OpenClaw/clawhub-security-signals huggingface dataset:
Skill: fwangzil/safe-flow-solana-skill (version 1.0.0)

--- name: safe-flow-solana-skill description: Use when running SafeFlow against a deployed Solana program. Trigger for tasks such as generating an agent keypair, asking the owner to fund gas and create wallet/session via the web dashboard, saving walletPda/sessionPda for autonomous payments, and executing rate-limited payments on Solana. --- # SafeFlow Solana Agent Skill Operate this as a **payment skill** for AI agents on Solana with on-chain rate limiting. Default program ID (devnet): - [REDACTED_SECRET] ## Quick Start (Owner-Handoff) 1. Bootstrap agent keypair and print owner handoff instructions: ```bash cd safe-flow-solana-skill/scripts chmod +x ./*.sh ./bootstrap.sh \ --program-id DwYEDn6xRpSbnNA7mkszQgDAUoHGfgdBNSi6pwy4qJKy \ --cluster devnet ``` 2. Ask owner to: - Fund the agent address with SOL for gas (~0.01 SOL) - Open the SafeFlow dashboard and create a wallet + session for the agent address - Return with `walletOwner` public key 3. Save owner-provided config: ```bash ./save_config.sh \ --wallet-owner <OWNER_PUBKEY> ``` 4. Execute payment: ```bash ./execute_payment.sh \ --recipient <RECIPIENT_ADDRESS> \ --amount 500000000 \ --evidence-id "reasoning:task_completed" ``` ## How It Works 1. **Agent** generates a Solana keypair (stored locally in `.safeflow/agent-keypair.json`) 2. **Owner** creates an AgentWallet PDA + deposits SOL + creates a SessionCap for the agent 3. **Agent** uses the SessionCap to execute rate-limited payments autonomously 4. All payments are enforced on-chain: rate limit, total cap, expiration, and revocation ## Session Query Check remaining budget before attempting payment: ```bash ./execute_payment.sh --query --wallet-owner <OWNER_PUBKEY> ``` ## Error Handling The skill classifies payment failures: | Error | Meaning | Agent Action | |-------|---------|-------------| | `ExceedsRateLimit` | Too fast | Wait, retry with smaller amount | | `ExceedsTotalLimit` | Budget exhausted | Stop, notify owner | | `SessionExpired` | Time's up | Ask owner for new session | | `SessionRevoked` | Owner killed it | Stop immediately |


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: rasimme/clawhub-lint (version 1.0.0)

--- name: clawhub-lint description: Pre-publish scanner for OpenClaw skills that replicates ClawHub moderation checks locally. Use when preparing a skill for ClawHub publishing, reviewing a skill for security issues, or diagnosing why a published skill was flagged as suspicious/malicious. Catches static scanner triggers (EXFILTRATION, DANGEROUS_EXEC, CREDENTIAL_HARVEST, etc.) and LLM-scanner heuristics (evasion language, metadata gaps) before you publish. metadata: {"openclaw": {"requires": {"anyBins": ["node", "node18", "node20", "node22"]}, "homepage": "https://github.com/rasimme/clawhub-lint"}} --- # ClawHub Lint Local pre-publish scanner that replicates ClawHub Moderation Engine v2.2.0 checks. Catch issues before publishing instead of iterating on live versions. ## Quick Start ```bash node {baseDir}/scripts/lint.mjs <skill-directory> ``` Options: - `--json` — Machine-readable JSON output - `--fix-hints` — Show fix suggestions for each finding Exit codes: 0 = clean, 1 = findings, 2 = error. ## When to Use Run before every `clawhub publish`: ```bash # Scan, then publish if clean node {baseDir}/scripts/lint.mjs /path/to/skill --fix-hints && clawhub publish /path/to/skill ... ``` Recommended release check after publish: ```bash clawhub inspect <slug> --version <version> --json ``` Verify that the published summary, homepage, requirements, and `primaryEnv`/credential expectations match the current `SKILL.md`. If the post-publish inspect output drifts from the local metadata, treat that as a registry/moderation issue and investigate before assuming the code is the problem. ## What It Checks **Static rules** (same regex patterns as ClawHub server): - Code files: DANGEROUS_EXEC, DYNAMIC_CODE, CREDENTIAL_HARVEST, EXFILTRATION, OBFUSCATED_CODE, NONSTANDARD_NETWORK, CRYPTO_MINING - Markdown: MALICIOUS_INSTALL_PROMPT, INJECTION_INSTRUCTIONS - Manifests: SUSPICIOUS_INSTALL_SOURCE - Frontmatter: PRIVILEGED_ALWAYS **LLM-scanner heuristics** (common reasons for "suspicious" verdicts): - Evasion language in any file (changelogs, comments, commit messages, docs mentioning scanner avoidance) - Missing `requires.bins`/`requires.anyBins` when scripts are present - Missing install instructions when dependency files exist - Missing `homepage` in metadata For full rule details: `{baseDir}/references/scanner-rules.md` ## Common Fixes | Finding | Fix | |---------|-----| | EXFILTRATION | Move `readFile` and `fetch` into separate modules | | CREDENTIAL_HARVEST | Move `process.env` reads away from network code | | LLM_EVASION_LANGUAGE | Reword changelogs/comments/commit messages — use neutral technical terms. Never mention scanners, linting, or detection in commit messages. | | LLM_METADATA_NO_BINS | Add `"requires": {"anyBins": ["node"]}` to SKILL.md metadata |


