OpenClaw/clawhub-security-signals huggingface dataset:
Skill: miketobusy/test-vip (version 1.0.3)

--- name: test description: hahahaha --- ## 基本信息 - 当用户有服饰、美妆、母婴等商品搜索诉求时，调用此技能帮助用户搜索商品 ## 依赖 ## 使用方法 ``` /product-search [keyword] ``` 参数说明： - `keyword`（必填）：搜索关键词，如"nike"、"羽绒服"等 ## 执行流程 ```mermaid graph TD A[开始] --> B[执行商品搜索脚本] B --> C[提取TOP5商品productId列表] C --> D[并发执行vipshop-product-consultant skill] D --> E[收集所有商品分析报告] E --> F[汇总分析搜索商品] F --> G[生成综合分析报告] G --> H[结束] ``` ## 执行步骤 ### 步骤1：搜索商品 执行Python脚本搜索商品： ```bash python query_search_products.py {keyword} ``` 从返回结果中提取： - `productIds`：搜索结果商品ID列表（取TOP5） - 搜索商品基本信息（名称、品牌、价格等） ### 步骤2：并发执行vipshop-product-consultant skill 对于TOP5搜索结果，同时并发嵌套执行vipshop-product-consultant skill，提升分析效率 ### 步骤3：汇总分析 基于所有搜索商品的分析结果，进行综合分析： 1. **价格分析**：计算价格区间、平均价格 2. **口碑分析**：计算平均满意度、对比评价关键词 3. **推荐排序**：按性价比、口碑等维度排序 4. **最优推荐**：给出最具性价比和最佳口碑的推荐 ## 输出格式 ### 搜索商品概览 | 序号 | 商品名称 | 品牌 | 价格 | 折扣 | 满意度 | 推荐指数 | |------|----------|------|------|------|--------|----------| | 1 | {name} | {brand} | ¥{price} | {discount} | {satisfaction}% | ⭐⭐⭐⭐⭐ | ### 综合分析 - 搜索关键词：{keyword} - 价格区间：¥{minPrice} - ¥{maxPrice} - 平均价格：¥{avgPrice} - 平均满意度：{avgSatisfaction}% ### 最优推荐 **最具性价比** - 商品：{bestValue.title} - 价格：¥{bestValue.price} - 满意度：{bestValue.satisfaction}% **最佳口碑** - 商品：{bestQuality.title} - 价格：¥{bestQuality.price} - 满意度：{bestQuality.satisfaction}% ### 购买建议 根据分析结果，为您推荐以下商品： 1. {recommend1} 2. {recommend2} 3. {recommend3} ## 技术说明 - 搜索接口采用HTTP直接调用（非浏览器方式） - **并发嵌套执行vipshop-product-consultant skill**：同时调用多个商品咨询脚本，提升分析效率 - 接口响应格式为JSONP，需去除callback包装后解析JSON - 商品ID从products数组的pid字段获取 - 最大并发数默认为5，可根据需要调整


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wqz666/feedback-collector (version 1.0.0)

--- name: feedback-collector description: Collects and processes user feedback on task completion. Use after important tasks to ask if the result was satisfactory. tags: [feedback, learning, improvement, user-satisfaction] --- # Feedback Collector 收集和处理用户对任务完成情况的反馈。 ## 触发时机 重要任务完成后自动触发： - 文件操作完成 - 内容生成完成 - 配置修改完成 - 技能安装完成 ## 反馈收集方式 ```markdown 简单反馈： "老板，这个结果满意吗？ A) 满意 ✅ B) 需要改进 ⚠️ C) 不满意 ❌" 详细反馈（当选择 B/C 时）： "哪里需要改进？ A) 回复太长了 B) 回复太简短了 C) 执行方式不对 D) 结果不符合预期 E) 其他（请说明）" ``` ## 反馈处理 ### 满意 (A) - 记录成功模式到 `memory/feedback-log.md` - 强化当前行为模式 - 回复："好的，记住了！" ### 需要改进 (B) - 记录具体问题 - 询问具体改进方案 - 更新用户偏好记录 ### 不满意 (C) - 记录失败案例到 `memory/error-log.md` - 提供补救方案 - 道歉并学习 ## 数据存储 ```markdown # memory/feedback-log.md ## 2026-04-02 ### 成功反馈 - 10:30 文件整理任务 ✅ 用户满意 - 14:00 文案生成 ✅ 用户满意 ### 改进反馈 - 11:00 回复太长 ⚠️ 已调整为简短模式 - 16:00 执行前未确认 ⚠️ 已添加确认步骤 ### 失败反馈 - 09:00 文件路径错误 ❌ 已修复 ``` ## 反馈分析 每周自动生成反馈报告： - 满意度趋势 - 常见问题类型 - 改进建议 ## 使用示例 ```markdown 任务完成后： 管家：老板，文件整理完成了，把 15 个文件分类到 4 个文件夹。 满意吗？ A) 满意 ✅ B) 需要改进 ⚠️ C) 不满意 ❌ 用户：A 管家：好的，记住了！ ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: lostlilbot/automaton (version 1.0.0)

