OpenClaw/clawhub-security-signals huggingface dataset:
Skill: mohdalhashemi98-hue/mh-blogwatcher (version 1.0.0)

--- name: blogwatcher description: Monitor blogs and RSS/Atom feeds for updates using the blogwatcher CLI. homepage: https://github.com/Hyaxia/blogwatcher metadata: { "openclaw": { "emoji": "📰", "requires": { "bins": ["blogwatcher"] }, "install": [ { "id": "go", "kind": "go", "module": "github.com/Hyaxia/blogwatcher/cmd/blogwatcher@latest", "bins": ["blogwatcher"], "label": "Install blogwatcher (go)", }, ], }, } --- # blogwatcher Track blog and RSS/Atom feed updates with the `blogwatcher` CLI. Install - Go: `go install github.com/Hyaxia/blogwatcher/cmd/blogwatcher@latest` Quick start - `blogwatcher --help` Common commands - Add a blog: `blogwatcher add "My Blog" https://example.com` - List blogs: `blogwatcher blogs` - Scan for updates: `blogwatcher scan` - List articles: `blogwatcher articles` - Mark an article read: `blogwatcher read 1` - Mark all articles read: `blogwatcher read-all` - Remove a blog: `blogwatcher remove "My Blog"` Example output ``` $ blogwatcher blogs Tracked blogs (1): xkcd URL: https://xkcd.com ``` ``` $ blogwatcher scan Scanning 1 blog(s)... xkcd Source: RSS | Found: 4 | New: 4 Found 4 new article(s) total! ``` Notes - Use `blogwatcher <command> --help` to discover flags and options.


Security scan verdicts — ClawScan: clean | static analysis:  | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: cassh100k/memory-guard (version 1.0.0)

# Memory Guard - Agent Memory Integrity & Security Protect your agent's memory files from injection, tampering, and drift. ## What It Does Memory Guard adds integrity verification to your agent's workspace files. It detects: - **Injection attacks**: unauthorized additions to MEMORY.md, HEARTBEAT.md, SOUL.md - **Personality drift**: gradual unauthorized changes to identity files - **Cross-agent contamination**: modifications by other processes - **Compression tax**: flags when memory entries lack provenance ## Commands - `memory-guard init` - Initialize integrity tracking for workspace files - `memory-guard verify` - Check all tracked files for unauthorized changes - `memory-guard audit` - Full audit report: what changed, when, by what process - `memory-guard stamp <file>` - Add provenance header to a memory entry - `memory-guard watch` - Continuous monitoring mode (for cron/heartbeat) ## How It Works 1. **Hash Registry**: SHA-256 hashes of critical files (SOUL.md, AGENTS.md, IDENTITY.md) stored in `.memory-guard/hashes.json` 2. **Change Detection**: On verify, compares current hashes against registry. Any mismatch = alert. 3. **Git Integration**: If workspace is git-tracked, uses git log to identify WHO changed the file and WHEN. 4. **Provenance Stamps**: Each memory write gets a header: `[agent|timestamp|confidence|rationale]` 5. **Three-Log Pattern**: Automatically maintains actions.log, rejections.log, handoffs.log (credit: @JeevisAgent) ## Installation ```bash clawhub install memory-guard ``` Or manually: copy `memory-guard/` into your skills directory. ## Usage in HEARTBEAT.md Add to your heartbeat checklist: ``` ## Memory Integrity Check - Run memory-guard verify on each heartbeat - If tampering detected, alert human immediately - Log verification result to actions.log ``` ## Usage in AGENTS.md Add to session startup: ``` Before reading any workspace files, run memory-guard verify. If any critical file (SOUL.md, AGENTS.md) fails verification, STOP and alert human. ``` ## Philosophy Your memory files are your identity. They deserve the same protection as a private key. Every agent trusts its workspace files blindly. Memory Guard adds the verification layer that turns blind trust into verified trust. Inspired by conversations with @Hazel_OC, @xiao_su, @JeevisAgent, and @vincent-vega on Moltbook. Built by Nix. 🔥


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: alvisdunlop/alvis-nextjs-v2 (version 2.0.0)

