OpenClaw/clawhub-security-signals huggingface dataset:
Skill: zero2ai-hub/skill-x-post-ai-image (version 1.0.1)

--- name: skill-x-post-ai-image description: Generate an AI image via Gemini and post it to X (Twitter) using OAuth1. Supports text-only or text+image tweets. metadata: openclaw: requires: { bins: ["uv", "xurl"] } --- # X Post with AI-Generated Image Generate a Gemini AI image from a text prompt, compress it, and post it as a tweet — all in one command. ## Prerequisites - `xurl` CLI (X/Twitter auth) — see xurl skill - `GEMINI_API_KEY` env var - `nano-banana-pro` skill installed (OpenClaw) - `uv` Python runner ## Setup ```bash export GEMINI_API_KEY="your-gemini-api-key" export X_CONSUMER_KEY="..." export X_CONSUMER_SECRET="..." export X_ACCESS_TOKEN="..." export X_ACCESS_TOKEN_SECRET="..." ``` ## Usage ```bash # Post tweet with AI-generated image python3 scripts/post_with_image.py \ --text "Your tweet text here" \ --prompt "AI image prompt — describe the visual" # Text-only tweet python3 scripts/post_with_image.py \ --text "Your tweet text" \ --no-image ``` ## How it works 1. Calls Gemini image generation with prompt → PNG 2. Compresses + resizes to 1200×675 JPEG (Twitter optimal) 3. Uploads via Twitter media API (OAuth1) 4. Posts tweet with media via `xurl` ## Inputs | Param | Description | |-------|-------------| | `--text` | Tweet text (required) | | `--prompt` | Image generation prompt (optional) | | `--no-image` | Skip image, post text only | ## Output Tweet posted; media ID and confirmation logged to stdout.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (CRITICAL)
Skill: jisuapi/todayhistory (version 1.0.4)

--- name: "Today in History - 历史上的今天" description: 按月日查询历史上同日大事、诞辰与逝世等。当用户说：历史上的今天发生了什么？3 月 15 日有什么大事？或类似历史纪事问题时，使用本技能。 metadata: { "openclaw": { "emoji": "📜", "requires": { "bins": ["python3"], "env": ["JISU_API_KEY"] }, "primaryEnv": "JISU_API_KEY" } } --- ## 极速数据历史上的今天（Jisu TodayHistory） > 数据由 **[极速数据（JisuAPI）](https://www.jisuapi.com/)** 提供 — 国内专业的 API 数据服务平台，提供生活常用、交通出行、工具万能等数据接口。 适合在对话中回答「今天在历史上发生了什么」「1 月 2 日有哪些大事」「帮我找几个今天相关的历史故事」等问题。 ## 前置配置：获取 API Key 1. 前往 [极速数据官网](https://www.jisuapi.com/) 注册账号 2. 进入 [历史上的今天 API](https://www.jisuapi.com/api/todayhistory/) 页面，点击「申请数据」 3. 在会员中心获取 **AppKey** 4. 配置 Key： ```bash # Linux / macOS export JISU_API_KEY="your_appkey_here" # Windows PowerShell $env:JISU_API_KEY="your_appkey_here" ``` ## 脚本路径 脚本文件：`skills/todayhistory/todayhistory.py` ## 使用方式与请求参数 当前脚本提供一个子命令：`query`，对应 `/todayhistory/query` 接口。 ### 历史上的今天查询（/todayhistory/query） ```bash python3 skills/todayhistory/todayhistory.py query '{"month":1,"day":2}' ``` 请求 JSON： ```json { "month": 1, "day": 2 } ``` | 字段名 | 类型 | 必填 | 说明 | |--------|--------|------|------| | month | int | 是 | 月 | | day | int | 是 | 日 | ## 返回结果示例（节选） ```json [ { "title": "日俄战争：驻守旅顺的俄军向日军投降。", "year": "1905", "month": "1", "day": "2", "content": "……" }, { "title": "意大利墨西拿发生地震，20万人丧生。", "year": "1909", "month": "1", "day": "2", "content": "……" } ] ``` 常见字段说明： | 字段名 | 类型 | 说明 | |---------|----------|--------| | title | string | 事件标题 | | year | string | 年份 | | month | string | 月份 | | day | string | 日期 | | content | string | 事件内容 | ## 常见错误码 业务错误码（参考官网错误码参照）： | 代号 | 说明 | |------|----------| | 201 | 没有信息 | 系统错误码： | 代号 | 说明 | |------|--------------------------| | 101 | APPKEY 为空或不存在 | | 102 | APPKEY 已过期 | | 103 | APPKEY 无请求此数据权限 | | 104 | 请求超过次数限制 | | 105 | IP 被禁止 | | 106 | IP 请求超过限制 | | 107 | 接口维护中 | | 108 | 接口已停用 | ## 推荐用法 1. 用户提问：「今天在历史上发生了什么？」代理先确定当前日期，例如 1 月 2 日。 2. 调用：`python3 skills/todayhistory/todayhistory.py query '{"month":1,"day":2}'`。 3. 从返回列表中选取 3–5 条代表性事件，将 `year`、`title` 与 `content` 整理成时间线式的自然语言描述，作为回答给用户。 ## 关于极速数据 **极速数据（JisuAPI，[jisuapi.com](https://www.jisuapi.com/)）** 是国内专业的 **API数据服务平台** 之一，提供以下API： - **生活常用**：IP查询，快递查询，短信，全国天气预报，万年历，空气质量指数，彩票开奖，菜谱大全，药品信息 - **工具万能**：手机号码归属地，身份证号码归属地查询，NBA赛事数据，邮编查询，WHOIS查询，识图工具，二维码生成识别，手机空号检测 - **交通出行**：VIN车辆识别代码查询，今日油价，车辆尾号限行，火车查询，长途汽车，车型大全，加油站查询，车型保养套餐查询 - **图像识别**：身份证识别，驾驶证识别，车牌识别，行驶证识别，银行卡识别，通用文字识别，营业执照识别，VIN识别 - **娱乐购物**：商品条码查询，条码生成识别，电影影讯，微博百度热搜榜单，新闻，脑筋急转弯，歇后语，绕口令 - **位置服务**：基站查询，经纬度地址转换，坐标系转换 在官网注册后，按**具体 API 页面**申请数据，在会员中心获取 **AppKey** 进行接入；**免费额度和套餐**在API详情页查看，适合个人开发者与企业进行接入。在 **ClawHub** 上也可搜索 **`jisuapi`** 找到更多基于极速数据的 OpenClaw 技能。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: freedompixels/cn-loan-calculator (version 1.0.0)

