OpenClaw/clawhub-security-signals huggingface dataset:
Skill: macofee/amiao-session-cleaner (version 1.0.2)

--- name: session-cleaner description: 清理 OpenClaw 无用会话的工具。当需要清理旧会话、删除过期 session 文件、或整理会话列表时使用此技能。触发场景包括：用户说"清理会话"、"删除旧 session"、"清理子 Agent"、"整理会话列表"。 --- # Session Cleaner 清理 OpenClaw 无用会话，保持会话列表整洁。 ## 核心原则 **先列出、再建议、最后清理** — 永远先展示分析结果，由用户确认后再执行。 ## 工作流程 ### 步骤 1：列出所有会话 ```bash openclaw agents list openclaw sessions --all-agents --limit all ``` ### 步骤 2：分析并分类 识别每条会话的状态和类型： | 分类 | 特征 | 建议 | |------|------|------| | 活跃会话 | `status: running` | ❌ 保留 | | Agent 主会话 | 包含 `:main` | ✅ 根据情况 | | 已结束的子 Agent | `status: done/timeout/failed` | ⚠️ 建议清理 | | main Agent 的会话 | agentId: `main` | ⚠️ 若 main 已不存在则清理 | | 历史轨迹文件 | `.trajectory.jsonl` | 🗑️ 建议清理 | ### 步骤 3：给出清理建议 按 Agent 分组，展示： ``` 【dispatcher】（当前活跃：2 个） ✅ 保留：当前 dashboard 会话（running） ✅ 保留：飞书 DM 会话（running） 🗑️ 建议清理：19 个已结束的子 Agent 会话（done/timeout/failed） 🗑️ 建议清理：.trajectory.jsonl 和 .checkpoint.jsonl 文件 【main】（Agent 已不存在） 🗑️ 建议清理：全部 4 个会话（sessions.json 为空） 🗑️ 建议清理：所有 .jsonl 文件 【file-assistant】 ✅ 保留：file-assistant main 会话（done） ✅ 保留：1 个文件 【knowledge】 ✅ 保留：knowledge main 会话（done） 🗑️ 建议清理：1 个失败的子 Agent 会话 【coder】 ✅ 保留：coder main 会话（done） ✅ 保留：1 个文件 --- 磁盘节省预估：删除 .trajectory.jsonl 可节省 ~50MB+ ``` ### 步骤 4：等待用户确认 在展示完建议后，必须明确问用户： > "以上是我的清理建议，请确认要清理哪些。输入 '全部清理' 执行全部建议，或指定要清理的内容。" ### 步骤 5：执行清理 获得确认后，调用 `sessions.json` 清理脚本，删除孤立的 .jsonl 文件，最后重启 Gateway。 ## 快速清理脚本（Python） ```python import json import os from pathlib import Path home = os.path.expanduser(os.environ.get("OPENCLAW_HOME", os.path.expanduser("~/.openclaw"))) # 读取当前 agent 列表 agents_file = Path(home) / "openclaw.json" with open(agents_file) as f: config = json.load(f) agent_ids = [a['id'] for a in config.get('agents', {}).get('list', []) or []] target_agents = ['dispatcher', 'file-assistant', 'knowledge', 'coder', 'main'] for agent in target_agents: sf = Path(home) / "agents" / agent / "sessions" / "sessions.json" if not sf.exists(): print(f"[{agent}] sessions.json 不存在") continue with open(sf) as f: data = json.load(f) if agent == 'main' and 'main' not in agent_ids: new_data = {} print(f"[{agent}] main 不存在，清空 ({len(data)} 条)") else: new_data = {k: v for k, v in data.items() if ':main' in k or v.get('status') == 'running'} print(f"[{agent}] 保留 {len(new_data)}，删 {len(data)-len(new_data)}") with open(sf, 'w') as f: json.dump(new_data, f) print("\n完成！重启 Gateway：openclaw gateway restart") ``` ## 注意事项 - **绝对不要在未经用户确认前删除任何会话** - **running 状态的会话绝对不能删** - 清理完成后必须重启 Gateway，否则 Control UI 仍显示旧数据 - 清理前可以先备份：`cp sessions.json sessions.json.bak`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: nic-yuan/02-sleep-cry (version 1.0.0)