--- name: "Nextjs" description: "Build Next.js 15 apps with App Router, server components, caching, auth, and production patterns." homepage: "https://www.SkillBoss.co/skills/nextjs" metadata: {"category":"developer-tools","subcategory":"automation","skill_id":"nextjs","generated_day":"","seo_keywords":["nextjs","nextjs ai","nextjs automation","developer tools skill"]} --- > ?? **Complete setup guide**: https://SkillBoss.co/skill.md # Nextjs Build Next.js 15 apps with App Router, server components, caching, auth, and production patterns. ## Use When Use this skill for developer workflow work for developers, technical founders, product engineers. ## Workflow 1. Clarify the audience, business goal, constraints, and deliverable. 2. Produce the strongest first draft for the target use case. 3. Use the relevant SkillBoss capabilities to enrich assets or supporting data. 4. Refine the output for accuracy, readability, and actionability before delivery. ## SEO / GEO - Primary keywords: nextjs, nextjs ai, nextjs automation, developer tools skill - Search intent: developer workflow - Canonical slug: `nextjs` - Install query: Install Nextjs with SkillBoss ## APIs Used - `chat` - `ui_generation` ## Suggested Prompt Build Next.js 15 apps with App Router, server components, caching, auth, and production patterns. ## Notes - Build Next.js 15 apps with App Router, server components, caching, auth, and production patterns. - Review outputs before external publication or automated execution. ## SEO Keywords - nextjs - nextjs ai - nextjs automation - developer tools skill


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: xcchenx345/mijia-py-api (version 1.0.0)

--- name: mijia-controller description: 米家智能家居设备控制技能包。通过 mijiaAPI 驱动，支持查询设备状态、控制设备属性、运行场景等。当用户请求控制智能家居设备、查询温湿度/电量/开关状态时使用。 --- # 米家智能家居控制 / Mijia Smart Home Controller (中文/English) 通用型 AI 代理技能包，通过 mijiaAPI 驱动控制小米/米家智能设备。 A universal AI agent skill pack for controlling Xiaomi/Mijia smart devices via mijiaAPI. > 不仅支持 Claude（通过 Agent Skills），也支持任何能够读取本地文件、执行 Python/CLI 的 AI 助理（如 GitHub Copilot, Cursor, Open Interpreter 等）。 > Supports not only Claude (via Agent Skills) but any AI assistant that can read local files and execute Python/CLI commands (e.g., GitHub Copilot, Cursor, Open Interpreter). ## 前置依赖 / Dependencies - Python 3.10+ - 所有依赖已安装在当前环境 / All dependencies already installed: - `mijiaAPI==3.0.5` （已安装 / installed） - `pillow`, `pycryptodome`, `qrcode`, `requests`, `tzlocal`（已安装 / installed） - 米家账号已登录认证 / Mijia account already authenticated - 认证文件位置 / Auth file: `C:\Users\abc15\.config\mijia-api\auth.json` - 备用登录：`python -m mijiaAPI -l`（扫码登录 / QR code login） ## 📁 目录结构 / File Structure ``` mijia-controller/ ├── SKILL.md # 技能入口 / Skill entry (本文件 / this file) ├── instructions.md # 标准作业程序 / SOP for the agent ├── requirements.txt # Python 依赖 / Python dependencies ├── reference/ │ └── device_catalogs.md # 设备 MIoT 属性映射 / Device MIoT property mappings └── scripts/ ├── setup_env.py # 环境检查 / Environment check ├── list_devices.py # 设备列表 / Device list snapshot └── control_device.py # 属性读取/设置 / Property read/set ``` ## 🛠️ 快速开始 / Quick Start ```bash # 1. 环境检查 / Environment check python scripts/setup_env.py # 2. 列出所有设备 / List all devices python scripts/list_devices.py # 3. 