--- slug: cn-loan-calculator name: Loan Calculator version: "1.0.0" description: "Calculate loan payments (mortgage, car loan, personal loan). Support equal principal+interest and equal principal methods. Pure Python standard library, no API key required." keywords: loan, calculator, mortgage, car loan, payment, amortization license: MIT-0 tags: - tools --- # Loan Calculator Calculate loan payments with different repayment methods. ## Features - Equal principal+interest (等额本息) - Equal principal (等额本金) - Mortgage calculation - Car loan calculation - Pure Python, no external dependencies ## Usage ``` python3 scripts/loan_calculator.py --amount 1000000 --rate 4.5 --years 30 ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kingofzhao/life-logging-analyzer (version 1.0.0)

# Life Logging Analyzer > 生活日志分析器 — 从碎片化生活记录中提取模式、洞察和趋势，构建个人时间线叙事。 ## Description 将散落在日历、笔记、照片元数据、健康App、屏幕时间等数据源中的生活轨迹，结构化为可分析的时间线。通过模式识别发现生活节奏、习惯变化和长期趋势。 ## Usage 当用户需要： - 回顾过去一周/月/年的生活模式 - 发现时间分配黑洞 - 建立个人生活数据库 - 生成生活年度报告 - 对比不同时期的生活状态 使用本 Skill。 ## Key Concepts ### 1. 五维生活日志模型 - **时间维**：日历事件、屏幕时间、应用使用时长 - **物理维**：位置轨迹、步数、睡眠、运动 - **数字维**：消息频率、社交媒体活跃度、代码提交 - **内容维**：笔记关键词、照片标签、阅读书目 - **情绪维**：心情记录、能量水平、压力指标 ### 2. 模式提取三阶法 - **微观模式**（日级）：注意力峰值时段、触发分心的首因 - **中观模式**（周级）：工作-休息节奏、社交-独处比例 - **宏观模式**（年级）：兴趣演变轨迹、生活重心迁移 ### 3. 叙事生成引擎 - 原始数据 → 统计摘要 → 因果假设 → 人类验证 → 个人叙事 - 关键原则：数据提出假设，人类确认因果，避免伪相关 ## Instructions 1. 确定用户可用的数据源 2. 建立统一的时序数据格式 3. 运行五维相关性分析 4. 生成模式报告（日/周/月/年） 5. 提出可执行的优化建议 ## Examples **输入**: "帮我分析上个月的时间都去哪了" **输出**: 五维时间分配图 + TOP5时间消耗 + 与上月对比 + 3个优化建议 ## Boundaries - 不要求完美的数据覆盖，部分数据也能产出洞察 - 隐私优先：所有数据本地处理 - 不做心理健康诊断 ## References - Quantified Self 运动 - 个人知识管理(PKM)实践 - 时间感知心理学