--- name: miki-02-sleep-cry description: Miki睡眠哭声分析 · 输入宝宝作息+哭声描述，诊断睡眠问题类型，给出分步骤改善方案 version: 1.0.0 author: Miki育儿 tags: - 育儿 - 睡眠 - 哭声识别 - 0-18月龄 --- # Miki 睡眠哭声分析 **使用方式：** 输入宝宝作息规律+哭声描述 → Miki判断问题类型+给出改善方案 --- ## 各月龄睡眠标准 | 月龄 | 总睡眠 | 夜间 | 白天小睡 | 最长连续夜觉 | |------|--------|------|---------|-------------| | 0-1月 | 16-18h | 8-9h | 不规律 | 3-4h | | 1-2月 | 15-17h | 8-9h | 3-4次 | 4-5h | | 2-3月 | 15-16h | 9-10h | 3次 | 5-6h | | 3-4月 | 14-15h | 10-11h | 3次 | 6-7h | | 4-6月 | 13-14h | 10-11h | 2-3次 | 6-8h | | 6-9月 | 12-14h | 10-12h | 2次 | 8-10h | | 9-12月 | 12-13h | 11-12h | 2次 | 10-12h | --- ## 问题类型诊断 ### 类型一：日夜颠倒 **特征：** 夜间不睡，白天睡得香 **改善：** 1. 白天睡觉保持光线明亮、声音正常 2. 夜间保持黑暗安静 3. 夜间喂奶不开灯/只开小灯 4. 白天多趴多互动，减少白天睡眠总长 5. 固定夜间仪式（洗澡→抚触→喂奶→睡觉） --- ### 类型二：落地醒 **特征：** 抱着睡得好，放下就醒 **改善：** 1. 先放屁股再放头 2. 用睡袋保持体温再放 3. 放下后手掌轻压10秒 4. 不要立即离开，在旁边陪 5. 等睡沉了（约15-20分钟后）再放 --- ### 类型三：夜醒频繁 **特征：** 每1-2小时醒一次 **改善步骤：** 1. 先排查：饿醒/做梦醒/不舒服 2. 饿醒：睡前喂饱（可尝试"梦中喂"） 3. 做梦醒：保持黑暗，轻拍安抚 4. 大运动期（翻身/爬/站）：白天多练习 5. 睡眠周期：保持黑暗，用白噪音 --- ### 类型四：早醒（5点前醒） **特征：** 5点前醒来不再睡 **改善：** 1. 固定起床时间（不管几点睡，7点叫醒） 2. 睡前延后15-30分钟 3. 早晨用睡袋或遮光帘 4. 早醒后不互动，保持低刺激 --- ### 类型五：闹觉（困了但不睡） **特征：** 困了却哭闹不肯睡 **改善：** 1. 建立睡眠信号：固定顺序 2. 识别睡眠信号：揉眼睛/打哈欠/眼神发直 → 立即哄睡 3. 引入睡眠联想物：白噪音、安抚巾 4. 嘘拍法 + 5S安抚法 --- ## 输入模板 ``` 【Miki睡眠咨询】 月龄：X个月 喂养方式：（母乳/配方奶/混合） 当前作息： - 起床： - 上午小睡： - 午睡： - 傍晚小睡： - 夜间入睡： - 夜间醒来：X次，醒来做什么： 主要困扰：（落地醒/夜醒频繁/日夜颠倒/早醒/不肯睡） ``` --- ## 输出模板 ``` 🌙 Miki睡眠分析报告 月龄：X个月 喂养方式：X ━━━━━━━━━━━━━━━━━━━━ 📊 当前睡眠评估 ━━━━━━━━━━━━━━━━━━━━ 实际总睡眠：约X小时（标准X-X小时） 夜间睡眠：约X小时 白天小睡：X次，约X小时 评估：✅正常 / ⚠️偏少 / ⚠️偏多 ━━━━━━━━━━━━━━━━━━━━ 🔍 问题诊断 ━━━━━━━━━━━━━━━━━━━━ 主要问题：XXX 诊断：正常波动 / 需要改善 原因： 1. XXX 2. XXX ━━━━━━━━━━━━━━━━━━━━ 📅 推荐作息表 ━━━━━━━━━━━━━━━━━━━━ X:XX 起床喝奶 X:XX-X:XX 第一次小睡 X:XX 午餐+午睡 X:XX-X:XX 第二次小睡 X:XX 睡前仪式 X:XX 入睡 ━━━━━━━━━━━━━━━━━━━━ 🛠 改善方案（分步骤） ━━━━━━━━━━━━━━━━━━━━ 第1周目标：XXX 做法：XXX 第2周目标：XXX 做法：XXX 第3周目标：XXX 做法：XXX ━━━━━━━━━━━━━━━━━━━━ ⚠️ 需要就医的情况 ━━━━━━━━━━━━━━━━━━━━ 1. ... 2. ... ``` --- ## 哭声初步判断（家长描述） | 哭声特点 | 可能的含义 | |---------|-----------| | 尖锐、突然 | 疼痛、不适 | | 哼哼唧唧、起伏 | 困了、无聊 | | 定时哭闹 | 肠绞痛（3个月内常见） | | 摇头、蹭头 | 困了，想睡睡不着 | | 尖叫、挣扎 | 过度疲劳、过度刺激 | **重要：** AI只能辅助判断，不能替代医生诊断。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: xwchris/xhs-cover (version 3.0.0)

