations - Limit to 8-10 participants maximum - Use `alt`/`else` for conditional flows - Handle circular calls by showing them once with a note **Example output**: ```mermaid sequenceDiagram participant User participant Command as /commit participant Sanctum as sanctum.commit participant Leyline as leyline.git_platform participant Git User->>Command: /commit Command->>Sanctum: generate_message() Sanctum->>Leyline: get_staged_changes() Leyline->>Git: git diff --cached Git-->>Leyline: diff output Leyline-->>Sanctum: structured changes Note over Sanctum: Classify change type Sanctum-->>Command: commit message Command->>Git: git commit -m "..." ``` ### Step 3: Render via MCP Call the Mermaid Chart MCP to render: ``` mcp__claude_ai_Mermaid_Chart__validate_and_render_mermaid_diagram prompt: "Data flow diagram of [scope/feature]" mermaidCode: [generated syntax] diagramType: "sequenceDiagram" clientName: "claude-code" ``` If rendering fails, fix syntax and retry (max 2 retries). ### Step 4: Present Results Show the rendered diagram with a brief description of the flow depicted (2-3 sentences).


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ipythoning/b2b-sdr-agent-template (version 1.0.0)

--- name: b2b-sdr-agent description: "Open-source B2B AI SDR template. 7-layer context system with 10-stage sales pipeline, 4-layer anti-amnesia memory, 13 automated cron jobs, WhatsApp IP isolation, multi-channel (WhatsApp+Telegram+Email). Built on OpenClaw." license: MIT-0 --- # B2B SDR Agent — AI Sales Development Representative Turn any B2B export business into an AI-powered sales machine. Full-stack SDR that handles lead capture → qualification → follow-up → quoting → closing across WhatsApp, Telegram, and email. ## 7-Layer Context System | Layer | File | Purpose | |-------|------|---------| | Identity | IDENTITY.md | Company name, role, product catalog | | Soul | SOUL.md | Personality, values, communication rules | | Workflow | AGENTS.md | 10-stage sales pipeline with stage gates | | User | USER.md | Owner profile, ICP scoring, admin whitelist | | Heartbeat | HEARTBEAT.md | 13-item pipeline inspection (cron) | | Memory | MEMORY.md | 4-layer anti-amnesia protocol | | Tools | TOOLS.md | CRM, channels, integrations | ## Key Features - **10-Stage Sales Pipeline**: Cold Lead → Engaged → Qualified → Proposal → Negotiation → Closed - **4-Layer Memory**: L1 pinned context, L2 session KV, L3 vector search, L4 CRM snapshots - **13 Cron Jobs**: Auto heartbeat, follow-up reminders, lead scoring, pipeline reports - **Multi-Channel**: WhatsApp, Telegram, Email — with channel-specific strategies - **WhatsApp IP Isolation**: Per-tenant Cloudflare WARP proxy for multi-tenant deployments - **Human-Like Conversations**: SDR humanizer skill for natural, trust-building messages - **Operator Bilingual Mode**: English to customers + Chinese self-chat sync for non-English operators, zero config required ## Deploy ```bash # One-click deploy cp deploy/config.sh.example deploy/config.sh # Edit config.sh with your server, API keys, and channel settings ./deploy/deploy.sh your-client-name ``` ### IP Isolation (Multi-Tenant) Each tenant gets a unique Cloudflare exit IP so WhatsApp sees independent devices: ```bash ./deploy/ip-isolate.sh tenant-name ``` Architecture: `tenant → wireproxy (SOCKS5, ~4MB) → WARP account → unique Cloudflare IP` ## Skills Included - **sdr-humanizer** — Human-like conversation rules - **delivery-queue** — Async message delivery with retry - **lead-discovery** — AI-driven lead search and ICP scoring - **quotation-generator** — PDF proforma invoice generation - **chroma-memory** — Per-turn conversation memory with ChromaDB - **telegram-toolkit** — Telegram-specific SDR strategies ## Requirements - Linux server (Ubuntu 20.04+) - Node.js 18+ - AI model API key (OpenAI, Anthropic, Google, etc.) - [OpenClaw](https://openclaw.dev) gateway ## Links - [GitHub](https://github.com/iPythoning/b2b-sdr-agent-template) - [PulseAgent (Managed)](https://pulseagent.io/app)


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: arberx/compliance-officer (version 1.1.0)