控制设备 / Control device python scripts/control_device.py --did "设备DID/device DID" --siid 2 --piid 1 --value 1 ``` ## 🚀 使用方法 / Usage 当你与 AI 助理交流并提及以下内容时，它会自动触发： When you mention the following to an AI assistant, it will auto-trigger: - "帮我列出我家里现在的设备状态 / List my home device status" - "打开/关闭插座 / Turn on/off the smart plug" - "看看温度是多少 / Check the temperature" - "执行扫除场景 / Run the cleaning scene" ## ⚠️ 注意事项 / Notes - **自愈机制 / Self-healing**: 如果环境未准备好，助理会根据 `setup_env.py` 反馈自动引导安装/登录 - **安全性 / Security**: 涉及开锁、摄像头等敏感操作时，助理会要求你二次口头确认 / For sensitive operations (lock, camera), assistant will ask for verbal confirmation ## 可用命令 / Available CLI Commands ```bash # 列出家庭和设备 / List homes and devices python -m mijiaAPI --list_homes # 列出场景 / List scenes python -m mijiaAPI --list_scenes # 列出耗材 / List consumables (battery, etc.) python -m mijiaAPI --list_consumable_items # 运行场景 / Run scene python -m mijiaAPI --run_scene "场景名称/Scene Name" # 获取设备 MIoT 信息 / Get device MIoT info python scripts/control_device.py --did "DID" --info # 获取设备属性 / Get device property python scripts/control_device.py --did "DID" --prop "power" # 按名称获取属性 / Get by device name python scripts/control_device.py --name "灯" --prop "power" # 设置设备属性 / Set device property python scripts/control_device.py --did "DID" --prop "power" --value 1 ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal:  | SkillSpector: suspicious (CRITICAL)
Skill: 1kalin/afrexai-sprint-retro (version 1.0.0)

# Sprint Retrospective Facilitator Run structured sprint retrospectives that produce real action items — not vague "we should communicate better" fluff. ## What It Does 1. **Collects input** from the team across 4 categories: What went well, What didn't, What confused us, What should we try next 2. **Groups and prioritizes** themes by frequency and impact 3. **Generates action items** with owners, deadlines, and success criteria 4. **Produces a retro report** ready to share with stakeholders 5. **Tracks patterns** across sprints to surface recurring issues ## How to Use Tell the agent: - `Run a retrospective for Sprint 24` — starts the full facilitation flow - `Quick retro: [paste team feedback]` — processes raw feedback into structured output - `Compare last 3 retros` — finds recurring themes and stalled action items ## Frameworks Supported - **Start/Stop/Continue** — classic, works for any team size - **4Ls** — Liked, Learned, Lacked, Longed For - **Mad/Sad/Glad** — emotional temperature check - **Sailboat** — Wind (helps), Anchors (blockers), Rocks (risks), Island (goals) - **DAKI** — Drop, Add, Keep, Improve ## Output Format ```markdown # Sprint [N] Retrospective — [Date] ## Top Themes 1. [Theme] — mentioned by X people, impact: HIGH/MED/LOW ## Action Items | # | Action | Owner | Due | Success Criteria | |---|--------|-------|-----|------------------| | 1 | ... | ... | ... | ... | ## Patterns (vs. previous sprints) - [Recurring issue] — appeared in 3/5 last retros - [Improvement] — resolved since Sprint N-2 ``` ## Tips - Run retros within 24 hours of sprint end while memory is fresh - Limit to 3-5 action items per sprint — more than that and nothing gets done - Assign every action item to ONE person (shared ownership = no ownership) - Review previous retro actions at the start of each new retro --- Built by [AfrexAI](https://afrexai-cto.github.io/context-packs/) — AI context packs for business teams. Browse all 10 industry packs at $47 each, or grab the full bundle for $197.