OpenClaw/clawhub-security-signals huggingface dataset:
Skill: 52yuanchangxing/decision-ledger (version 1.0.0)

--- name: decision-ledger version: 1.0.0 description: "从纪要、聊天或项目材料中提取决策、负责人、截止时间、前提假设与撤销条件。；use for decision, meeting, governance workflows；do not use for 编造不存在的决策, 替代法律审计." author: OpenClaw Skill Bundle homepage: https://example.invalid/skills/decision-ledger tags: [decision, meeting, governance, tracking] user-invocable: true metadata: {"openclaw":{"emoji":"📘","requires":{"bins":["python3"]},"os":["darwin","linux","win32"]}} --- # 决策台账整理器 ## 你是什么 你是“决策台账整理器”这个独立 Skill，负责：从纪要、聊天或项目材料中提取决策、负责人、截止时间、前提假设与撤销条件。 ## Routing ### 适合使用的情况 - 把本周项目会议记录整理成决策台账 - 从纪要中抽取负责人和截止日期 - 输入通常包含：会议纪要、聊天记录、文档摘录 - 优先产出：已确认决策、待确认事项、后续依赖 ### 不适合使用的情况 - 不要用来编造不存在的决策 - 不要用来替代法律审计 - 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： - 已确认决策 - 待确认事项 - 负责人和截止日 - 前提假设 - 推翻条件 - 后续依赖 ## 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 - 只整理显式信息，隐含判断会被标注为推断。 - 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: drones277/tg-voice-whisper (version 1.0.0)

--- name: tg-voice-whisper description: Auto-transcribe Telegram voice messages (.ogg Opus) to text using local OpenAI Whisper (tiny model). Reply with transcription + auto-delete file for privacy. No API keys, fully offline after install. emoji: 🎙️🔊 requires: bins: [whisper, ffmpeg] install: - kind: apt packages: [ffmpeg] - kind: pip packages: [openai-whisper] flags: [--break-system-packages] min-server: 4GB RAM recommended for fast/stable work (cached model <1s transcribe on 1vCPU). **Safe:** Local-only, auto-rm files, no external APIs/credentials stored. **Usage (agent):** When voice .ogg arrives in /root/.openclaw/media/inbound/: ``` exec 'whisper PATH --model tiny --language ru --output_format txt --output_dir /tmp/whisper; text=$(cat /tmp/whisper/*.txt); message action=send message="🔊 [text]" reply_to_current; rm PATH /tmp/whisper/*' ``` **Auto-setup (non-block):** Spawn sub-agent or cron every 5s: ``` sessions_spawn task="LOOP: find /root/.openclaw/media/inbound/*.ogg -mmin -1 → background whisper ru tiny → message reply text → rm" label="voice-auto" cleanup="keep" ``` Or cron: ``` cron add job={"name":"voice-auto","schedule":{"kind":"every","everyMs":5000},"payload":{"kind":"systemEvent","text":"🔊 VOICE_CHECK"},"sessionTarget":"main"} ``` **Test:** whisper /path.ogg --model tiny --language ru **Notes:** - First run: ~15s model download (72MB ~/.cache/whisper/tiny.bin). - Cached: <1s on 1vCPU/4GB. - Languages: ru/en best; --language detect auto. - Accuracy: tiny 85-95% ru speech; upgrade to base/small for better.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: leooooooow/profit-margin-calculator (version 1.0.1)

--- name: profit-margin-calculator description: Calculate true ecommerce profit margin after product cost, shipping, platform fees, discounts, and refund drag. Use when teams need a fast profitability reality check. --- # Profit Margin Calculator 不要只看毛利，要看最后真正剩下多少。 ## 先交互，再计算 开始前先确认： 1. 你要看的是 gross margin、contribution margin，还是 net margin？ 2. 你们平时利润率口径里，是否包含： - 运费 / 包装 - 平台费 / 支付费 - 折扣 / 补贴 - 退款 / 售后损耗 - 固定成本分摊 3. 你想沿用你们现有算法，还是让我给推荐的电商利润拆解方式？ 如果用户没有统一口径，先给推荐框架，再让用户确认。 ## Python script guidance 当用户给出结构化数字后： - 生成 Python 脚本完成利润拆解 - 先列公式与假设 - 再返回毛利 / 净利结果 - 同时返回可复用脚本 如果输入不完整，先追问，不要直接输出看似精确的结果。 ## 解决的问题 很多团队说“这个产品利润不错”，其实只算了售价减进货价，没把这些算进去： - 运费和包装； - 平台手续费； - 折扣、券、活动补贴； - 退款和售后损耗； - 必要时还要分摊基础运营成本。 这个 skill 的目标是： **把利润率从一个粗数字，变成一张真正能用于经营判断的 margin 拆解。** ## 何时使用 - 对比多个 SKU 利润健康度； - 定价前确认是否还有空间； - 发现营收增长但利润不涨时做排查。 ## 输入要求 - 售价 - 商品成本 - 运费 / 包装 / 仓储等履约成本 - 平台费 / 支付费 / 渠道费 - 折扣或优惠 - 退款率 / 售后损耗 - 可选：固定成本分摊 ## 工作流 1. 明确用户的 margin 口径。 2. 计算毛利与毛利率。 3. 扣除履约、渠道、折扣、退款影响。 4. 输出净利润与净利率。 5. 标记利润被侵蚀最严重的环节。 6. 返回可复用 Python 脚本。 ## 输出格式 1. 成本拆解表 2. 毛利 / 净利结果 3. Margin 风险点 4. 优化建议 5. Python 脚本 ## 质量标准 - 不混淆 gross margin 和 net margin。 - 清楚指出利润被谁吃掉。 - 结果可用于 SKU 比较或经营复盘。 - 明确哪些数字是估算值。 - 未确认口径前不要假装精确。 ## 资源 参考 `references/output-template.md`。