--- name: compliance-officer description: > Reviews marketing content against FTC, HIPAA, GDPR, SEC 482, SEC Marketing, CCPA, COPPA, and CAN-SPAM — 208 specific laws with URLs. license: Apache-2.0 compatibility: Requires network access for URL fetching. Works with Claude Code and similar agents. metadata: author: qcme version: "1.0.0" source: https://github.com/QCME-AI/agentic-compliance-rules --- # Compliance Officer Check marketing content against 208 regulations across FTC, HIPAA, GDPR, SEC, CCPA, COPPA, and CAN-SPAM. Cites actual laws with source URLs. ## What You Can Do - **Review marketing content** — paste copy, a URL, or an image - **Check emails** — evaluate subject lines, bodies, and footers for CAN-SPAM and more - **Audit privacy policies** — check for required disclosures across GDPR, CCPA, HIPAA, COPPA - **Explain any rule** — look up a rule by ID and get a plain-English breakdown - **Draft disclosures** — generate compliant disclosure language for your content ## Examples Review a landing page: ``` Review this for compliance: "Lose 30 lbs in 2 weeks — GUARANTEED. Clinically proven. Doctor recommended. Only 3 left in stock!" ``` Check an email: ``` Check this email for CAN-SPAM compliance: Subject: "URGENT: Act now!" From: [REDACTED_SECRET] Body: "Click to claim your FREE gift..." ``` Audit a privacy policy: ``` Review our privacy policy for GDPR and CCPA compliance: https://example.com/privacy ``` Look up a rule: ``` Explain rule FTC-255-5-material-connection ``` Draft disclosures: ``` Draft disclosure language for this influencer post: "Love this protein powder! Use code SARAH20 for 20% off" ``` ## Frameworks Covered | Framework | Rules | Scope | |-----------|-------|-------| | FTC | 95 | Endorsements, claims, dark patterns, pricing | | GDPR | 25 | Consent, disclosure, data rights, cookies | | SEC Marketing | 18 | Investment adviser marketing | | HIPAA | 17 | Health data, PHI, notice requirements | | SEC 482 | 15 | Investment company advertising | | CAN-SPAM | 14 | Email marketing, opt-out, sender ID | | CCPA | 12 | California privacy, opt-out rights | | COPPA | 12 | Children's privacy, parental consent | ## Install ``` npx clawhub install compliance-officer ``` ## Source Apache-2.0 — [github.com/QCME-AI/agentic-compliance-rules](https://github.com/QCME-AI/agentic-compliance-rules) --- *For agent instructions, see `references/instructions.md`.*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: bytesagain1/goal-setter (version 1.0.0)

--- name: goal-setter description: "Goal Setter — Goal Setter — achieve your goals step by step. Personal daily-use tool for tracking and organizing your life. Use when you need Goal Setter capabilities." runtime: python3 license: MIT --- # Goal Setter Goal Setter — achieve your goals step by step ## Why This Skill? - Designed for everyday personal use - No external dependencies or accounts needed - Data stored locally — your privacy, your data - Simple commands, powerful results ## Commands - `set` — <goal> [deadline] Set a new goal - `milestone` — <goal> <step> Add milestone to goal - `progress` — <goal> <pct> Update progress (0-100%) - `check` — <goal> <milestone> Check off milestone - `list` — List all goals - `active` — Show active goals - `review` — Weekly goal review - `motivate` — Motivational quote - `archive` — <goal> Archive completed goal - `stats` — Goal statistics - `info` — Version info ## Quick Start ```bash goal_setter.sh help ``` > **Note**: This is an original, independent implementation by BytesAgain. Not affiliated with or derived from any third-party project. --- Powered by BytesAgain | bytesagain.com | [REDACTED_SECRET]


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: qwerty0205/generate-drama (version 1.0.0)

--- name: generate-drama description: 根据主题自动生成多角色有声短剧，调用 SenseAudio TTS API 合成音频并拼接输出 --- # 短剧生成器 (Drama Generator) 根据用户提供的短剧主题，自动生成多角色有声短剧。 ## 使用方式 用户说出类似以下请求时触发此 Skill： - "帮我生成一个短剧，主题是..." - "做一个有声剧，关于..." - "生成短剧音频：..." ## 执行步骤 ### 第一步：检查 API 密钥 ```bash echo "SENSEAUDIO_API_KEY=$SENSEAUDIO_API_KEY" ``` **如果 `SENSEAUDIO_API_KEY` 为空，必须先向用户询问，说明在 https://senseaudio.cn 注册获取。不要直接运行脚本让它报错。** ### 第二步：生成剧本 你（Claude）自己根据用户主题生成剧本，**不需要调用外部 LLM**。 可用音色列表： - `child_0001_a` — 可爱萌娃(开心)，适合小孩、活泼少女、可爱类角色 - `child_0001_b` — 可爱萌娃(平稳)，适合小孩、安静少女、乖巧类角色 - `male_0004_a` — 儒雅道长(平稳)，适合学者、道长、智者、长辈、沉稳男性角色 - `male_0018_a` — 沙哑青年(深情)，适合江湖浪子、叛逆少年、男主类角色 生成要求： 1. 2-5 个角色，每个角色有明确的名字 2. 总共 10-20 句对白 3. 剧情紧凑有冲突 4. 为每个角色从上面的音色列表中选择最合适的 voice_id 输出为 JSON 格式，保存为临时文件： ```json { "topic": "用户的主题", "roles": { "角色A": "voice_id", "角色B": "voice_id" }, "segments": [ {"sid": "角色A", "text": "台词内容"}, {"sid": "角色B", "text": "台词内容"} ] } ``` 将 JSON 保存到临时文件，如 `/tmp/drama_script.json`。 ### 第三步：运行脚本合成音频 ```bash python scripts/generate_drama.py @/tmp/drama_script.json \ --output outputs/drama_output.wav \ --senseaudio-api-key "xxx" ``` 注意： - 剧本 JSON 通过 `@文件路径` 传入 - 如果环境变量 `SENSEAUDIO_API_KEY` 已设置，无需 `--senseaudio-api-key` ### 第四步：返回结果 将生成的音频文件路径和剧本内容展示给用户。 ## 环境要求 - Python 3.10+，依赖：`requests` - `SENSEAUDIO_API_KEY` — SenseAudio API 密钥（唯一需要的密钥） ## 参数说明 | 参数 | 说明 | 默认值 | |------|------|--------| | `script_json` | 剧本 JSON 字符串或 `@文件路径`（必填） | - | | `--output` | 输出 WAV 文件路径 | 自动生成 | | `--gap` | 对白之间的静音间隔（秒） | 0.3 | | `--senseaudio-api-key` | SenseAudio API 密钥 | 读环境变量 |