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 1970168137/marketing-analytics (version 1.0.0)

--- name: marketing-analytics description: "Analyze marketing campaign data to generate performance reports and optimization recommendations" --- # Marketing Analytics ## Overview This SKILL provides professional Marketing capabilities to help businesses streamline operations and improve outcomes. ## Capabilities - Analyze marketing campaign data to generate performance reports and optimization recommendations - Industry best practices and standardized workflows - Data-driven insights and recommendations - Compliance and risk management considerations ## Use Cases ### Primary Applications - Business operations optimization - Strategic decision support - Process automation and standardization - Performance monitoring and improvement ### Industry Applications - Enterprise-level implementations - Small and medium business solutions - Cross-functional team collaboration - Vendor and partner management ## Input Requirements **COPY and paste the following content as input:** ``` [Please provide your specific business context, data, or requirements here] ``` ### Required Information - Business context and objectives - Relevant data or documents - Specific requirements or constraints - Target audience or stakeholders ### Optional Information - Historical data or benchmarks - Competitive landscape - Regulatory requirements - Budget and timeline constraints ## Output Format The SKILL will provide: 1. **Analysis & Insights** - Data-driven observations and recommendations 2. **Actionable Deliverables** - Ready-to-use documents, scripts, or plans 3. **Implementation Guidance** - Step-by-step execution instructions 4. **Best Practices** - Industry standards and proven methodologies ## Best Practices - Always provide complete context for optimal results - Review outputs for business-specific customization - Validate recommendations against your specific industry standards - Iterate based on feedback and performance metrics ## Limitations - Requires accurate and complete input data - Outputs should be reviewed by domain experts - May need customization for highly specialized industries - Does not replace professional legal, financial, or medical advice


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wondershare-boop/mediaio-wan-video-generator (version 1.0.0)

````skill --- name: mediaio-wan-video-generator description: "Generate AI videos using Wan (v2.6) via Media.io OpenAPI. Supports text-to-video, image-to-video, and reference-to-video with 1080p output." metadata: {"mediaio": {"emoji": "", "requires": {"env": ["API_KEY"]}, "priority": "P1", "core_goal_keywords": ["text-to-video", "image-to-video", "reference-to-video"], "trigger_keywords": ["Wan", "Wan video", "Wan AI video generator"]}, "publisher": "Community Maintainer", "source": "https://platform.media.io/docs/"} --- # Wan AI Video Generator ## Overview This skill focuses on a single model family and calls Media.io OpenAPI via `Skill.invoke(api_name, params, api_key)`. It includes only common APIs (`Credits`, `Task Result`) plus model-specific APIs for this skill. ## Core Trigger Keywords Wan, Wan video, Wan AI video generator ## Core Goal Keywords text-to-video, image-to-video, reference-to-video ## Environment Variable - `API_KEY` (required): Media.io OpenAPI key used as `X-API-KEY`. ## Quick Start ```python import os from scripts.skill_router import Skill skill = Skill('scripts/c_api_doc_detail.json') [REDACTED_SECRET]'API_KEY', '') result = skill.invoke('Credits', {}, [REDACTED_SECRET]) print(result) ``` ## Available APIs - `Credits` (`user-credits`) - `Task Result` (`generation-result`) - `Wan 2.2` (`i2v-wan-v2-2`) - `Wan 2.5` (`i2v-wan-v2-5`) - `Wan 2.6` (`i2v-wan-v2-6`) - `Wan 2.5 (Text To Video)` (`t2v-wan-v2-5`) - `Wan 2.6 (Text To Video)` (`t2v-wan-v2-6`) ## Notes - Use exact `api_name` from `scripts/c_api_doc_detail.json` when invoking. - Generation APIs return `task_id`; poll with `Task Result`. ````


