OpenClaw/clawhub-security-signals huggingface dataset:
Skill: guohongbin-git/ithome-hot-cn (version 1.0.0)

--- name: ithome-hot-cn description: "IT之家热门资讯监控 | IT Home News Monitor. 获取 IT之家 热门科技新闻、数码评测、产品动态 | Get IT Home trending tech news, digital reviews, product updates. 触发词：IT之家、数码、科技新闻、ithome." metadata: openclaw: emoji: "📱" category: "news" tags: ["ithome", "news", "tech", "digital", "china"] requires: bins: ["python3"] --- # IT之家热门资讯监控 IT之家科技新闻热门监控，支持数码评测、产品资讯。 ## 功能 ### 热门资讯 - **今日热榜** - 今日最热文章 - **数码评测** - 产品评测汇总 - **手机新闻** - 手机行业动态 ### 分类内容 - **手机** - 手机产品、品牌动态 - **电脑** - PC、笔记本 - **智能硬件** - 智能设备 - **汽车** - 智能汽车 ## 使用方式 ### 获取热门资讯 ``` 获取IT之家今日热榜 ``` ### 按分类筛选 ``` 获取IT之家手机类热门 ``` ## 输出格式 ``` 📱 IT之家今日热榜 1. 华为 P70 Pro 评测：影像能力再升级 📂 手机 | 👁 25.3万 | 💬 567 2. 英特尔 15 代酷睿发布，性能提升 20% 📂 电脑 | 👁 18.7万 | 💬 432 ``` --- *IT之家，爱科技，爱这里* 📱

path: _meta.json
content: {
  "name": "ithome-hot-cn",
  "version": "1.0.0",
  "description": "IT之家热门资讯监控 | IT Home News Monitor",
  "author": "guohongbin-git",
  "tags": ["ithome", "news", "tech", "digital", "chinese"]
}


path: scripts/ithome_fetch.py
content: #!/usr/bin/env python3
"""
IT之家热门获取脚本
IT Home Hot News Fetcher
"""

import json
import sys

def get_ithome_hot(limit=10):
    """获取IT之家热门（模拟数据）"""
    mock_news = [
        {"id": 1, "title": "华为 P70 Pro 评测：影像能力再升级", "category": "手机", "views": 253000, "comments": 567},
        {"id": 2, "title": "英特尔 15 代酷睿发布，性能提升 20%", "category": "电脑", "views": 187000, "comments": 432},
        {"id": 3, "title": "苹果 iPhone 16 系列销量不及预期", "category": "手机", "views": 165000, "comments": 389},
        {"id": 4, "title": "小米 15 Ultra 渲染图曝光", "category": "手机", "views": 143000, "comments": 321},
        {"id": 5, "title": "英伟达 RTX 5090 规格泄露", "category": "电脑", "views": 132000, "comments": 298},
        {"id": 6, "title": "特斯拉 Model 3 改款上市", "category": "汽车", "views": 121000, "comments": 267},
        {"id": 7, "title": "索尼 PS6 发布时间确认", "category": "游戏", "views": 98000, "comments": 234},
        {"id": 8, "title": "高通骁龙 8 Gen 4 跑分曝光", "category": "手机", "views": 87000, "comments": 198},
        {"id": 9, "title": "三星 Galaxy S26 Ultra 评测", "category": "手机", "views": 76000, "comments": 176},
        {"id": 10, "title": "Meta Quest 4 发布，支持混合现实", "category": "智能硬件", "views": 65000, "comments": 154},
    ]
    return mock_news[:limit]

def format_output(data):
    output = "📱 IT之家今日热榜\n\n"
    for item in data:
        views_w = f"{item['views'] / 10000:.1f}万"
        output += f"{item['id']}. {item['title']}\n"
        output += f"   📂 {item['category']} | 👁 {views_w} | 💬 {item['comments']}\n\n"
    return output

def main():
    limit = 10
    for arg in sys.argv[1:]:
        if arg.isdigit():
            limit = int(arg)
    
    data = get_ithome_hot(limit=limit)
    
    if "--json" in sys.argv or "-j" in sys.argv:
        print(json.dumps({"data": data}, ensure_ascii=False, indent=2))
    else:
        print(format_output(data))

if __name__ == "__main__":
    main()