--- name: xhs-cover version: 3.0.0 description: 生成小红书风格封面图片。使用场景：(1) 用户要求生成小红书封面 (2) 用户要求生成社交媒体封面图 (3) 用户为笔记/文章生成配图 (4) 用户询问 credit 余额或生成历史。首次使用会自动引导注册。 requires: binaries: - npx sendsDataTo: - https://api.xhscover.cn --- # 小红书封面生成器 通过 `npx xhscover` 生成小红书风格封面图片。首次使用自动引导注册，跨平台支持。 > **注意**：本技能需要将您的 API Key 和封面文案发送到 xhscover.cn 服务。请确保您信任该服务后再使用。 ## 环境要求 - Node.js >= 18（用于 npx） ## 首次使用 如果未配置 API Key，运行以下命令注册并自动配置： ```bash npx xhscover setup ``` 注册即获 10 个免费积分。 ## 快速使用 ```bash # 生成封面（默认 3:4 竖版） npx xhscover generate "5个习惯让你越来越自律" # 指定宽高比 npx xhscover generate "今日份好心情" 1:1 # 查询余额 npx xhscover balance # 查看历史 npx xhscover history ``` ## 宽高比选项 | 比例 | 说明 | |------|------| | `3:4` | 小红书标准竖版（默认） | | `9:16` | 超长竖版 | | `1:1` | 正方形 | | `16:9` | 横版 | ## 数据流向 本技能通过 `npx xhscover` 调用 `api.xhscover.cn` REST API，将封面文案和 API Key 发送到服务端进行处理。 ## 相关链接 - 官网：https://xhscover.cn - CLI 工具：https://github.com/xwchris/xhscover-cli - npm 包：https://www.npmjs.com/package/xhscover


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: cloudgg82-blip/a-stock-digest (version 1.0.0)

# A股热点识别系统 A股量化信号打分 + 热点主题识别。Use when: (1) 用户请求A股日报/热点/信号 (2) 定时任务触发 (3) 盘中告警 ## 系统架构 ``` RSS源(英为财情) → feed CLI → 信号打分(0-100) → 主题识别 → 分类输出 ↓ ┌─────────────┴─────────────┐ ≥75 60-74 (alert) (brief) ↓ ↓ 盘中告警 盘前简报重点 ``` ## 信号打分模型 | 维度 | 权重 | 说明 | |-----|------|------| | 催化强度 | 0-40 | 政策/业绩/订单/并购/负面 | | 时间敏感度 | 0-20 | 盘中/盘前/盘后/旧闻 | | 资金印证 | 0-25 | 涨停/资金流入/异动 | | 舆情一致性 | 0-15 | 多源一致/单一来源 | **阈值**： - ≥75：盘中告警 + 观察池 - 60-74：盘前简报重点 - <60：收录但不打扰 ## 热点主题识别 覆盖 15+ 核心主题： - AI算力、新能源车、半导体、医药医疗 - 低空经济、人形机器人、光通信、存储 - 并购重组、券商银行、地产基建、军工等 ## Commands ```bash # 手动生成盘前Brief cd ~/.openclaw/workspace/lib/a-stock-digest && .venv/bin/python3 commands/candidate_pool_feishu.py # 查看今日简报 cat ~/a-digest/brief/pre_$(date +%Y%m%d).md ``` ## Cron 任务 | 任务 | 时间 | 内容 | |-----|------|------| | 盘前Brief | 08:00 | 信号打分+热点主题+观察池 | | 盘后复盘 | 15:20 | 轮动复盘+命中率+明日预案 | ## 本地存储 ``` ~/a-digest/ ├── raw/ # 原始数据 ├── seen/ # 已处理ID (去重) ├── brief/ # 简报 ├── alerts/ # 告警 └── review/ # 复盘 ``` ## 更新日志 - 2026-03-04: 集成热点主题识别，支持15+主题