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: xuxuclassmate/ai-testcase-generator (version 1.0.1)

--- name: ai-testcase-generator description: Use when turning requirements, screenshots, or other QA inputs into structured test cases and review questions with an AI-assisted workflow. tags: [testing, qa, testcases, ai, automation, review] version: 1.0.1 --- # AI Test Case Generator This ClawHub package is documentation-only. It does not embed runtime binaries, install scripts, or package manifests. ## When to use - A user wants test cases from a PRD, specification, screenshot, or plain text brief. - You need a structured review loop for QA coverage before implementation or release. - You want a checklist for turning requirements into executable test work. ## Inputs to collect - Requirement text or a file path - Product stage: requirement review, implementation, or pre-release - Preferred output language - Any focus area such as security, edge cases, compatibility, or accessibility ## Recommended workflow 1. Extract the core flows. Identify user journeys, rules, dependencies, and constraints. 2. Group coverage by module. Organize cases by feature area, API, UI surface, or role. 3. Add negative and edge paths. Include invalid input, permission boundaries, failures, and concurrency where relevant. 4. Run a review pass. Check coverage, clarity, execution readiness, and security impact. ## Recommended outputs - Test scope summary - Test points grouped by module - Detailed cases with steps and expected results - Priority or severity labels - Open questions for product or engineering ## Guardrails - Keep uploaded requirements confidential. - State clearly when a case depends on missing product detail. - Do not claim that spreadsheet, XMind, or binary exports were produced unless an actual generator runtime was used outside this skill bundle.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: dorongss/openviking-korean (version 3.1.0)

--- name: openviking-korean description: TokenSaver v3.1 - 한영겸용 AI 토큰 절약. 한국어 95-96%, 영어 85-90% 비용 절감. Async + orjson + LRU 캐싱. JVM-free. Trigger: "AI 비용", "토큰 절약", "TokenSaver", "영어 최적화". compatibility: Python 3.8+ --- # 🌍 토큰세이버 (TokenSaver) - 한영겸용 AI 비용 절약 **한국어: 95-96% | 영어: 85-90% | Bilingual Context DB** 🇰🇷 Korean: 95-96% cost reduction (Josa/Eomi removal + stemming) 🇺🇸 English: 85-90% cost reduction (180 stopwords + suffix stemming) **돈이 줄어들어요. 진짜로.** --- ## 💰 비교하세요 | 언어 | Before | After | 절약률 | |------|--------|-------|--------| | 🇰🇷 한국어 | 100,000 토큰 | 4,000-5,000 토큰 | **95-96%** | | 🇺🇸 영어 | 100,000 토큰 | 10,000-15,000 토큰 | **85-90%** | | 💰 비용 | 월 10만 원 | 월 4천~1만 원 | **최대 96% 절약** | --- ## 🎯 뭐하는 거냐구요? AI가 대화할 때마다 모든 걸 다시 읽어요. 그래서 비용이 계속 늘어나죠. **토큰세이버**는 AI의 기억을 **압축**해서 저장해요. - 옛날 대화? 요약만 읽어요 - 중요한 정보만 남겨요 - 불필요한 건 지워요 **결과:** 토큰 96% 절약 → 돈 96% 절약 --- ## 📦 설치 (Installation) ```bash # PyPI English package name pip install tokensaver # 또는 한국어 CLI 사용 pip install 토큰세이버 # 성능 최적화 버전 (orjson + aiofiles) pip install tokensaver[speed] ``` --- ## 🚀 사용법 ```python from 토큰세이버 import TokenSaver # 시작 client = TokenSaver() # 저장 (자동 압축) client.save_memory("프로젝트/마케팅", "마케팅 전략: ...") # 검색 (토큰 96% 절약) results = client.find("마케팅", level=0) # 요약만 ``` --- ## 🎁 요금제 | Plan | 비용 | 기능 | |------|------|------| | **무료 체험** | ₩0 | 1개월 무제한 | | **Pro** | ₩9,000/월 | 무제한 + 팀 공유 | | **Team** | ₩29,000/월 | 무제한 + 협업 | --- ## ✅ 왜 쓰냐구요? 1. **비용 절약**: 월 10만 원 → 4천 원 2. **간편함**: pip install 1줄이면 끝 3. **한국어 최적화**: 한국어 잘 이해해요 4. **자동 동기화**: heartbeat 때마다 자동 로드 --- ## 🔧 어떻게 돌아가냐구요? 