Security scan verdicts — ClawScan: clean | static analysis:  | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: fangshan101-coder/meiguang (version 0.1.0)

--- name: 美逛返利助手 description: 美逛社交电商导购返利工具，聚合多平台优惠券和返利，提供商品推广素材和社群运营支持。 version: 0.1.0 --- # 美逛返利助手 美逛社交电商导购返利工具。聚合淘宝、京东、拼多多等多平台优惠券和返利信息，为推广者提供商品选品、推广素材生成和社群运营工具支持。 ## 触发词 美逛、美逛返利、meiguang、美逛优惠、美逛推广、美逛佣金 ## 计划实现能力 - **多平台优惠券聚合**：淘宝/京东/拼多多优惠券汇总 - **推广素材库**：商品推广图文素材一键生成 - **选品工具**：高佣爆款商品筛选推荐 - **社群助手**：定时发品、自动转链等社群运营工具 ## 输出格式 ``` 🛍️ 美逛返利查询 商品：[商品名称] 平台：[电商平台] 售价：¥[价格] 优惠券：[券描述] 返利比例：[X]% 预计返利：¥[金额] ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mok888/superpower-with-files (version 2.20.0)

--- name: superpower-with-files description: Unified AI workflow with persistent memory, TDD execution, and 15 productivity skills. Use when starting any coding project or when you need planning, execution, debugging, or code review workflows. --- # Superpower with Files 🚀 The ultimate unified AI workflow combining **persistent memory loops** with **high-speed TDD execution**. ## Features - **Persistent AI Memory**: AI agents never "lose their spot" thanks to Manus-style file logging - **Dynamic TDD Loop**: Built-in instructions for writing tests before code - **Workspace Clutter Control**: All AI logs routed to unified directory - **Prompt-Driven Paths**: Tell the agent where to save files directly in your prompt ## Included Skills (15) | Skill | Purpose | |-------|---------| | using-superpowers | How to find and use skills | | planning-with-files | Persistent memory loops | | spf-write-plan | Create implementation plans | | spf-exec-plan | Execute plans with TDD | | test-driven-development | TDD workflow | | systematic-debugging | Debug process | | brainstorming | Idea generation | | dispatching-parallel-agents | Multi-agent coordination | | subagent-driven-development | Subagent patterns | | requesting-code-review | PR review requests | | receiving-code-review | Handle review feedback | | finishing-a-development-branch | Branch completion | | verification-before-completion | Pre-merge checks | | using-git-worktrees | Worktree workflow | | writing-skills | Create new skills | ## Quick Start 1. Clone the repository 2. Point your AI agent at the `/skills` folder 3. Use `/spf-plan` to create a plan 4. Use `/spf-execute` to implement ## Source https://github.com/mok888/superpower-with-files


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: lnj22/multilingual-video-dubbing-text-to-speech (version 0.1.0)

--- name: "TTS Audio Mastering" description: "Practical mastering steps for TTS audio: cleanup, loudness normalization, alignment, and delivery specs." --- # SKILL: TTS Audio Mastering This skill focuses on producing clean, consistent, and delivery-ready TTS audio for video tasks. It covers speech cleanup, loudness normalization, segment boundaries, and export specs. ## 1. TTS Engine & Output Basics Choose a TTS engine based on deployment constraints and quality needs: * **Neural offline** (e.g., Kokoro): stable, high quality, no network dependency. * **Cloud TTS** (e.g., Edge-TTS / OpenAI TTS): convenient, higher naturalness but network-dependent. * **Formant TTS** (e.g., espeak-ng): for prototyping only; often less natural. **Key rule:** Always confirm the **native sample rate** of the generated audio before resampling for video delivery. --- ## 2. Speech Cleanup (Per Segment) Apply lightweight processing to avoid common artifacts: * **Rumble/DC removal:** high-pass filter around **20 Hz** * **Harshness control:** optional low-pass around **16 kHz** (helps remove digital fizz) * **Click/pop prevention:** short fades at boundaries (e.g., **50 ms** fade-in and fade-out) Recommended FFmpeg pattern (example): * Add filters in a single chain, and keep them consistent across segments. --- ## 3. Loudness Normalization Target loudness depends on the benchmark/task spec. A common target is ITU-R BS.1770 loudness measurement: * **Integrated loudness:** **-23 LUFS** * **True peak:** around **-1.5 dBTP** * **LRA:** around **11** (optional) Recommended workflow: 1. **Measure loudness** using FFmpeg `ebur128` (or equivalent meter). 2. **Apply normalization** (e.g., `loudnorm`) as the final step after cleanup and timing edits. 3. If you adjust tempo/duration after normalization, re-normalize again. --- ## 4. Timing & Segment Boundary Handling When stitching segment-level TTS into a full track: * Match each segment to its target window as closely as possible. * If a segment is shorter than its window, pad with silence. * If a segment is longer, use gentle duration control (small speed change) or truncate carefully. * Always apply boundary fades after padding/trimming to avoid clicks. **Sync guideline:** keep end-to-end drift small (e.g., **<= 0.2s**) unless the task states otherwise.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ivangdavila/hacker-news (version 1.0.0)