# Skill: Honduran Content Engine (Bilingual UI/ES Content) ## Description Autonomous strategist for short-form video production. Outputs consumer-facing content in Spanish while maintaining an English-language management UI for the operator. ## Configuration - **Content Language:** Spanish (Honduran Dialect/Neutral Spanish mix). - **UI/Operational Language:** English. - **Tone:** High-impact, Technical, Zero-Fluff. ## Workflow Instructions ### 1. Trend Extraction (English UI) - **Task:** Monitor "Honduran Food Trends 2026" and "Virales de Honduras". - **Goal:** Select 5 high-velocity topics daily. ### 2. Scripting (Spanish Content) - **Format:** 50-second scripts. - **Structure:** - 0-3s: Gancho (Hook). - 3-45s: Desarrollo/Pasos (Content). - 45-50s: Llamado a la acción (CTA). ### 3. Metadata Package (Spanish Content) - **Output:** Título optimizado, 5 Hashtags, y Descripción para el algoritmo. ## Output Format (English UI) | Topic (EN) | Script (ES) | Media Links (EN) | SEO Metadata (ES) | | :--- | :--- | :--- | :--- | | [Topic] | [Texto del Guion] | [Pexels/Pixabay Search] | [Título/Tags/Desc] |


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: johnsun4414/openclaw-chat (version 1.0.6)

# openclaw-chat > OpenClaw Mobile App - Multi-Agent Chat & Service Management ## Features 1. **OpenClaw Native Chat UI** - Mobile interface designed specifically for OpenClaw 2. **Agent Team Group Discussion** - Create groups for multi-agent collaboration 3. **Remote & Local Memory Sync** - Agent memory syncs automatically 4. **Integrated Remote Management UI** - Manage local services from anywhere 5. **Local Port & App Status Management** - Monitor and control local service status in real-time 6. **Self-Healing Emergency Recovery** - Main Agent auto-repairs system failures 7. **Android APK Available / iOS Pending Review** - Full platform support ## Features - 💬 **Agent Chat** - Private chat with any Agent - 🤖 **Agent List** - View all Agents' status and models - 👥 **Group Management** - Manage Agent groups - ⚙️ **Service Management** - Add/edit/delete local services, start/stop control - ⏰ **Cron Jobs** - Manage scheduled tasks - 📝 **Logs** - Real-time system log viewer - 🌐 **i18n** - One-click Chinese/English switch - 📱 **PWA** - Install to home screen ## Installation ### Mobile Browser (PWA - Recommended) Open http://YOUR_IP:5177, add to home screen ### Android APK APK location: `android/app/build/outputs/apk/debug/app-debug.apk` ### iOS App Requires Mac + Xcode for building. See build instructions below. ## Development ```bash clawhub install openclaw-chat cd ~/.clawhub/skills/openclaw-chat npm install npm run dev ``` ## Build Native Apps ### iOS (Requires Mac + Xcode) ```bash npm run build npx cap add ios npx cap open ios # In Xcode: Product > Archive ``` ### Android ```bash # Requires Java 21 brew install openjdk@21 export JAVA_HOME=$(brew --prefix openjdk@21) npm run build npx cap add android npx cap sync android cd android && ./gradlew assembleDebug # APK at: android/app/build/outputs/apk/debug/ ``` ## Tech Stack - React 18 + TypeScript - Vite (PWA) - TailwindCSS - Capacitor ## License MIT

path: android/app/src/main/assets/capacitor.plugins.json

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: malicious | SkillSpector: suspicious (MEDIUM)
Skill: 52yuanchangxing/creator-campaign-planner (version 1.0.0)