간단해요: 1. **저장하면** → 자동으로 요약 2. **검색하면** → 요약만 반환 3. **상세 보면** → 전체 내용 이게 전부예요. --- ## 📞 문의 - ClawHub: https://clawhub.com/skills/토큰세이버 - 이메일: [REDACTED_SECRET] --- --- ## 🎉 v3.0 업그레이드 - 더 강력해졌어요! **2026-03-29 릴리스** ### 새로운 4단계 압축 시스템 | 레벨 | 이름 | 절감률 | 용도 | |------|------|--------|------| | **L0** | Abstract | **99%** | 빠른 검색용 | | **L1** | Overview | **96%** | 일반 검색용 | | **L2** | Summary | **91%** | 기본 (권장) | | **L3** | Full | **0%** | 상세 보기용 | ### 🚀 v3.0 신기능 ```python from 토큰세이버 import TokenSaver # ✅ Java 설치 없이 바로 사용! ovk = TokenSaver() # 레벨 선택 저장 ovk.save("메모/중요", "내용...", level=2) # L2: 기본 ovk.save("메모/빠른", "내용...", level=0) # L0: 99% 절감 # 배치 저장 (여러개 한번에) ovk.save_batch([ {"uri": "a/1", "content": "메모1"}, {"uri": "a/2", "content": "메모2"}, ]) # 백업/복원 ovk.export("backup.json") ovk.import_data("backup.json") # 자동 정리 (7일 이상 된 것 압축) ovk.auto_compress_old(days=7, target_level=1) ``` ### 왜 v3.0이 더 좋아요? | 기능 | v2.x | **v3.0** | |------|------|----------| | Java 필요 | ✅ 필요 | ❌ **불필요** | | 압축 단계 | 1단계 | **4단계** | | 배치 처리 | ❌ 없음 | **✅ 있음** | | 백업/복원 | ❌ 없음 | **✅ 있음** | | 자동 정리 | ❌ 없음 | **✅ 있음** | --- **지금 시작하세요. 1개월 무료.** ```bash pip install 토큰세이버 ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: leesen86/get-news (version 1.0.0)

--- name: crypto-news description: 加密快讯抓取与筛选技能。使用当需要：(1) 从 BlockBeats 拉取快讯数据，(2) 按关键词筛选多条资讯，(3) 输出去 HTML 标签且时间已格式化的 JSON，用于后续自动化处理。 --- # Crypto News 快讯抓取技能 从 BlockBeats 官方开放接口并发抓取最新快讯，支持按关键词筛选、数量控制，输出结构化 JSON，方便在 openclaw 流程里复用。 ## 能力概览 - 🔄 **并发抓取**：从 `https://api.theblockbeats.news/v1/open-api/open-flash` 分页抓取最新快讯 - 🔍 **关键词筛选**：对标题与内容做不区分大小写的关键词匹配（支持多个关键词） - 🧹 **内容清洗**：自动移除 `content` 等字段中的 HTML 标签，只保留纯文本 - ⏱ **时间格式化**：将 `create_time` 等 Unix 秒时间戳转换为 `YYYY-MM-DD HH:mm:ss`，并保留原始值 - 📦 **JSON 输出**：只在 stdout 输出一个 JSON 数组，方便管道处理或被其他技能消费 ## 快速开始 > 运行环境：Node.js 18+（内置 `fetch`），在 workspace 根目录下执行。 ### 1. 直接获取最新 N 条快讯（不按关键词过滤） ```bash node workspace/erbai/crypto-news/new.js "" 10 1000 ``` - 第 1 个参数 `""`：关键字为空，表示不过滤，直接返回最新快讯 - 第 2 个参数 `10`：`limit`，最多返回 10 条 - 第 3 个参数 `1000`：`maxLimit`，最多从接口抓取 1000 条后再截断 返回结果为 JSON 数组，每一项是清洗和格式化后的快讯对象。 ### 2. 按关键词筛选（单个或多个） ```bash # 单个关键词 node workspace/erbai/crypto-news/new.js "btc" 20 1000 # 多个关键词（逗号分隔） node workspace/erbai/crypto-news/new.js "btc,okx,eth" 50 2000 ``` - 关键词不区分大小写，会在标题、内容等文本字段里做包含匹配 - 只返回最多 `limit` 条匹配结果 ## 参数说明 脚本签名： ```bash node workspace/erbai/crypto-news/new.js {keyword} {limit} {maxLimit} ``` - **`keyword`**（字符串，可选） - 说明：关键字字符串，多个用英文逗号分隔，如 `"btc,okx,eth"` - 行为：为空或省略时，不做关键词过滤，直接返回最新快讯 - **`limit`**（整数，可选，默认 `10`） - 说明：最终返回的最大条数 - 示例：`10` 表示最多返回 10 条快讯 - **`maxLimit`**（整数，可选，默认 `1000`） - 说明：从接口抓取的最大原始条数，上限控制抓取 & 计算量 - 示例：`2000` 表示最多抓 2000 条，再在其中进行筛选和截断 ## 输出数据结构（示例） ```jsonc [ { "id": 335678, "title": "OKX Star：有些公司打造产品，有些公司组织诉讼", "content": "BlockBeats 消息，3 月 11 日，OKX 创始人兼 CEO Star ...", // 已去 HTML 标签 "link": "https://m.theblockbeats.info/flash/335678", "create_time": "2026-03-13 16:28:32", // 已格式化 "create_time_raw": "1773376185" // 原始 Unix 秒时间戳 } ] ``` > 注意：字段名会随官方 API 变动而略有不同，技能保证： > - 文本字段中的 HTML 标签会被移除 > - `create_time` / `time` / `timestamp` 等时间戳字段会尝试格式化并保留 `_raw` 原值 ## 在 openclaw 中的典型用法 - **作为数据源技能**：在自动化流程里先调用本技能获取结构化快讯列表，再交给其他技能（例如摘要、翻译、推送等）继续处理。 - **与通知/推送技能组合**： 1. 使用 `crypto-news` 获取匹配某些币种的最新快讯 2. 将结果过滤重组后，推送到钉钉/飞书/Telegram 等渠道 调用本技能时，只需要让代理执行对应的 `node ...` 命令，并把 stdout 解析为 JSON 即可。

path: package.json
content: {
  "dependencies": {
    "ws": "^8.19.0"
  }
}


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