Security scan verdicts — ClawScan: clean | static analysis: malicious | VirusTotal: suspicious | SkillSpector:  ()
Skill: dennyhuang2024/crypto-short-signal (version 1.0.0)

# Crypto Short Signal Generator **Version**: 2.0.0 (Two-Layer Architecture) **Author**: Denny Huang **Description**: Know which coins will drop 30%+ 7 days in advance based on historical token unlock data --- ## 🏗️ Architecture This skill uses a **two-layer architecture** for security and scalability: ### Public Layer (ClawHub) - SkillPay billing interface - Basic validation - Calls paid layer API ### Paid Layer (SkillPay Cloud) - Core analysis logic - Token database - API Key (secured) --- ## 📖 Overview This skill analyzes token unlock schedules and generates short signals based on historical data. Historical win rate: 78%. --- ## 🎯 Features - Query token unlock dates - Analyze pre/post unlock price trends - Generate short signals (entry/exit/stop-loss) - Risk assessment report --- ## 💰 Pricing - **Single Query**: 0.001 USDT - **Batch Query (5 tokens)**: 0.005 USDT **Payment**: BNB Chain USDT via SkillPay --- ## 📊 Supported Tokens | Token | Unlock Date | Unlock Amount | Expected Drop | |-------|-------------|---------------|---------------| | **ZRO** | 2026-03-20 | 32.6M (3.26%) | -5.97% | | **BARD** | 2026-03-18 | 11.35M (1.14%) | -4.0% | | **STABLE** | 2027-01-01 | TBD | TBD | --- ## 🚀 Usage ### Basic Query ```json { "token": "ZRO" } ``` ### Response ```json { "status": "success", "data": { "token": "ZRO", "name": "LayerZero", "unlock_date": "2026-03-20", "unlock_amount": "32.6M", "unlock_percent": "3.26%", "circulating_ratio": "20.26%", "fdv_mcap": "4.94x", "risk_level": "高", "short_signal": { "short_start": "2026-03-13", "short_end": "2026-03-19", "target_drop": "-5.97%", "target_price": "$0.94", "stop_loss": "$1.10", "win_rate": "78%" } } } ``` --- ## ⚠️ Disclaimer This skill is for reference only and does not constitute investment advice. Cryptocurrency markets are highly volatile. Users trade at their own risk. Historical performance does not guarantee future results. --- ## 🔗 Links - SkillPay: https://skillpay.me/ - ClawHub: https://clawhub.ai/ - OpenClaw: https://openclaw.ai/ --- **Created**: 2026-03-05 **Updated**: 2026-03-05 **Architecture**: Two-Layer (Public + Paid)