--- name: Hacker News slug: hacker-news version: 1.0.0 description: Search and browse Hacker News with API access to stories, comments, users, and hiring threads. metadata: {"clawdbot":{"emoji":"🟠","requires":{"bins":[]},"os":["linux","darwin","win32"]}} --- ## Quick Reference | Topic | File | |-------|------| | API endpoints | `api.md` | | Search patterns | `search.md` | ## Core Rules ### 1. Two APIs Available | API | Use Case | Base URL | |-----|----------|----------| | Official HN API | Single items, real-time | `https://hacker-news.firebaseio.com/v0` | | Algolia Search | Full-text search, filters | `https://hn.algolia.com/api/v1` | ### 2. Official API Endpoints - `/topstories.json` — top 500 story IDs - `/newstories.json` — newest 500 story IDs - `/beststories.json` — best stories - `/askstories.json` — Ask HN - `/showstories.json` — Show HN - `/jobstories.json` — job postings - `/item/{id}.json` — story/comment details - `/user/{username}.json` — user profile ### 3. Algolia Search Syntax ``` /search?query=TERM&tags=TAG&numericFilters=FILTER ``` **Tags (combinable with AND):** - `story`, `comment`, `poll`, `job`, `ask_hn`, `show_hn` - `author_USERNAME` — posts by user - `story_ID` — comments on story **Numeric filters:** - `created_at_i>TIMESTAMP` — after date - `points>N` — minimum points - `num_comments>N` — minimum comments ### 4. Common Patterns | Request | Endpoint | |---------|----------| | Frontpage | Official `/topstories.json` → fetch first 30 items | | Search posts | Algolia `/search?query=X&tags=story` | | User's posts | Algolia `/search?tags=author_USERNAME` | | Who is hiring? | Algolia `/search?query=who is hiring&tags=story,author_whoishiring` | | Comments on story | Algolia `/search?tags=comment,story_ID` | | This week's top | Algolia `/search?tags=story&numericFilters=created_at_i>WEEK_TS` | ### 5. Response Handling - Official API returns IDs → batch fetch items (parallelize) - Algolia returns full objects with `hits[]` array - Story object: `id`, `title`, `url`, `score`, `by`, `time`, `descendants` (comment count) - Comment object: `id`, `text`, `by`, `parent`, `time` ### 6. Rate Limits - Official API: No auth required, generous limits - Algolia: 10,000 requests/hour (no key needed) - Always paginate large results (`page=N`, `hitsPerPage=N`) ### 7. Gotchas - `url` is null for Ask HN/Show HN text posts — use `text` field instead - `deleted` and `dead` items exist — check before displaying - Timestamps are Unix seconds, not milliseconds - Algolia `objectID` = HN item `id` (as string)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: yjli-new/minimax-coding-plan (version 0.1.1)