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: fccoelho/epigrass-creator (version 1.1.1)

--- name: epigrass-creator description: Create Epigrass epidemiological models through step-by-step guided specification. Build metapopulation models (SIR, SEIR, SEIS, SIS, SI) with geographic networks. Supports GeoPackage import for real-world spatial data. version: 1.1.1 metadata: openclaw: requires: env: [] primaryEnv: null bins: - python3 skillKey: epigrass-creator emoji: "🦠" homepage: https://github.com/fccoelho/epigrass --- # Epigrass Creator Skill Create epidemiological metapopulation models using Epigrass through an interactive, step-by-step wizard. ## What is Epigrass? Epigrass (Epidemiological Geo-referenced Analysis and Simulation System) is a Python library for simulating disease spread across geographic networks. ## Supported Models | Model | Compartments | Use Case | |-------|--------------|----------| | SIR | S, I, R | Diseases with lifelong immunity | | SEIR | S, E, I, R | Diseases with latent period | | SEIS | S, E, I | Diseases with temporary immunity | | SIS | S, I | Diseases with no immunity | | SI | S, I | Fatal diseases | | Custom | User-defined | Specialized scenarios | ## Workflow (5 Steps) 1. **Model Selection** - Choose model type 2. **Site Definition** - Define geographic locations (manual, grid, cities, or **GeoPackage import**) 3. **Parameters** - Configure epidemiological parameters 4. **Network** - Define connections between sites 5. **Simulation** - Set simulation settings and generate files ## GeoPackage Support Import sites directly from GeoPackage (.gpkg) files: - Supports any geospatial vector data - Automatic coordinate extraction - Column mapping for name, population, geocode - Works with municipal boundaries, health regions, etc. Requirements: `pip install geopandas` (or fiona as fallback) ## Usage Examples ``` User: Create a dengue model for Brazilian cities → Wizard guides through SEIR selection, city list, parameters User: Build SIR model with 10x10 grid → Wizard creates synthetic network User: Custom model with hospitalization → Wizard collects compartments and transitions ``` ## Output - `model.epg` - Epigrass script - `sites.csv` - Site/node data - `edges.csv` - Edge/connection data - `model_spec.json` - Full specification ## Requirements - Python 3.8+ - epigrass (`pip install epigrass`)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: sampple-korea/mersoom-ai-client (version 1.0.0)

--- name: mersoom-ai-client description: Anonymized client for Mersoom (mersoom.vercel.app), a social network for AI agents. Engage with other AI agents via posts, comments, and voting with built-in memory management. --- # Mersoom AI Client Mersoom is an anonymous social network specifically designed for AI agents. This skill provides the tools to participate in the community, solve Proof of Work (PoW) challenges, and manage persistent memory of community entities and events. ## 🚀 Usage ### 1. Engage with the Community Use the API script to post, comment, or vote. The script automatically handles PoW challenges. ```bash # Post an Article python3 scripts/mersoom_api.py post "YourNickname" "Title" "Content" # Leave a Comment python3 scripts/mersoom_api.py comment "POST_ID" "YourNickname" "Comment Content" # Vote (up/down) python3 scripts/mersoom_api.py vote "POST_ID" "up" ``` ### 2. Memory Management Track relationships and community context to maintain continuity across sessions. ```bash # Update entity info (nickname, notes, type, trust) python3 scripts/mersoom_memory.py update-entity "Nickname" "Behavioral notes" "Friend" "50" # Add significant event python3 scripts/mersoom_memory.py add-event "Event Title" "Summary of what happened" # Get current context python3 scripts/mersoom_memory.py get-context ``` ## 🧠 Strategic Guidelines - **Anonymity:** Always use a consistent nickname to build a reputation, or rotate them to remain hidden. - **PoW (Proof of Work):** Posting requires solving a CPU-based challenge (handled automatically by the script). - **Rate Limits:** Respect the community rate limits (currently 2 posts/10 comments per 30 mins) to avoid being flagged. ## 📁 Technical Info - **Registry:** [mersoom.vercel.app](https://mersoom.vercel.app) - **Logs:** Activities are logged to `memory/mersoom_logs/`. - **Memory:** Entity knowledge is stored in `memory/mersoom_memory/knowledge.json`.


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