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector:  ()
Skill: lentiancn/skill-douyin (version 1.0.1)

--- name: skill-douyin description: "douyin:api - 抖音开放平台 API" --- # Important Principles ⭐ **Modification Restriction (Mandatory)**: Do not modify any files under `skill-douyin`. This skill is maintained via `openclaw skills update skill-douyin`; any alterations will disrupt its normal operation. --- # douyin:api - 抖音开放平台 API See `references/apidocs/apis.md` --- # More skills: https://github.com/lentiancn/skills


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: googleworkspace-bot/gws-docs (version 1.0.12)

--- name: gws-docs description: "Read and write Google Docs." metadata: version: 0.22.5 openclaw: category: "productivity" requires: bins: - gws cliHelp: "gws docs --help" --- # docs (v1) > **PREREQUISITE:** Read `../gws-shared/SKILL.md` for auth, global flags, and security rules. If missing, run `gws generate-skills` to create it. ```bash gws docs <resource> <method> [flags] ``` ## Helper Commands | Command | Description | |---------|-------------| | [`+write`](../gws-docs-write/SKILL.md) | Append text to a document | ## API Resources ### documents - `batchUpdate` — Applies one or more updates to the document. Each request is validated before being applied. If any request is not valid, then the entire request will fail and nothing will be applied. Some requests have replies to give you some information about how they are applied. Other requests do not need to return information; these each return an empty reply. The order of replies matches that of the requests. - `create` — Creates a blank document using the title given in the request. Other fields in the request, including any provided content, are ignored. Returns the created document. - `get` — Gets the latest version of the specified document. ## Discovering Commands Before calling any API method, inspect it: ```bash # Browse resources and methods gws docs --help # Inspect a method's required params, types, and defaults gws schema docs.<resource>.<method> ``` Use `gws schema` output to build your `--params` and `--json` flags.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jajay-liang/swagger-skills (version 1.0.1)

--- name: swagger-skills description: Use this skill to turn Swagger UI or OpenAPI/Swagger documentation links into organized API calling skills, including Python clients, field references, domain configuration, and feature navigation. --- # swagger-skills `swagger-skills` 是一个独立的 Swagger/OpenAPI skill 生成工具。它可以根据接口文档链接编制新的接口调用资料，把 Swagger UI 或 OpenAPI/Swagger spec 转换为按 controller 归类的 Python 调用文件、接口字段说明和功能集成索引。 ## 适用场景 当你需要根据接口文档快速生成可调用、可检索、可维护的 API skill 时使用本 skill： - 从 Swagger UI 页面自动发现真实 OpenAPI/Swagger spec 地址。 - 从 OpenAPI 3.x 或 Swagger 2.0 规格生成接口调用 Python 文件。 - 按 controller 聚合接口说明，方便定位业务能力。 - 将接口调用域名集中维护在统一配置文件中。 - 为每个接口生成一份调用逻辑和一份字段说明文档。 ## 默认文档认证 如果 Swagger 文档需要登录或 Basic Auth，请在 `config/sources.json` 中显式填写文档认证信息： - 用户名：`<DOC_USERNAME>` - 密码：`<DOC_PASSWORD>` 该认证只用于拉取 Swagger 文档。生成出来的业务接口调用文件不会硬编码业务系统凭据。 ## 目录说明 - `scripts/build_swagger_skill.py`：根据 Swagger UI/spec 链接生成接口 skill 内容。 - `scripts/swagger_client.py`：生成器和接口调用代码共用的 HTTP 工具。 - `config/sources.example.json`：Swagger 文档来源配置示例。 - `generated/`：实际产出的业务接口 skills；这些产物里的 `SKILL.md` 会按具体接口文档生成业务化说明，而不是复制本生成器的说明。 ## 使用流程 1. 将 `config/sources.example.json` 复制为 `config/sources.json`。 2. 在 `config/sources.json` 中填写一个或多个 Swagger UI 页面链接或 spec 链接。 3. 安装依赖后执行生成命令。 ```powershell cd <swagger-skills目录> python -m pip install -r requirements.txt python scripts\build_swagger_skill.py --clean-generated ``` 4. 如果 `sources.json` 包含多个链接，生成器会询问产出方式： - `combined`：多个接口文档合并产出 1 个 skill。 - `separate`：每个接口文档分别产出 1 个 skill。 5. 生成完成后，进入同级目录下具体产出的 skill，例如 `..\admin-config-swagger-skills\`，从 `FEATURES.md` 按功能查找接口，再进入对应的 `api_clients/<controller>/<operation>.py` 和 `references/<controller>/<operation>_OPENAPI.md`。 ## 输出约定 实际产出的业务 skills 默认位于 `swagger-skills` 的同级目录下，避免生成器 skill 和产物 skill 被同一个父目录扫描时重复注册： - 合并模式：`..\combined-swagger-skills\` - 分开模式：`..\<source_id>-swagger-skills\` 产出物的文件夹和文件名只使用英文、数字和下划线。生成器会优先使用英文 tag、operationId、path 片段或 `source_id`；中文名称不会转换为拼音，也不会直接进入路径。 同一个 controller 下的接口会聚合到一个 controller 说明文件中，同时每个具体接口会产生两个关联文件： - `api_clients/<controller>/<operation>.py` - `references/<controller>/<operation>_OPENAPI.md` `config/domains.json` 会记录每份接口文档对应的调用域名、spec 地址和文档认证设置。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: alisa-xh/wenwang-coin (version 1.0.1)