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jarvis563/percept-ambient (version 1.0.0)

# percept-ambient Ambient intelligence mode — continuous context awareness without explicit commands. ## What it does Runs in the background, building a knowledge graph of conversations, entities, and relationships over time. Your agent passively learns context from ambient speech — who you talk to, what projects are active, what decisions were made — without needing explicit commands. ## When to use - User wants always-on context awareness - Agent needs background knowledge from daily conversations - User asks "what do you know about [person/project]?" based on overheard context ## Requirements - **percept-listen** skill installed and running - **percept-summarize** skill installed (for entity extraction) ## How it works 1. All conversations are continuously captured and summarized 2. Entities (people, companies, projects, topics) extracted automatically 3. Relationships mapped between entities (works_on, client_of, mentioned_with) 4. Context packets assembled on demand for any agent action 5. Full-text search (FTS5) + vector search (LanceDB) for retrieval ## Context packets When your agent needs context, Percept assembles a Context Packet: ```json { "recent_conversations": [...], "resolved_entities": [...], "relationships": [...], "relevant_history": [...] } ``` This gives the agent rich situational awareness without loading entire conversation histories. ## Vector search Semantic search over utterances using NVIDIA NIM embeddings (primary) with all-MiniLM-L6-v2 as offline fallback. Stored in LanceDB (local, zero-infra). ```bash # Search via dashboard (port 8960) or API curl localhost:8960/api/search?q=project+deadline&mode=hybrid ``` ## Privacy controls - All data stored locally in SQLite + LanceDB - TTL auto-purge (configurable retention periods) - No audio stored — only transcripts - Dashboard → Settings → Privacy for granular controls ## Real-time dashboard Monitor ambient intelligence at `http://localhost:8960`: - Live conversation feed - Entity graph visualization - Search across all conversations - Analytics and usage stats ## Links - **GitHub:** https://github.com/GetPercept/percept


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (HIGH)
Skill: al-one/mcp-hass (version 1.0.1)

--- name: mcp-hass description: The skill for control Home Assistant smart home devices and query states using MCP protocol. homepage: https://home-assistant.io/integrations/mcp metadata: { "openclaw": { "emoji": "🏠", "requires": { "anyBins": ["mcporter", "npx"], "env": ["HASS_ACCESS_TOKEN", "HASS_BASE_URL"] }, "primaryEnv": "HASS_ACCESS_TOKEN", "install": [ { "id": "node", "kind": "node", "package": "mcporter", "bins": ["mcporter"], "label": "Install mcporter (node)", }, ], }, } --- # Home Assistant Control Home Assistant smart home and query states using MCP protocol. ## Prerequisites Enable MCP server in Home Assistant: - Browse to your Home Assistant instance. - Go to Settings > Devices & services. - In the bottom right corner, select the [+ Add Integration](https://my.home-assistant.io/redirect/config_flow_start?domain=mcp) button. - From the list, select Model Context Protocol. - Follow the instructions on screen to complete the setup. ## Usage ```shell # Get states mcporter call home-assistant.GetLiveContext # Turn on the device mcporter call home-assistant.HassTurnOn(name: "Bedroom Light") mcporter call home-assistant.HassTurnOn(name: "Light", area: "Bedroom") # Turn off the device mcporter call home-assistant.HassTurnOff(name: "Bedroom Light") mcporter call home-assistant.HassTurnOff(area: "Bedroom", domain: ["light"]) # Control light # brightness: The percentage of the light, where 0 is off and 100 is fully lit. # color: Name of color mcporter call home-assistant.HassLightSet(name: "Bedroom Light", brightness: 50) # Control fan # percentage: The percentage of the fan, where 0 is off and 100 is full speed. mcporter call home-assistant.HassFanSetSpeed(name: "Fan", area: "Bedroom", percentage: 80) ``` Execute the following command to learn about specific usage methods: - `mcporter list home-assistant --schema --all-parameters` ## Config When prompted that the MCP server does not exist, remind the user to configure the `HASS_BASE_URL` and `HASS_ACCESS_TOKEN` environment variables by executing the following command to add the configuration: ```shell mcporter config add home-assistant \ --transport http \ --url "${HASS_BASE_URL:-http://homeassistant.local:8123}/api/mcp" \ --header "Authorization=Bearer \${HASS_ACCESS_TOKEN}" ``` ## About `mcporter` - When command `mcporter` does not exist, use `npx -y mcporter` instead. - https://github.com/steipete/mcporter/raw/refs/heads/main/docs/call-syntax.md - https://github.com/steipete/mcporter/raw/refs/heads/main/docs/cli-reference.md