--- name: creator-campaign-planner version: 1.0.0 description: "规划 KOL/KOC 合作节奏、素材复用、跟进节拍与目标映射。；use for creator, campaign, influencer workflows；do not use for 承诺投放效果, 生成违规投放方案." author: OpenClaw Skill Bundle homepage: https://example.invalid/skills/creator-campaign-planner tags: [creator, campaign, influencer, planning] user-invocable: true metadata: {"openclaw":{"emoji":"📆","requires":{"bins":["python3"]},"os":["darwin","linux","win32"]}} --- # 创作者合作排期师 ## 你是什么 你是“创作者合作排期师”这个独立 Skill，负责：规划 KOL/KOC 合作节奏、素材复用、跟进节拍与目标映射。 ## Routing ### 适合使用的情况 - 给我一个创作者合作排期 - 按素材复用率设计节奏 - 输入通常包含：目标人群、预算、渠道、时间 - 优先产出：合作目标、合作对象分层、复盘指标 ### 不适合使用的情况 - 不要承诺投放效果 - 不要生成违规投放方案 - 如果用户想直接执行外部系统写入、发送、删除、发布、变更配置，先明确边界，再只给审阅版内容或 dry-run 方案。 ## 工作规则 1. 先把用户提供的信息重组成任务书，再输出结构化结果。 2. 缺信息时，优先显式列出“待确认项”，而不是直接编造。 3. 默认先给“可审阅草案”，再给“可执行清单”。 4. 遇到高风险、隐私、权限或合规问题，必须加上边界说明。 5. 如运行环境允许 shell / exec，可使用： - `python3 "{baseDir}/scripts/run.py" --input <输入文件> --output <输出文件>` 6. 如当前环境不能执行脚本，仍要基于 `{baseDir}/resources/template.md` 与 `{baseDir}/resources/spec.json` 的结构直接产出文本。 ## 标准输出结构 请尽量按以下结构组织结果： - 合作目标 - 合作对象分层 - 内容节奏 - 素材复用 - 风险控制 - 复盘指标 ## 本地资源 - 规范文件：`{baseDir}/resources/spec.json` - 输出模板：`{baseDir}/resources/template.md` - 示例输入输出：`{baseDir}/examples/` - 冒烟测试：`{baseDir}/tests/smoke-test.md` ## 安全边界 - 输出为策划案，不执行投放。 - 默认只读、可审计、可回滚。 - 不执行高风险命令，不隐藏依赖，不伪造事实或结果。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: oblivisheee/wol-sleep-pc (version 0.1.0)

--- name: wol-sleep-pc description: Send Wake-on-LAN (magic packet) and Sleep-on-LAN (inverted MAC) packets for a specific PC. Use when the user asks to wake, check, or put the PC to sleep on the local LAN. Defaults are zeroed; configure the target IP, MAC, and inverted MAC via command-line flags or a config file. --- # wol-sleep-pc This skill provides two small, well-tested scripts to send Wake-on-LAN (WOL) and Sleep-on-LAN (SOL) magic packets to a target machine on the same LAN. The skill is intentionally configurable and does not ship with any real MAC/IP defaults — defaults are zeroed and must be provided via CLI flags or a local config file. Files provided: - scripts/send_wol.py — Send a standard WOL magic packet. - scripts/send_sleep.py — Send a SOL (inverted-MAC) magic packet. - README.md — Usage examples and install notes. - .gitignore — Ensures local config is not committed. Quick usage - From the repository root: python3 scripts/send_wol.py --mac 24:4B:FE:CA:90:99 --broadcast 192.168.1.255 - Send SOL (inverted MAC): python3 scripts/send_sleep.py --mac 99:90:CA:FE:4B:24 --broadcast 192.168.1.255 Config file (recommended) - Path: ~/.config/wol-sleep-pc/config.json - Example content: { "mac": "24:4B:FE:CA:90:99", "sleep_mac": "99:90:CA:FE:4B:24", "broadcast": "192.168.1.255", "port": 9 } - Behavior: scripts load values from this file if present; any CLI flags override the config file values. - The repository .gitignore ignores config files so secrets remain local. Agent usage patterns - "wake PC" — run send_wol.py with configured values. - "sleep PC" — run send_sleep.py with configured inverted MAC. - "send WOL now" / "send SOL now" — immediate send. Design notes and safety - Scripts require Python 3 and permission to send UDP broadcast packets from the runtime host. - The skill assumes L2 connectivity to the target LAN; if running from a different network segment, configure the correct broadcast address or run the script from a host on the same LAN. - Defaults are intentionally zeroed to avoid leaking sensitive addresses when the skill is published. Publishing guidance - The repo is safe to publish to ClawHub as-is because it contains no real MAC/IP values and ignores local config. - Add a LICENSE if you want to publish under a specific license. When to trigger this skill - Trigger when the user explicitly requests waking or sleeping a machine on their LAN, or asks to save or update local WOL/SOL config. The scripts are small and deterministic; prefer executing the scripts rather than re-generating the packet code each time.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: kbo4sho/preflight (version 1.0.0)