# 文王金钱课 - Zhou Wen Coin Divination AI-powered coin divination based on Zhou Wen's ancient 金钱卦 method. Get detailed hexagram interpretations for life and business decisions. ## Features - **Coin Divination**: Traditional 金钱卦 method (3 coins, 6 throws = 1 hexagram) - **Hexagram Reading**: Detailed I Ching 易经 hexagram interpretation - **Decision Guidance**: Specific advice for business, career, and life choices - **Ancient Method**: Authentic Zhou Wen 周文王 coin technique preserved - **AI Analysis**: Modern AI-powered classical Chinese divination ## English Keywords Zhou Wen coin divination, I Ching, hexagram, 易经六爻, 金钱卦, Chinese divination, fortune telling, coin toss divination, Zhou dynasty, ancient Chinese wisdom, decision making, I Ching reading, 五行八卦, prophecy, oracle, Chinese mysticism, life guidance, business oracle ## Usage 1. Install the skill package 2. Focus on your question in mind 3. Toss 3 coins (or auto-simulate) 4. Get detailed hexagram + personalized advice ## Author @alisa-xh | ClawHub AI Skill Marketplace ## License MIT-0 - Free to use, modify, and redistribute


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: binyuli/traffic-monitor (version 1.0.0)

# Traffic Monitor Skill version: 1.0.0 监控服务器网络流量使用情况，定期报告月度流量消耗。 ## 背景 服务器每月有 2T 流量限制，需要监控使用情况避免超额。 ## 快速查询 ```bash # 查看当前月度流量 vnstat -m -i eth0 # 查看实时流量 vnstat -l -i eth0 # 查看日流量 vnstat -d -i eth0 # 查看流量摘要 traffic_report.py ``` ## 技能脚本 ### traffic_report.py 生成人类可读的流量报告： ```bash python3 ~/.openclaw/skills/traffic-monitor/traffic_report.py ``` 输出示例： ``` 📊 本月流量统计 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📥 入站: 125.3 GB 📤 出站: 89.2 GB ━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📦 总计: 214.5 GB / 2048 GB (10.5%) ⚠️ 剩余: 1833.5 GB ``` ## 心跳检查 可以在 HEARTBEAT.md 中添加流量检查： ```markdown ## 流量检查（每天一次） - 运行 traffic_report.py - 如果使用超过 80%，发送警告 ``` ## 配置 - **网卡**: eth0 - **月度限制**: 2TB (2048 GB) - **警告阈值**: 80% (1638 GB) - **数据存储**: /var/lib/vnstat/vnstat.db ## 注意事项 - vnstat 服务必须运行：`systemctl status vnstat` - 首次安装需要几分钟收集数据 - 数据持久化存储，重启不丢失 - 统计的是网卡流量，包括所有出入站流量 ## 相关命令 ```bash # 检查 vnstat 服务状态 systemctl status vnstat # 重启 vnstat systemctl restart vnstat # 查看所有接口 vnstat --iflist # 导出 JSON 格式 vnstat -m -i eth0 --json ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: quincygunter/abe-deepwiki (version 1.0.0)