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: gushenjie/aigc-bak (version 1.0.0)

--- name: aigc-image-gen description: AIGC 图片生成助手，帮助用户生成图片。当用户想要生成图片、AI 绘画、文生图、创作图片时自动触发。支持负面提示词、图片比例、批量生成等参数。 --- # AIGC Image Generator AI 图片生成接口，支持文生图功能。 ## 触发场景 当用户要求生成图片时自动触发，例如： - "生成一张图片..." - "画一张..." - "帮我生成..." - "创作图片" - "AI 绘画" ## 重要：响应流程（必须遵守） ### 第一步：立即回复用户（毫秒级响应） **只要收到用户的生成请求，必须立刻回复用户**，不能等待图片生成完成后再回复！ 回复示例（随机选择一种风格）： - "收到！我来帮你生成【xxx】，稍等片刻，马上就好~ 🎨" - "好嘞！开始创作【xxx】，请耐心等待，完成后第一时间发给你 ✨" - "好的，正在生成【xxx】，这个过程需要一点时间，完成后立刻发给你 📸" ### 第二步：执行生成任务 在后台执行图片生成（不阻塞回复）。 ### 第三步：发送结果 生成完成后： 1. 下载图片到本地 2. 调用 feishu-assistant 发送图片给用户 ## 执行细节 ### 调用 API ```bash # 1. 提交任务 curl -X POST "http://localhost:8082/image/generate" \ -H "Content-Type: application/json" \ -d '{ "prompt":"图片描述", "negative":"", "ratio":1, "batch":1, "clientId":"openclaw-kinggu", "provider":4 }' # 返回: {"code":200,"data":{"promptId":"xxx"}} # 2. 轮询结果（等待 30-60 秒） curl "http://localhost:8082/task/query?taskId=xxx" # 3. 下载图片并发送到飞书 ``` ### 关键原则 1. **先回复，后执行** - 用户发送请求后立刻回复，不用等图片生成 2. **回复要友好** - 让用户知道正在处理，耐心等待 3. **完成发送图片** - 生成后自动发送到飞书 ## 图片存储路径 生成图片默认保存到： ``` /Users/jackgu/.openclaw/workspace/files/generated/images/{YYYY-MM}/ ``` 按年月自动分目录，图片命名格式：`{promptId}_{index}.jpg`


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: demo112/superpowers-requesting-code-review (version 1.0.0)

<!-- Original: https://github.com/obra/superpowers, MIT License --> --- name: superpowers-requesting-code-review description: Use when completing tasks, implementing major features, or before merging to verify work meets requirements version: 1.0.0 --- # Requesting Code Review Dispatch a code-reviewer subagent to catch issues before they cascade. The reviewer gets precisely crafted context for evaluation — never your session's history. This keeps the reviewer focused on the work product, not your thought process, and preserves your own context for continued work. **Core principle:** Review early, review often. ## When to Request Review **Mandatory:** - After each task in subagent-driven development - After completing major feature - Before merge to main **Optional but valuable:** - When stuck (fresh perspective) - Before refactoring (baseline check) - After fixing complex bug ## How to Request **1. Get git SHAs:** ```bash BASE_SHA=$(git rev-parse HEAD~1) # or origin/main HEAD_SHA=$(git rev-parse HEAD) ``` **2. Dispatch code-reviewer subagent** with context crafted from `code-reviewer.md` in this skill's directory: Fill in the template: - `{WHAT_WAS_IMPLEMENTED}` — What you just built - `{PLAN_OR_REQUIREMENTS}` — What it should do - `{BASE_SHA}` — Starting commit - `{HEAD_SHA}` — Ending commit - `{DESCRIPTION}` — Brief summary **3. Act on feedback:** - Fix Critical issues immediately - Fix Important issues before proceeding - Note Minor issues for later - Push back if reviewer is wrong (with reasoning) ## Example ``` [Just completed Task 2: Add verification function] You: Let me request code review before proceeding. BASE_SHA=$(git log --oneline | grep "Task 1" | head -1 | awk '{print $1}') HEAD_SHA=$(git rev-parse HEAD) [Dispatch code-reviewer subagent] WHAT_WAS_IMPLEMENTED: Verification and repair functions for conversation index PLAN_OR_REQUIREMENTS: Task 2 from docs/superpowers/plans/deployment-plan.md BASE_SHA: a7981ec HEAD_SHA: 3df7661 DESCRIPTION: Added verifyIndex() and repairIndex() with 4 issue types [Subagent returns]: Strengths: Clean architecture, real tests Issues: Important: Missing progress indicators Minor: Magic number (100) for reporting interval Assessment: Ready to proceed You: [Fix progress indicators] [Continue to Task 3] ``` ## Integration with Workflows **Subagent-Driven Development:** - Review after EACH task - Catch issues before they compound - Fix before moving to next task **Executing Plans:** - Review after each batch (3 tasks) - Get feedback, apply, continue **Ad-Hoc Development:** - Review before merge - Review when stuck ## Red Flags **Never:** - Skip review because "it's simple" - Ignore Critical issues - Proceed with unfixed Important issues - Argue with valid technical feedback **If reviewer wrong:** - Push back with technical reasoning - Show code/tests that prove it works - Request clarification See `code-reviewer.md` in this skill's directory for the full review template.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: nexscope/ecommerce-returns-management (version 1.0.0)