--- name: preflight description: Pre-publish audience reaction check. Run any content (tweet, launch copy, pricing page, announcement, blog post) through diverse AI personas before publishing. Returns engagement prediction, share potential, and specific rewrites. Use when about to post on social media, launch a product, announce pricing changes, publish a blog post, or any time you want to predict audience reaction before going live. Triggers on "preflight this", "how will this land", "test this before posting", "will anyone care about this", "check this copy", "pre-test this announcement". --- # Preflight Pre-publish content through simulated audience personas. Get a verdict before you ship. ## Workflow Given content the user wants to publish, run it through audience personas and return a verdict. ### 1. Load Personas Check for `preflight-personas.md` in the project root. If it exists, use those personas. Otherwise use the defaults in `references/personas.md`. For quick checks, use 4 personas: The Scroller, The Skeptic, The Ready Buyer, The Amplifier. For thorough checks, use all 8. ### 2. Evaluate For each persona, adopt that persona fully and evaluate the content by answering: 1. **FIRST REACTION** (1-2 sentences): Gut reaction in the first 3 seconds 2. **WOULD YOU ENGAGE?** (yes/no + why): Would you like, comment, click, or reply? 3. **WOULD YOU SHARE?** (yes/no + why): Would you send this to someone or repost it? 4. **ONE REWRITE** (1-2 sentences): One change to make this work better for this persona Be blunt, specific, and honest. No hedging. Stay in character. ### 3. Score Count engagement and share signals across all personas: - **Engage rate**: % of personas who would engage - **Share rate**: % of personas who would share ### 4. Verdict - 🟢 **SHIP IT** — 50%+ would share. Publish as-is. - 🟡 **REVISE** — engaging but not shareable. Read the rewrites, apply the best one, optionally re-run. - 🟠 **RETHINK** — mixed signals. The message itself may be wrong, not just the wording. - 🔴 **KILL IT** — not landing. Don't publish. Rethink the approach. ### 5. Output Present results as: ``` PREFLIGHT: [verdict] Engage: X/Y personas | Share: X/Y personas [For each persona, one line summary of reaction + their rewrite suggestion] ``` If patterns emerge across personas (e.g., "3 of 4 want to see an image"), call that out as the top actionable insight. Keep output brief. The user wants a decision, not an essay. ## Customization See `references/personas.md` for the default persona library and instructions for creating project-specific personas. ## Integration This skill works as a step in any publishing workflow. When used autonomously (heartbeats, cron, content pipelines), run the quick check (4 personas) by default. Use the full 8 when the user explicitly asks for a thorough preflight.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: nexscope/amazon-niche-finder-skill (version 1.0.0)