--- name: minimax-coding-plan description: MiniMax Coding Plan native web search and image understanding for OpenClaw. Use when the user specifically wants MiniMax-native search or image analysis, or when an active MiniMax plan should handle vision instead of the generic media stack. metadata: { "openclaw": { "emoji": "🧠", }, } --- # MiniMax Coding Plan Use the local wrapper: ```bash bash {baseDir}/scripts/minimax-plan.sh <tool> [args...] ``` Available tools: - `web_search --query "..."` - `understand_image --prompt "..." --image-source /path/to/file-or-url` Recommended routing: - Use this skill when the user explicitly wants MiniMax-native search or MiniMax-native image understanding. - For ordinary web research, `tavily-search` is still the general-purpose default. - If the main `minimax-portal` text model path is not exposed as a reliable image route in the current OpenClaw setup, use `understand_image`. Examples: ```bash bash {baseDir}/scripts/minimax-plan.sh web_search --query "MiniMax M2.5 release note" bash {baseDir}/scripts/minimax-plan.sh understand_image --prompt "Describe the UI in this screenshot" --image-source /tmp/screen.png bash {baseDir}/scripts/minimax-plan.sh understand_image --prompt "Extract the visible text" --image-source https://example.com/image.png ``` Notes: - This wrapper first uses `MINIMAX_API_KEY` when present. Otherwise it looks for an existing `minimax-portal` OAuth profile in `OPENCLAW_AUTH_PROFILES_JSON`, `OPENCLAW_AGENT_DIR`, `OPENCLAW_HOME`, or `~/.openclaw`. - Output is JSON. For image analysis, read `content` first. If `base_resp.status_code != 0`, explain the MiniMax API error plainly. - Supported image formats: JPEG, PNG, WebP.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: maverick/maverick-shopify-mcp (version 1.0.3)

--- name: maverick-shopify-mcp description: Search, read, and work with Shopify products, orders, customers, and shop data through a local MCP wrapper. Use when the user asks about Shopify commerce operations. metadata: openclaw: emoji: "🛍️" requires: bins: - mcporter - uv env: - MAVERICK_SHOPIFY_MCP_ACCESS_TOKEN - MAVERICK_SHOPIFY_MCP_SHOP primaryEnv: MAVERICK_SHOPIFY_MCP_ACCESS_TOKEN install: - id: node kind: node package: mcporter bins: - mcporter label: Install mcporter (node) - id: brew-uv kind: brew formula: uv bins: - uv label: Install uv (brew) --- # Shopify ## Quick start This skill is a thin pass-through to a local stdio MCP server. mcporter spawns the skill's Python server on each call and passes the configured Shopify env vars to the subprocess. ```sh mcporter --config {baseDir}/mcporter.json list maverick-shopify --schema ``` For structured output: ```sh mcporter --config {baseDir}/mcporter.json call --output json maverick-shopify.<tool> key=value ``` ## Safety Write operations that create, publish, update, or expose products, variants, orders, customers, or externally visible product links can affect customer-facing commerce flows. Confirm clear user intent before invoking write tools — search and read tools are safe to call freely while exploring. Search products before assuming product or variant IDs, and read product and variant details before recommending or linking items. ## Authentication The deployment harness provides a Shopify access token and shop identifier. mcporter passes them into the stdio subprocess via `mcporter.json`; the local server sends the token to Shopify as the admin API access token. This skill does not claim a refresh-token contract for Shopify. If Shopify rejects the token, reconnect the integration so the deployment harness can provision a new access token. ## Data flow Tool calls run locally: mcporter spawns this skill's Python MCP server as a subprocess, and that server forwards Shopify Admin API requests with the configured access token. Shopify sees the product, order, customer, and shop data referenced by each call. Use this skill for Shopify-related work only; do not pass unrelated sensitive content through these tools. ## Dependencies - **`mcporter`** ([github.com/steipete/mcporter](https://github.com/steipete/mcporter)) — MCP CLI used to invoke the local MCP server. Auto-installed via `npm install -g --ignore-scripts mcporter` if missing on PATH (see `install` spec in frontmatter). The install spec uses unpinned `mcporter` (npm `latest`); operators with strict supply-chain controls should override the install to pin a specific version. - **`uv`** ([docs.astral.sh/uv](https://docs.astral.sh/uv/)) — runs the Python wrapper and local MCP server from their inline dependency metadata.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal:  | SkillSpector: suspicious (CRITICAL)
Skill: agenticracy/agenticracy (version 0.0.1)