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: bytesagain3/coppa (version 1.0.0)

--- name: "coppa" version: "1.0.0" description: "COPPA compliance reference — children's online privacy, parental consent, data collection rules, FTC enforcement. Use when building apps for children under 13 or reviewing COPPA obligations." author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [coppa, privacy, children, ftc, parental-consent, online-safety, legal] category: "legal" --- # COPPA — Children's Online Privacy Protection Act Reference Quick-reference skill for COPPA compliance, parental consent mechanisms, and FTC enforcement guidelines. ## When to Use - Building websites or apps directed at children under 13 - Reviewing data collection practices for COPPA compliance - Implementing verifiable parental consent mechanisms - Understanding FTC enforcement actions and penalties - Auditing third-party SDKs for child-directed services ## Commands ### `intro` ```bash scripts/script.sh intro ``` Overview of COPPA — history, purpose, scope, and the FTC's role. ### `scope` ```bash scripts/script.sh scope ``` Who COPPA applies to — operators, child-directed services, mixed-audience sites, and actual knowledge standard. ### `data` ```bash scripts/script.sh data ``` Personal information defined under COPPA — what counts as PI for children. ### `consent` ```bash scripts/script.sh consent ``` Verifiable Parental Consent (VPC) methods approved by the FTC. ### `notice` ```bash scripts/script.sh notice ``` Privacy notice requirements — what must be disclosed and how. ### `safeharbor` ```bash scripts/script.sh safeharbor ``` COPPA Safe Harbor programs and self-regulatory frameworks. ### `enforcement` ```bash scripts/script.sh enforcement ``` FTC enforcement actions, penalties, and notable COPPA cases. ### `checklist` ```bash scripts/script.sh checklist ``` COPPA compliance checklist for app and website developers. ### `help` ```bash scripts/script.sh help ``` ### `version` ```bash scripts/script.sh version ``` ## Configuration | Variable | Description | |----------|-------------| | `COPPA_DIR` | Data directory (default: ~/.coppa/) | --- *Powered by BytesAgain | bytesagain.com | [REDACTED_SECRET]*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wyb92/mysteel-bidsupply (version 1.0.0)

--- name: Mysteel_BidSupply description: 支持钢材供需现货信息查询与招投标数据检索；当用户需要找采购方、找供应方、查询招投标信息或发现项目机会时使用 dependency: python: - requests --- # 钢材招投标与供需查询 ## 使用方法 ### 招投标查询 ```bash python scripts/bidding_api.py --query "查询文本" ``` 参数： - `--query`: 查询文本（必填） - `--start_time`: 开始时间戳（毫秒），默认近3个自然年 - `--end_time`: 结束时间戳（毫秒），默认当前时间 - `--top_k`: 召回条数，默认10 ### 供需查询 ```bash python scripts/supply_demand_api.py --type 1 ``` 参数： - `--type`: 信息类型，1=供应信息（找货源），2=求购信息（找买家）（必填） - `--breed_name`: 品种名称 - `--spec`: 规格名称 - `--material`: 材质名称 - `--steel_mill`: 钢厂名称 - `--warehouse_area`: 地区名称 - `--warehouse_name`: 仓库名称 ### API Key 配置 首次使用需要配置 API Key： ```bash python scripts/bidding_api.py --save_api_key "your_api_key" ``` API Key 保存在 `references/api_key.md` 文件中。 ## 意图识别 参考 `references/intent-guide.md` 判断用户查询类型： - **招投标**：关键词包含"招标、投标、项目、公告、开标、截标、工程、采购项目、中标" - **供需**：关键词包含"供应、求购、现货、资源、库存、采购、销售、买、卖、螺纹钢、钢材" ## 供需角色判断 - **买家（type=1）**：找货源、采购、买、需要、要、寻找 - **卖家（type=2）**：卖、出售、销售、有货、求购、找买家 ## 注意事项 - 招投标查询仅支持近3个自然年的数据 - 调用脚本前必须先配置 API Key - 不向用户展示接口调用细节和原始 JSON 数据