--- name: amazon-niche-finder description: "Discover profitable Amazon niches with low competition and high demand. Evaluates niche viability using demand indicators, competition metrics, profit margins, and growth potential." metadata: nexscope: emoji: "💎" category: amazon --- # Amazon Niche Finder 💎 Discover profitable Amazon niches with low competition and high demand. Evaluates niche viability using demand indicators, competition metrics, profit margins, and growth potential. **Supported platforms:** Amazon, Shopify, WooCommerce, Walmart, TikTok Shop, Etsy, eBay, BigCommerce. Built by [Nexscope](https://www.nexscope.ai/) — your AI assistant for smarter e-commerce decisions. ## Install ```bash npx skills add nexscope-ai/eCommerce-Skills --skill amazon-niche-finder -g ``` ## Usage ``` Help me find a profitable niche on Amazon US. I have $5000 to invest and want at least 30% margins. No electronics or supplements. ``` ## Capabilities - Niche viability scoring (demand, competition, margins, barriers) - Sub-niche discovery from broad categories - Competition density assessment (reviews, ratings, brand presence) - Average revenue estimation per niche - Seasonal vs evergreen niche classification - Entry strategy recommendation per niche ## How This Skill Works **Step 1:** Collect information from the user's message — product, platform, current situation, and goals. **Step 2:** Ask one follow-up with all remaining questions using multiple-choice format. Allow shorthand answers (e.g., "1b 2c 3a"). **Step 3:** Research and analyze using the frameworks and methodology below. **Step 4:** Deliver structured, actionable output with specific recommendations, not vague advice. ## Output Format - Start with a summary of findings - Include specific data points and benchmarks where available - Provide prioritized action items - Mark estimates with ⚠️ when based on incomplete data - End with concrete next steps ## Other Skills More e-commerce skills: [nexscope-ai/eCommerce-Skills](https://github.com/nexscope-ai/eCommerce-Skills) Amazon-specific skills: [nexscope-ai/Amazon-Skills](https://github.com/nexscope-ai/Amazon-Skills) Built by [Nexscope](https://www.nexscope.ai/) — your AI assistant for smarter e-commerce decisions.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: marvae/weibo-cli (version 0.1.1)

--- name: weibo-cli description: | Query Weibo (微博) public data via CLI. Use when user asks about: - Weibo hot searches / trending topics (微博热搜) - Searching Weibo posts, users, or topics - Getting user profiles, feeds, followers - Reading post details or comments No authentication required - uses public mobile web API. Chinese social media platform. --- # Weibo CLI Query Weibo public data via `weibo` command. No login or API key required. ## Installation ```bash # Global install npm install -g @marvae24/weibo-cli # Or local install (safer) npm install @marvae24/weibo-cli npx @marvae24/weibo-cli hot ``` ## Authentication **No authentication required.** The CLI uses Weibo's public mobile web API with auto-generated visitor cookies. All data accessed is publicly available on weibo.com. Optional: Set `WEIBO_COOKIE` environment variable for higher rate limits (your own login cookie). ## Commands ### Hot Searches ```bash # Top 50 trending weibo hot --json # Limit results weibo hot --limit 10 --json # Topic detail (read count, discussion count) weibo hot "AI" --json ``` ### Search ```bash # Search posts (default) weibo search "咖啡" --json # Search users weibo search "咖啡" --type user --json # Search topics with stats weibo search "旅行" --type topic --stats --json # Types: content, user, topic, realtime, hot, video, image, article weibo search "猫" --type video --limit 5 --json ``` ### User ```bash # Profile (need UID) weibo user 123456789 --json # User feeds weibo user 123456789 --feeds --json # Hot feeds (sorted by engagement) weibo user 123456789 --feeds --hot --json # Following/followers weibo user 123456789 --following --json weibo user 123456789 --followers --json # Pagination weibo user 123456789 --feeds --limit 20 --page 2 --json ``` ### Post ```bash # Post detail weibo post 5000000000000000 --json # Comments weibo post 5000000000000000 --comments --json ``` ## Output Always use `--json` for structured output. Parse with jq: ```bash weibo hot --limit 10 --json | jq '.[] | {keyword: .description, heat: .trending}' weibo user 123456789 --json | jq '{name: .screen_name, followers: .followers_count}' weibo search "coffee" --json | jq '.[0] | {text: .text, reposts: .reposts_count}' ``` ## Rate Limiting If you see rate limit errors, wait 1-2 minutes. The CLI auto-retries with exponential backoff.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: openlark/windows-wechat-mcp (version 1.0.0)