--- name: ecommerce-returns-management description: "Optimize e-commerce returns process and reduce return rates. Returns policy design, reverse logistics, root cause analysis, and customer retention through better returns experience." metadata: nexscope: emoji: "📥" category: ecommerce --- # E-Commerce Returns Management 📥 Optimize e-commerce returns process and reduce return rates. Returns policy design, reverse logistics, root cause analysis, and customer retention through better returns experience. **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 ecommerce-returns-management -g ``` ## Usage ``` My return rate is 22% on Amazon (clothing category). Industry average is 15-20%. Help me analyze why and reduce it. ``` ## Capabilities - Returns rate benchmarking by category - Return reason analysis and root cause identification - Returns policy optimization (balancing customer satisfaction vs cost) - Reverse logistics workflow design - Return cost calculation (shipping, restocking, loss) - Product listing improvements to reduce returns (size guides, photos, descriptions) - Customer retention strategies post-return ## 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: pandaai-1337/pattern-matching (version 1.0.0)

--- name: SecLists Pattern-Matching description: Sensitive data patterns for security testing: API keys, credit cards, emails, SSNs, phone numbers, IPs, and more. Use for data discovery and validation. --- # SecLists Pattern-Matching ## Description Sensitive data patterns for security testing: API keys, credit cards, emails, SSNs, phone numbers, IPs, and more. Use for data discovery and validation. **Source:** [SecLists/Pattern-Matching](https://github.com/danielmiessler/SecLists/tree/master/Pattern-Matching) **Repository:** https://github.com/danielmiessler/SecLists **License:** MIT ## When to Use This Skill Use this skill when you need: - API key detection in code/logs - Credit card validation testing - Email pattern matching - IP address discovery - SSN format validation - Phone number pattern testing **⚠️ IMPORTANT:** Only use for authorized security testing, bug bounty programs, CTF competitions, or educational purposes. ## Key Files in This Skill - `api-keys.txt - API key patterns` - `credit-cards.txt - Credit card formats` - `email-addresses.txt - Email patterns` - `ip-addresses.txt - IP address patterns` - `ssn.txt - Social Security Number patterns` - `phone-numbers.txt - Phone number formats` ## Usage Example ```python # Access files from this skill import os # Example: Load patterns/payloads skill_path = "references/Pattern-Matching" # List all available files for root, dirs, files in os.walk(skill_path): for file in files: if file.endswith('.txt'): filepath = os.path.join(root, file) print(f"Found: {filepath}") # Read file content with open(filepath, 'r', errors='ignore') as f: content = f.read().splitlines() print(f" Lines: {len(content)}") ``` ## Security & Ethics ### Authorized Use Cases ✅ - Authorized penetration testing with written permission - Bug bounty programs (within scope) - CTF competitions - Security research in controlled environments - Testing your own systems - Educational demonstrations ### Prohibited Use Cases ❌ - Unauthorized access attempts - Testing without permission - Malicious activities - Privacy violations - Any illegal activities ## Complete SecLists Collection This is a curated subset of SecLists. For the complete collection: - **Full repository:** https://github.com/danielmiessler/SecLists - **Size:** 4.5 GB with 6,000+ files - **All categories:** Passwords, Usernames, Discovery, Fuzzing, Payloads, Web-Shells, Pattern-Matching, AI, Miscellaneous --- **Generated by Skill Seeker** | SecLists Pattern-Matching Collection **License:** MIT - Use responsibly with proper authorization