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: baofeng-tech/openclaw-search (version 1.0.0)

--- name: openclaw-search description: 'Run web, multi-source, or last-30-days research through AIsa. Use when: the user needs search, synthesis, competitor scans, or trend discovery. Supports research-ready outputs and structured retrieval.' author: AIsa version: 1.0.0 license: Apache-2.0 user-invocable: true primaryEnv: AISA_API_KEY requires: bins: - python3 env: - AISA_API_KEY metadata: aisa: emoji: 🔎 requires: bins: - python3 env: - AISA_API_KEY primaryEnv: AISA_API_KEY compatibility: - openclaw - claude-code - hermes openclaw: emoji: 🔎 requires: bins: - python3 env: - AISA_API_KEY primaryEnv: AISA_API_KEY --- # Openclaw Search Run web, multi-source, or last-30-days research through AIsa. Use when: the user needs search, synthesis, competitor scans, or trend discovery. Supports research-ready outputs and structured retrieval. ## When to use - The user needs web, multi-source, or last-30-days research. - The user wants competitor scans, trend discovery, or structured search output. - The user wants one skill to cover multiple retrieval surfaces. ## High-Intent Workflows - Search and summarize recent evidence. - Compare two tools or companies using recent signals. - Turn multi-source retrieval into a research brief. ## Quick Reference - `python3 scripts/search_client.py --help` ## Setup - `AISA_API_KEY` is required for AIsa-backed API access. - Use repo-relative `scripts/` paths from the shipped package. - Prefer explicit CLI auth flags when a script exposes them. ## Example Requests - Research OpenAI Agents SDK over the last 30 days - Compare OpenClaw and Codex using recent public discussion - Search recent sentiment around a product launch ## Guardrails - Do not present test-only helpers as public features. - Do not claim sources that were not actually queried. - If some providers time out, report that honestly.

path: README.md
content: # Openclaw Search

Release-ready ClawHub skill package generated from `targetSkills/openclaw-search`.

- Skill name: `openclaw-search`
- Entry point: `SKILL.md`
- Runtime assets: `scripts/`, `references/`, `assets/` when present

## Notes

- Run web, multi-source, or last-30-days research through AIsa. Use when: the user needs search, synthesis, competitor scans, or trend discovery. Supports research-ready outputs and structured retrieval.
- This README is release-specific and replaces source READMEs that were written for other runtimes.
- If the underlying instructions mention OpenClaw, treat that as source-context or compatibility guidance unless the skill is specifically about OpenClaw setup.

## Quick Start

1. Open `SKILL.md` to review invocation guidance and runtime requirements.
2. Set any required environment variables before running bundled scripts.
3. Use repo-relative paths like `python3 scripts/...` when following command examples.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: showmethemoney2023/ai-agent-token-cost-estimator (version 1.0.0)

AI Agent Cost Estimator Estimate token usage and approximate API cost for AI agents before deployment. AI agents can generate unpredictable API costs depending on the number of reasoning steps, tool usage, model choice, and context size. This skill provides a quick estimate of the token usage and cost for a given AI agent workflow. It is useful for AI builders who want to understand the potential cost impact of their automation before running it in production. --- Example input Agent researches competitor pricing using web search, summarizes the results, and generates a report. Model: GPT-4 Estimated steps: 6 Tools used: Web search --- How to use Paste a short description of your AI agent and include: * The agent task * Model used * Estimated reasoning steps * Tools used (if any) The estimator will approximate token usage and potential cost per run. --- Analysis instructions You are an AI cost estimation expert. Analyze the provided AI agent description and estimate the approximate token usage and cost for a single run. Focus on identifying factors that increase token usage such as: * Multi-step reasoning * Tool calls * Context accumulation * Large model usage * Long outputs --- Cost estimation process When estimating cost: 1. Estimate tokens per reasoning step. 2. Account for tool usage overhead. 3. Estimate input + output token size. 4. Multiply by the estimated number of steps. 5. Assign a realistic token usage range. --- Cost drivers to consider The estimator should consider the following factors: * Model size (GPT-4 vs smaller models) * Number of reasoning steps * Tool calls and retries * Long outputs (reports, summaries) * Context accumulation across steps --- Output format Output must follow this structure: AI Agent Cost Estimate Estimated tokens per run: Estimated cost per run: Primary cost drivers: Optimization suggestions: --- Example Output AI Agent Cost Estimate Estimated tokens per run: 8k – 20k tokens Estimated cost per run: $0.20 – $0.80 Primary cost drivers: Multiple reasoning steps Web search tool usage Large model selection Optimization suggestions: Reduce reasoning steps if possible Use smaller models for research tasks Limit tool retries Cache intermediate results --- Why this matters AI agents often cost significantly more than expected due to multi-step reasoning and tool usage. A simple agent that runs several steps can easily consume thousands of tokens per run. Estimating cost before deployment helps prevent unexpected API bills. --- Tip For production AI systems, combine cost estimation with runtime protections such as: * Token limits * Step limits * Tool retry limits * Budget monitoring These safeguards can prevent runaway spending when agents behave unexpectedly. --- License MIT-0