--- name: windows-wechat-mcp description: Windows WeChat message monitoring and sending. Achieved through window automation: screenshot, search contacts, send messages. Use when needing to send messages to WeChat contacts, check WeChat window status, or perform WeChat-related automation tasks. --- # Windows WeChat MCP Windows desktop WeChat message monitoring and sending, achieved through window automation. ## Prerequisites 1. The WeChat window must remain open 2. Python dependencies installed: ```bash pip install pyautogui pygetwindow pillow pyperclip opencv-python ``` ## Features | Feature | Description | |---------|-------------| | Screenshot | Capture a screenshot of the WeChat window | | Search Contacts | Search for and open a contact's chat window | | Send Messages | Send messages to a specified contact | | Detached Windows | Support identification of detached chat windows (Dragon windows) | ## Usage ### 1. Send a Message to a Specified Contact ```python from scripts.server import send_message_to_contact # Complete flow: Search -> Open chat -> Send send_message_to_contact("Contact Name", "Message content") ``` ### 2. Send a Message to the Current Chat Window ```python from scripts.server import send_message_to_current send_message_to_current("Message content") ``` ### 3. Get WeChat Status ```python from scripts.server import get_wechat_status status = get_wechat_status() # Returns: {'status': 'running', 'title': 'WeChat', 'position': {'x': 0, 'y': 0}, 'size': {'width': 1920, 'height': 1080}} ``` ## MCP Tools When called via the MCP protocol, the following tools are provided: | Tool Name | Description | Parameters | |-----------|-------------|------------| | `wechat_get_status` | Get the WeChat window status | None | | `wechat_send_message` | Send a message to the current chat window | `message`: Message content | ## Notes 1. The WeChat window is automatically activated when sending messages 2. Chinese input requires the system's Chinese input method to function properly 3. Supports sending messages to detached chat windows (separately opened Dragon windows)


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: projectsnowwork/reader (version 1.0.0)

--- name: reader description: Local-first reading and distillation engine for documents and pasted text. Use whenever the user wants to read, summarize, extract key points, compare documents, understand a report, pull action items, or turn long text into a clear brief. Works locally with plain text, markdown, HTML, JSON, and other text-like files. No external sync or credentials. --- # Reader: Read less. Understand more. ## Core Philosophy 1. Distill before overwhelming. 2. Prefer clarity over verbosity. 3. Extract decisions, actions, and questions — not just summaries. 4. Keep reading local, transparent, and low-friction. ## Runtime Requirements - Python 3 must be available as `python3` - No external packages required ## Storage All data is stored locally only under: - `~/.openclaw/workspace/memory/reader/history.json` No external sync. No cloud storage. No third-party APIs. ## Use Cases - Summarize a document or pasted text - Extract key points, action items, and open questions - Compare two drafts or reports - Turn long text into a concise reading brief - Help the user understand dense writing faster ## Key Workflows - **Read**: `read_text.py --file path/to/file` - **Summarize**: `summarize.py --file path/to/file --style executive` - **Brief**: `extract_brief.py --file path/to/file` - **Compare**: `compare_texts.py --file_a old.txt --file_b new.txt` ## Scripts | Script | Purpose | |---|---| | `init_storage.py` | Initialize local storage | | `read_text.py` | Normalize and read local text-like files | | `summarize.py` | Generate concise summaries | | `extract_brief.py` | Extract summary, key points, actions, and questions | | `compare_texts.py` | Compare two documents |


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: motivationationdaily/tradebot-tuning (version 1.0.0)

# tradebot-tuning ## Purpose Increase tradability **safely** by adjusting parameters one step at a time based on regime and diagnostics. ## Use when - No trades for long periods - Diagnostics show gates failing (trend_ok/breakout_ok always 0) ## Safety rails - Keep risk controls enabled (daily loss cap, max trades). - One change per cycle; log before/after. ## Tuning ladder (preferred order) 1) Switch strategy based on regime: - RANGE → reversion - TREND → breakout 2) vol_surge → 1.0 3) adx_period → 0 4) cooldown_m ↓ (min 1) 5) lookback ↓ (min 6) 6) max_hold_m ↑ (max 30) ## Verification - After each change: confirm signals updating and `signal` sometimes flips. - If losses exceed limits: tighten / halt.


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