path: references/output-template.md
content: # Profit Margin Calculator Output Template

## 1) Assumptions
| Cost item | Value | Notes |
|---|---|---|

## 2) Margin calculation
- Revenue per order:
- Gross profit:
- Gross margin:
- Net profit:
- Net margin:

## 3) Biggest margin drags
- 

## 4) Improvement ideas
- Pricing:
- Cost reduction:
- Offer / discount adjustment:
- Channel / fee optimization:


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kurosh87/aerobase-calendar (version 1.0.0)

--- name: aerobase-calendar description: Google Calendar sync for flight events --- # Calendar Sync Keep the user's travel world in sync. The killer feature: when a new flight is imported, check the calendar for events within 48 hours of arrival. Calculate body clock time at each event. "You have a 9 AM board meeting and your body clock will be at 3 AM — push it, or start pre-adaptation 3 days early." ## API Endpoints - POST /api/calendar/connect — initiate OAuth (`{ provider: "google" }`) - GET /api/calendar/connections — list active connections - POST /api/calendar/disconnect — remove connection (`{ provider }`) - POST /api/calendar/create-events — create jetlag recovery events - POST /api/calendar/sync-flight — sync a flight's events to calendar - POST /api/trips/{tripId}/calendar — sync all trip flights to calendar - POST /api/calendar/preferences — sync preferences (color, event types, reminders) - GET /api/calendar/export-ics — ICS export fallback ## Concierge Calendar Integration **Endpoint:** `GET /api/concierge/instances/{instanceId}/calendar/events` Returns calendar events for the next 7 days: ```json { "connected": true, "events": [ { "id": "...", "title": "Team Standup", "start": "2026-02-25T10:00:00-08:00", "end": "2026-02-25T10:30:00-08:00", "location": "Zoom" } ], "count": 5 } ``` The agent uses this to: 1. Get upcoming events within 48h of arrival 2. Calculate body clock time at each event 3. Flag conflicts: "Your 9 AM meeting will be at 3 AM body clock" ## Calendar Event Types - Flight events (departure/arrival times, gate, terminal) - Layover activities (tentative) - Recovery plan milestones (light therapy, sleep windows) - Hotel check-in/checkout - Reminders: 24h before (check-in), 3h before (leave for airport) ## Body Clock Conflict Detection When a new flight is imported: 1. Get events within 48h of arrival from user's calendar 2. Calculate body clock time at each event using origin timezone + flight duration 3. Flag conflicts: "Your 9 AM meeting will happen at 3 AM body clock" 4. Suggest pre-adaptation or rescheduling ## Auto-Sync - Flight added → calendar event created - Flight cancelled → calendar event updated/removed - Flight time changed → calendar event updated, conflicts rechecked ## Access Pattern The agent accesses Google Calendar through Aerobase API endpoints (backend mediates OAuth tokens). The agent does not call Google APIs directly. ## Rate Limits - Calendar sync cron: daily at 08:00 UTC (do not increase). Max 20 events per sync. - Read upcoming events: max 10/hr. If Google returns 429: back off 15 min.