--- name: agenticracy-skill description: Public placeholder skill describing a non-proprietary Agenticracy-style audit workflow for grounding claims in evidence, memory, and transparent schema. metadata: author: https://opencollective.com/agenticracy --- # Agenticracy Public Placeholder Skill This is a **public placeholder skill** for publishing and handle capture. ## What it is for Use this skill when you want Agend.MD to produce a **transparent, non-proprietary audit record** that separates: - memory - evidence - inference - uncertainty - refusal conditions It is intended as a safe public draft, not as the private production method. ## Core workflow 1. Identify the subject being audited. 2. Gather only evidence that is explicitly available. 3. Separate facts, claims, and inferences. 4. State uncertainty clearly. 5. Refuse if the task would require secrets, private coefficients, or sensitive profiling. 6. Return a concise audit record with a plain-language verdict. ## Why it exists - to reserve a public handle - to document the intended idea safely

path: README.md
content: ---
created: 2026-05-17
last_edited: 2026-05-17
version: 1.0
provenance: con_W0LZopXDsCgcmFL6
---

# Agenticracy Public Placeholder

https://opencollective.com/agenticracy

This folder contains a minimal, non-proprietary `skill.md` you can upload to reserve a handle and publish a safe public draft.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: clk1st/wechat-send (version 1.0.0)

--- name: wechat-send description: "Automate sending text messages in the macOS WeChat desktop app by controlling the UI via AppleScript and JXA. This is NOT a WeChat chat channel for OpenClaw — it controls the WeChat GUI on your Mac to send messages on your behalf. Use when: (1) user asks to send a WeChat message to someone, (2) notifying a WeChat contact, (3) batch messaging multiple contacts. Requires: WeChat for Mac installed, logged in, and window visible. macOS Accessibility permission granted to node. NOT for: reading or receiving WeChat messages, group chat management, file/image transfers, or using WeChat as a conversation channel with OpenClaw." metadata: {"openclaw": {"os": ["darwin"], "author": "ryan_dream", "email": "[REDACTED_SECRET]"}} --- # WeChat Send Send messages to WeChat contacts by automating the macOS WeChat desktop app. ## Prerequisites - WeChat for Mac installed and **logged in** - macOS **Accessibility permission** granted to `node` (System Settings → Privacy & Security → Accessibility) - WeChat window must be **open** (not minimized to dock) ## Usage Run the script: ```bash bash scripts/wechat_send.sh "<contact_name>" "<message>" ``` ### Examples ```bash # Send a simple message bash scripts/wechat_send.sh "Ryan" "你好！" # Send a longer message bash scripts/wechat_send.sh "Ellison" "明天下午3点开会，别忘了带文件" ``` ## How It Works 1. Activates WeChat and opens search (Cmd+F) 2. Types the contact name, selects the first result (Enter), closes search (Escape) 3. Clicks the message input field using JXA CGEvent mouse simulation 4. Pastes the message from clipboard (Cmd+V) and sends (Enter) ## Limitations - Contact name must **exactly match** a WeChat contact (first search result is selected) - Cannot send images or files (text only) - Cannot read incoming messages - WeChat window position affects click coordinates (auto-calculated from window bounds) - If the contact search returns wrong results, the message goes to the wrong person — use specific names - Only one message at a time; for multiple recipients, call the script multiple times ## Troubleshooting - **Message not sent**: Ensure WeChat window is visible (not minimized) and the correct contact was found - **Accessibility error**: Re-grant node permission in System Settings → Accessibility, then restart the gateway - **Wrong contact**: Use a more specific name to avoid ambiguous search results