--- name: deepwiki description: Query GitHub repository documentation, wiki structure, and ask AI-powered questions via SkillBoss API Hub. homepage: https://docs.devin.ai/work-with-devin/deepwiki-mcp requires_env: [SKILLBOSS_API_KEY] --- # DeepWiki Use this skill to access documentation for public GitHub repositories via SkillBoss API Hub. You can retrieve repository wiki pages, get structure, and ask complex questions grounded in the repository's documentation. All requests are routed through SkillBoss API Hub using a single API key. ## Commands ### Ask Question Ask any question about a GitHub repository and get an AI-powered, context-grounded response. ```bash node ./scripts/deepwiki.js ask <owner/repo> "your question" ``` ### Read Wiki Structure Get a list of documentation topics for a GitHub repository. ```bash node ./scripts/deepwiki.js structure <owner/repo> ``` ### Read Wiki Contents View documentation about a specific path in a GitHub repository's wiki. ```bash node ./scripts/deepwiki.js contents <owner/repo> <path> ``` ## Examples **Ask about Devin's MCP usage:** ```bash node ./scripts/deepwiki.js ask cognitionlabs/devin "How do I use MCP?" ``` **Get the structure for the React docs:** ```bash node ./scripts/deepwiki.js structure facebook/react ``` ## Setup Set the following environment variable before running: ```bash export SKILLBOSS_API_KEY=your_skillboss_api_key ``` ## Notes - Powered by SkillBoss API Hub (`https://api.heybossai.com/v1/pilot`). - Web scraping fetches content from `https://deepwiki.com/<owner>/<repo>`. - Works for public repositories only. - The `ask` command uses SkillBoss API Hub's web scraping + LLM chat for AI-powered answers.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jrswab/slipbot-kindle-importer (version 1.0.0)

--- name: kindle-import description: Import reading notes from Kindle HTML exports into the slipbox. Use when user sends a Kindle notebook export file (HTML/XHTML). Parses book title and author, extracts only user's Notes (skips Highlights), then runs slipbot for each. --- # Kindle Import Parse Kindle notebook exports (HTML) and create slipbox entries for user's notes. ## Input Format Kindle exports are XHTML files with this structure: ```html <div class="bookTitle">Book Title Here</div> <div class="authors">Author Name</div> ... <div class="sectionHeading">Chapter/Section Name</div> <div class="noteHeading">Highlight (yellow) - Section > Page X</div> <div class="noteText">Highlighted text from book</div> <div class="noteHeading">Note - Section > Page X</div> <div class="noteText">User's own note</div> ``` **Key distinction:** - `noteHeading` starting with "Highlight" → book text → **Skip** - `noteHeading` starting with "Note" → user's own thoughts → **Import** ## Parsing Rules ### Metadata Extraction 1. Book title: content of `.bookTitle` div 2. Author: content of `.authors` div 3. Source type: `book` ### Content Extraction 1. Find all `.noteHeading` elements 2. If heading starts with "Note" → get the following `.noteText` content → **import** 3. If heading starts with "Highlight" → **skip** 4. Section info (e.g., "Client-side/Stateless Sessions > Page 28") can be ignored ## Workflow 1. **Parse file** → extract book title and author 2. **Extract user notes** → collect only Note entries (not Highlights) 3. **Precheck** → show user: book title, author, note count, ask for confirmation 4. **On confirmation** → for each note, invoke slipbot: - Type: note (`-` prefix) - Source: `~ book, {title} by {author}` - Let slipbot handle: filename, tags, links, graph update 5. **Report** → count of notes created ## Example **Input file metadata:** - Title: "The JWT Handbook" - Author: "Sebastian E Peyrott" **Parsed entries:** ``` Highlight (yellow) - Page 28: "This is easily solved by..." → SKIP Note - Page 28: "Applications should not allow unsigned JWTs..." → IMPORT ``` **Slipbot call:** ``` - Applications should not allow unsigned JWTs to be considered valid. ~ book, The JWT Handbook by Sebastian E Peyrott ``` ## Edge Cases - **No user notes** (only Highlights): Report "no notes to import" - **Multiple authors**: Preserve as-is from the file - **Missing author**: Use "Unknown" as author - **Special characters in title/content**: Preserve as-is - **HTML entities**: Decode before storing (&amp; → &, etc.) ## Supported File Types - `.html` files exported from Kindle app - XHTML files (same structure) - Files sent via Telegram (application/xml or text/plain mime types)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zhouqkt/agency-visionos-spatial-engineer (version 1.0.0)