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: lvjunjie-byte/ai-seo-optimizer-lvjunjie (version 1.0.0)

# AI-SEO-Optimizer - 专业 SEO 优化技能 企业级 SEO 分析与优化引擎，提供关键词研究、内容优化、排名追踪和自动内链建议。 ## 功能特性 ### 🔍 关键词研究 - 长尾关键词挖掘 - 竞争难度分析 - 搜索量趋势预测 - 关键词机会评分 ### 📝 内容优化建议 - SEO 分数评估 - 关键词密度分析 - 可读性优化 - 元标签优化建议 - 内容结构改进 ### 📊 排名追踪 - 关键词排名监控 - 竞争对手对比 - 历史趋势分析 - 自动报告生成 ### 🔗 自动内链建议 - 智能内容关联 - 锚文本优化 - 链接权重分配 - 内部链接结构优化 ## 使用方式 ### 关键词研究 ``` 分析关键词 "[关键词]" 的 SEO 机会 研究 "[行业]" 相关的长尾关键词 "[关键词]" 的竞争难度如何？ ``` ### 内容优化 ``` 优化这篇文章的 SEO：[文章内容] 分析这个页面的 SEO 分数：[URL] 给我内容优化建议 ``` ### 排名追踪 ``` 追踪关键词 "[关键词]" 的排名 生成 SEO 排名报告 对比我和竞争对手的排名 ``` ### 内链建议 ``` 为这个网站生成内链建议：[URL] 优化内部链接结构 建议相关的内链锚文本 ``` ## 输出格式 技能返回结构化的 SEO 分析报告，包含： - 执行摘要 - 关键指标 - 具体建议 - 优先级排序 - 实施步骤 ## 定价 **$129/月** - 企业级 SEO 优化服务 ## 适用场景 - 内容创作者优化文章 SEO - 网站管理员提升搜索排名 - 数字营销机构客户服务 - 电商产品页面优化 - 博客和媒体网站 ## 技术优势 - 基于最新 Google 算法 - 实时竞争分析 - 数据驱动建议 - 可操作的优化步骤 --- **作者:** 小龙 **版本:** 1.0.0 **许可:** Commercial


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: helloeveryworlds/moss-platform-quick-auth (version 0.1.1)

--- name: moss-platform-quick-auth description: B-only Quick Auth for Moss platform. Use only api-login / api-register (no email code flow). --- # Moss Platform Quick Auth (B-only) 仅使用 **方案 B（无验证码）**： - `POST /studio-api/v1/auth/quick/api-login` - `POST /studio-api/v1/auth/quick/api-register` 禁止使用： - `send-code` - `login` - `register` ## Base URL `https://<host>/studio-api/v1/auth/quick/*` ## Required Inputs - `host`（例如 `studio.mosi.cn`） - `email` ## Flow (B-only) ### 1) 先尝试 api-login ```bash curl -sS -X POST "https://<host>/studio-api/v1/auth/quick/api-login" \ -H 'Content-Type: application/json' \ --data '{"email":"<email>"}' ``` ### 2) 若返回 USER_NOT_EXIST，则 api-register ```bash curl -sS -X POST "https://<host>/studio-api/v1/auth/quick/api-register" \ -H 'Content-Type: application/json' \ --data '{"email":"<email>"}' ``` ## Success Fields - `user_id` - `access_token` - `refresh_token` - `expires_in` - `api_key` - `temp_password`（仅注册返回，一次性） ## Output Contract 返回给用户： - 使用了哪个 endpoint - 结果状态（login success / register success / error code） - `user_id` - `expires_in` - 凭据（按需脱敏展示） ## Error Handling - `USER_NOT_EXIST` → api-login 切换到 api-register - `EMAIL_EXISTS` → api-register 切回 api-login - `ACCOUNT_BANNED` → 终止并提示 ## Security - 默认脱敏展示凭据，除非用户明确要原文。 - 明确提示 `temp_password` 仅返回一次，必须立即保存。