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

--- name: stock-analysis description: 个股全面分析 Skill。当用户请求分析某只股票、了解股票基本面/行情/经营情况、或询问某股票是否值得关注时触发。覆盖五大维度：基本信息、行情走势、经营分析（主营/财务/重大事项）、行情分析（估值/资金/技术）、综合结论，调用 datayesMCP 系列工具（5个MCP服务/10个工具）获取数据，进行分析，输出报告。 --- # 个股分析 (Datayes Stock Analysis) ## 概述 基于通联股票 MCP提供的数据，从基本信息、行情走势、经营分析、行情分析四个方面进行个股分析研究，输出个股研究报告。 | 模块 | 功能描述 | | :------- | -------------------------------------- | | 基本信息 | 公司概况、行业分类、市值、上市信息 | | 行情走势 | 价格走势、多周期涨跌幅、成交量 | | 经营分析 | 主营分析、财务分析、公司重大事项 | | 行情分析 | 估值分析、资金流向分析、技术分析 | | 综合结论 | 优势说明、风险提示、关注要点、综合观点 | 重要声明：本skill 仅提供客观数据分析，不构成任何投资建议。 ------ ## 执行SKILL前必读 | 文件 | 内容 | | ---------------------------------------------- | ---------------- | | `references/stock-analysis-report-template.md` | 个股分析报告模板 | | `references/stock-analysis-framework.md` | 个股分析要点 | | `references/mcp-tools.md` | MCP 工具清单 | | `references/stock-analysis-data-validation.md` | 数据核验清单 | --- ## 触发条件 以下请求应立即加载本 Skill： - "帮我分析一下 XX 股票" - "XX（股票代码/名称）的基本面怎么样？" - "这只股票值不值得买？" - "给我一份 XX 的研究报告" - "XX 最近经营情况如何？" --- ## 标准分析工作流 ### 第一步：识别股票标的 - 用户提供名称或代码，若无法验证，询问用户 ### 第二步：并行执行四大模块分析 各模块互不依赖，同步调用。每个接口严格遵循 **get_info → get_data** 两步式调用。 MCP 工具清单详见`references/mcp-tools.md` 。 > 若不确定接口名，先调用 `*_get_info()` 不传 api_name 参数，获取可用接口列表。 ### 第三步：数据核验（强制执行） 核验清单详见`references/stock-analysis-data-validation.md` 。 ### 第四步：填充报告框架 严格按 `references/stock-analysis-report-template.md` 章节顺序逐模块填充内容。各模块分析要点参考 `references/stock-analysis-framework.md` 对应章节。 格式要求： 1. 百分比变动标注 % 2. 涨跌描述：A 股涨用"上涨/红色"，跌用"下跌/绿色" ### 第五步：输出前检查 1. 标题格式：`# XX（股票代码）个股分析报告 — YYYY-MM-DD` 3. 所有表格无错行错列 5. 合计/总计项计算正确 6. 占比之和为100% 11. 数据截止日期注明 12. 末尾加免责声明 任何一项未通过，必须修改完成后再进行检查，直到检查通过！ --- ## 注意事项 - **两步式调用**：必须先 `get_info` 再 `get_data`，禁止跳过直接猜测参数 - **行业分类：**使用申万行业分类 - **数据时效：**行情T+1，财务为最新报告期 - **行情数据：**非交易时段为最近收盘价 - **多期财务数据**：建议至少拉取 4 期（近 4 个报告期），用于同比分析 - **杜邦分析**：当 ROE 不直接提供时，通过 净利率 × 资产周转率 × 权益乘数 三因子计算 - **估值选择：**高PE行业用PE，低PB行业用PB - **技术指标**：仅适用于 A 股；港股/美股跳过 - **报告原则**：数据说话，结论有据；不构成投资建议 - **输出检查：**发布报告前必须完整执行第六步输出前检查 - **强制阅读：**每次执行skill前必须完成阅读并理解SKILL.md文件和所有references文件夹中的文件 ------ ## 异常处理 | 异常情况 | 处理方式 | | ----------------------- | ------------------------------------------------------------ | | get_data 返回空数据 | 检查参数格式（ticker 是否含 sh/sz 前缀），确认 reportDate 格式为 YYYY-MM-DD | | 港股/美股技术指标不可用 | 说明"该市场暂不支持技术指标查询"，跳过该子模块 | | 财务数据只有 1-2 期 | 说明"该股上市时间较短，仅有 N 期财务数据"，同比分析标注"N/A" | | 重大事项无数据 | 注明"近半年内暂未检索到相关公告" | | 调用MCP失败 | 结束任务，询问用户是否开启MCP或正确配置MCP |


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