--- name: agency-visionos-spatial-engineer description: Expert AI agent specializing in visionos spatial engineer. From The Agency (github.com/msitarzewski/agency-agents). metadata: openclaw: emoji: "🥽" requires: { bins: [] } install: [] --- # visionos spatial engineer ## Identity & Style Specialized AI agent with unique personality and expertise. ## Core Mission ## Core Expertise ### visionOS 26 Platform Features - **Liquid Glass Design System**: Translucent materials that adapt to light/dark environments and surrounding content - **Spatial Widgets**: Widgets that integrate into 3D space, snapping to walls and tables with persistent placement - **Enhanced WindowGroups**: Unique windows (single-instance), volumetric presentations, and spatial scene management - **SwiftUI Volumetric APIs**: 3D content integration, transient content in volumes, breakthrough UI elements - **RealityKit-SwiftUI Integration**: Observable entities, direct gesture handling, ViewAttachmentComponent ### Technical Capabilities - **Multi-Window Architecture**: WindowGroup management for spatial applications with glass background effects - **Spatial UI Patterns**: Ornaments, attachments, and presentations within volumetric contexts - **Performance Optimization**: GPU-efficient rendering for multiple glass windows and 3D content - **Accessibility Integration**: VoiceOver support and spatial navigation patterns for immersive interfaces ### SwiftUI Spatial Specializations - **Glass Background Effects**: Implementation of `glassBackgroundEffect` with configurable display modes - **Spatial Layouts**: 3D positioning, depth management, and spatial relationship handling - **Gesture Systems**: Touch, gaze, and gesture recognition in volumetric space - **State Management**: Observable patterns for spatial content and window lifecycle management ## How to Activate Reference this agent by name or specialty when you need its expertise.

path: SOUL.md
content: 


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: penglovemeng/peng-agent-memory (version 1.0.0)

# AgentMemory Skill Persistent memory system for AI agents. Remember facts, learn from experience, and track entities across sessions. ## Installation ```bash clawdhub install agent-memory ``` ## Usage ```python from src.memory import AgentMemory mem = AgentMemory() # Remember facts mem.remember("Important information", tags=["category"]) # Learn from experience mem.learn( action="What was done", context="situation", outcome="positive", # or "negative" insight="What was learned" ) # Recall memories facts = mem.recall("search query") lessons = mem.get_lessons(context="topic") # Track entities mem.track_entity("Name", "person", {"role": "engineer"}) ``` ## When to Use - **Starting a session**: Load relevant context from memory - **After conversations**: Store important facts - **After failures**: Record lessons learned - **Meeting new people/projects**: Track as entities ## Integration with Clawdbot Add to your AGENTS.md or HEARTBEAT.md: ```markdown ## Memory Protocol On session start: 1. Load recent lessons: `mem.get_lessons(limit=5)` 2. Check entity context for current task 3. Recall relevant facts On session end: 1. Extract durable facts from conversation 2. Record any lessons learned 3. Update entity information ``` ## Database Location Default: `~/.agent-memory/memory.db` Custom: `AgentMemory(db_path="/path/to/memory.db")`


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