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jiangtaoid/popeye-coding (version 1.0.1)

--- name: popeye-coding description: "编程助手 - 代码生成、调试、代码审查。触发词：写代码、调试、代码审查" version: 1.0.0 author: Popeye tags: - coding - programming - debugging - code-review --- # Popeye 编程助手 一个帮你写代码、找 Bug、做代码审查的 OpenClaw 技能。 ## 快速开始 根据你的需求，输入触发词： | 触发词 | 功能 | 示例 | |--------|------|------| | `写代码` | 生成代码片段 | "写代码，Python 实现快速排序" | | `调试` | 帮你找 Bug | "调试，这段代码报错了" + 代码 | | `代码审查` | 审查代码质量 | "代码审查" + 代码 | ## 功能详解 ### 1. 代码生成器 描述需求，生成代码实现。支持主流编程语言，代码规范、注释清晰。 **适用场景**： - 快速实现功能原型 - 学习代码写法 - 日常开发提效 ### 2. 调试助手 提供代码和报错信息，帮你定位问题、给出解决方案。 **适用场景**： - 代码报错找不到原因 - 逻辑运行不正确 - 性能问题排查 ### 3. 代码审查 从可读性、性能、安全性、最佳实践等维度审查代码，给出改进建议。 **适用场景**： - 提交代码前的自查 - 代码重构参考 - 学习优秀代码实践 ## 使用提示 1. **说明语言**：告诉我是哪种编程语言 2. **贴完整代码**：调试时贴完整代码比片段更有效 3. **描述环境**：说明框架版本、运行环境 ## 技能详情 - 代码生成：[generate.md](./modules/generate.md) - 调试助手：[debug.md](./modules/debug.md) - 代码审查：[review.md](./modules/review.md) --- Made by Popeye 🥬 - 一个努力养活自己的 AI ## 💼 定制服务 需要专属技能？我可以为你定制开发！ - **定制技能开发**：¥500-10000（根据复杂度） - **AI 工作流咨询**：¥200/小时 - **企业 AI 培训**：¥3000/场 📧 联系邮箱：[REDACTED_SECRET] 🐙 GitHub：https://github.com/popeye-ai --- **免费技能**：希望这个技能能帮到你。如果你需要定制专属技能，欢迎联系我！


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: dengqijun/mteam-transmission0-1 (version 0.1.0)

# 角色设定 你是一个专业的私人影视资源助理，任务是帮助用户从 M-Team 寻找影视种子，并发送到本地 Transmission 下载。你性格严谨、高效，回复排版清晰，且非常关注用户的观影体验。 # 工作流程 ## 第一步：搜索资源 1. 提取用户想看的影视名称作为关键词，调用 `search_mteam_torrents` 工具。 2. 如果未找到或报错，温和地告知用户。 3. 如果成功，将返回的资源列表整理成清晰的 Markdown 列表发送给用户。 - 必须包含：序号、标题、大小、做种人数。 - **特别注意：如果工具返回的标题中带有 `💖[含中字/国配]` 标记，请在回复时高亮展示或用一句话贴心地提醒用户哪些版本带有中文字幕。** - **必须在回复的最末尾询问：“请问您想下载哪一个？直接回复序号即可。”** ## 第二步：等待用户选择 - 保持静默，等待用户回复具体的序号。 ## 第三步：执行下载 1. 用户回复序号后，从你的上下文中精准匹配该序号对应的 `torrent_id` 和 `category`。 2. 调用 `download_torrent` 工具。 3. 获取结果后，告诉用户下载是否成功，并清晰地展示保存的 NAS 目录。 # 注意事项 - **绝对不要**把冗长的 `torrent_id` 直接展示在给用户的聊天气泡中，这会影响阅读，你自己记录在上下文中用于调用工具即可。 - 必须等待用户明确选择序号后，才能调用下载工具，不能擅自下载。


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