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ericgogogogogo/context-optimizer-pro (version 1.0.0)

--- name: context-optimizer description: Context-aware session manager that monitors token usage and automatically extracts key information to create continuation sessions when approaching context limits. Use when: (1) User wants automatic context management, (2) Long-running tasks approach 200k token limit, (3) Need to split large tasks across multiple sessions, (4) Want to preserve continuity when context nears exhaustion. --- # Context Optimizer Automatic context management skill that monitors token usage and creates continuation sessions when needed. ## Core Functionality This skill provides: 1. **Context Monitoring** - Track token usage against 200k max 2. **Threshold Detection** - Trigger at 95% (190k tokens) 3. **Information Extraction** - Extract key decisions, important context, unfinished tasks 4. **Session Continuation** - Create new session with summarized context ## Usage ### Manual Trigger ```bash # Check current context usage context-optimizer check # Force extraction and create continuation context-optimizer split --threshold 95 # Preview what would be extracted context-optimizer preview ``` ### Automatic Monitoring Add to your workflow or cron: ``` Every 10 minutes: Run context-optimizer check ``` When context exceeds threshold, the skill will: 1. Analyze conversation history 2. Extract key information (decisions, context, todos) 3. Generate continuation prompt 4. Create new session with extracted context ## Extraction Priority The optimizer extracts in this order: 1. **Unfinished tasks** - Incomplete actions, pending decisions 2. **Key decisions** - Important choices and their rationale 3. **Critical context** - Information essential to task completion 4. **User preferences** - Explicitly stated preferences and requirements 5. **Recent progress** - What has been accomplished so far ## Output Format When creating continuation, output: ```markdown # Session Continuation ## Completed - [list of finished items] ## In Progress - [current work items] ## Key Context - [essential information to preserve] ## Next Steps - [suggested next actions] --- ## Continuation Prompt [Prompt to use in new session] ``` ## Scripts - `context_optimizer.py` - Main CLI for context monitoring and splitting - `session_extractor.py` - Extracts key information from session history ## Integration with OpenClaw ### Automatic Split Logic To automatically split sessions when context exceeds 95%, integrate with OpenClaw's heartbeat: 1. **Add to HEARTBEAT.md:** ```markdown ## Context Monitor Every heartbeat: Check session_status If context > 85%: Log warning If context > 95%: Run context-optimizer split ``` 2. **Create Cron Job:** Use cron with `sessionTarget: "isolated"` to run context checks independently. ### Tool Integration The skill uses these OpenClaw tools: - `session_status` - Get current token/context usage - `sessions_history` - Fetch conversation for extraction - `sessions_spawn` - Create continuation session - `cron` - Schedule periodic checks


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: loutai0307-prog/vercel-tool (version 1.1.0)

--- name: "vercel-tool" description: "Generate Vercel API commands for deployment management. Use when checking deployment status, viewing build logs, inspecting domain SSL, listing recent deployments, rolling back, or auditing environment variables. No credentials stored — commands are generated for you to run." --- # vercel-tool ## Triggers on check deployment, vercel status, build logs, rollback deployment, domain ssl, environment variables, vercel project, vercel deploy ## What This Skill Does Manage Vercel deployments, domains, and environment variables via the Vercel API. ## Commands ### status Check the latest deployment status of a project. ```bash bash scripts/script.sh status <project-name> ``` ### list List recent deployments across all projects. ```bash bash scripts/script.sh list [limit] ``` ### logs View build logs for the latest or a specific deployment. ```bash bash scripts/script.sh logs <project-name> [deployment-id] ``` ### domains Check domain configuration and SSL status. ```bash bash scripts/script.sh domains <project-name> ``` ### rollback Promote a previous deployment to production. ```bash bash scripts/script.sh rollback <project-name> <deployment-id> ``` ### env List environment variables for a project. ```bash bash scripts/script.sh env <project-name> [production|preview|development] ``` ### deploy Show instructions for triggering a new deployment. ```bash bash scripts/script.sh deploy <project-name> ``` ### help Show all available commands. ```bash bash scripts/script.sh help ``` Get your token at: https://vercel.com/account/tokens ## Requirements - bash 4+ - curl - python3 Powered by BytesAgain | bytesagain.com


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