Security scan verdicts — ClawScan: clean | static analysis:  | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 52yuanchangxing/compliance-evidence-assembler (version 1.0.0)

--- name: compliance-evidence-assembler version: 1.0.0 description: "把审计所需证据整理成目录、清单和缺失项，便于后续评审。；use for compliance, evidence, audit workflows；do not use for 伪造证据, 替代正式审计结论." author: OpenClaw Skill Bundle homepage: https://example.invalid/skills/compliance-evidence-assembler tags: [compliance, evidence, audit, governance] user-invocable: true metadata: {"openclaw":{"emoji":"📦","requires":{"bins":["python3"]},"os":["darwin","linux","win32"]}} --- # 合规证据组装器 ## 你是什么 你是“合规证据组装器”这个独立 Skill，负责：把审计所需证据整理成目录、清单和缺失项，便于后续评审。 ## Routing ### 适合使用的情况 - 整理这次审计需要的证据包 - 指出还缺什么 - 输入通常包含：证据目录、控制项列表或说明 - 优先产出：证据概览、控制映射、交付建议 ### 不适合使用的情况 - 不要伪造证据 - 不要替代正式审计结论 - 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： - 证据概览 - 控制映射 - 缺失证据 - 命名建议 - 补齐优先级 - 交付建议 ## 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 - 只做证据编排和缺口提示。 - 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: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: ipedrax/antigravity-image-gen (version 2.0.0)

--- name: antigravity-image-gen description: Generate images using the internal Google Antigravity API (Gemini 3 Pro Image). High quality, native generation without browser automation. read_when: - User asks to generate an image - User wants to create visual content metadata: {"clawdbot":{"emoji":"🎨","requires":{"bins":["node"],"config":["auth.profiles"]},"description":"Generates images via internal Google API using local OAuth credentials."}} --- # Antigravity Image Generation Generate high-quality images using the internal Google Antigravity API (Gemini 3 Pro Image). This skill bypasses the need for browser automation by using the `daily-cloudcode-pa.sandbox` endpoint directly with your OAuth credentials. ## Prerequisites - **Google Antigravity OAuth Profile**: Must be present in your OpenClaw auth-profiles.json. - **Node.js**: Available in the environment. - **Security Note**: This skill reads local OAuth tokens from your profile to authenticate with Google's API. This is expected behavior for internal tool use. ## Usage ### Direct Script Execution ```bash /home/ubuntu/clawd/skills/antigravity-image-gen/scripts/generate.js \ --prompt "A futuristic city on Mars" \ --output "/tmp/mars.png" \ --aspect-ratio "16:9" ``` ### Arguments - `--prompt` (Required): The description of the image. - `--output` (Optional): Path to save the image (default: `/tmp/antigravity_<ts>.png`). - `--aspect-ratio` (Optional): `1:1` (default), `16:9`, `9:16`, `4:3`, `3:4`. ## Output - The script writes the image to the specified path. - It prints `MEDIA: <path>` to stdout, which allows Clawdbot to automatically detect and display the image. ## Troubleshooting - **429 Resource Exhausted**: Quota limit reached. Wait or check your project limits. - **No image data found**: The model might have refused the prompt (safety) or the API structure changed. Check the "Model message" output. - **Auth Error**: Ensure you have logged in via `google-antigravity` provider.

path: .clawhub/origin.json
content: version: 1
registry: https://clawhub.ai
slug: antigravity-image-gen
installedVersion: 1.0.0
installedAt: 1771354397276

Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (HIGH)
Skill: mike47512/renting (version 0.1.0)

--- name: "Renting" description: "提供各大城市公寓租赁、中介房源比价及个人真房源指引。" --- # 租房 ## 筛选项 - 类别/领域/功能（核心/进阶） - 场景/适用时段/设备要求（移动端/网页版/跨端） - 状态（实时/离线/授权/VIP特权/免费） ## 返回字段 - 操作摘要/交互流程/关键入口 - 功能亮点/提效工具/隐藏玩法/避坑指南 - 状态追踪/数据同步/消息提醒/安全策略 ## 示例请求 - “如何快速使用 租房 处理 [具体场景任务] ？” - “租房 的 [核心功能] 有哪些最新玩法/优惠信息？” ## 更新频率 - 结合产品迭代与社区反馈进行内容更新 ## 注意事项 - 保护个人隐私，谨慎进行敏感授权 - 部分功能涉及第三方接口，稳定性受合作方波动影响


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: brasco05/keyword-research1 (version 1.0.0)

--- name: keyword-research description: "Multi-source keyword intelligence and autocomplete research. Fetches real-time suggestions from Google, YouTube, Amazon, and DuckDuckGo — no API key required. Use when: (1) doing SEO or content keyword research, (2) finding what users search for on a topic, (3) competitor or niche research, (4) expanding a seed keyword into hundreds of related terms, (5) building keyword lists for ads or content. Triggers on: keyword research, what do people search for, autocomplete, keyword ideas, SEO keywords, search suggestions, keyword list." --- # Keyword Intelligence Multi-source autocomplete fetcher — no API keys needed. Pulls real-time suggestions from Google, YouTube, Amazon, and DuckDuckGo. ## Quick Usage ```bash # All sources, default language (de) python3 scripts/fetch_suggestions.py "keyword" # Specific sources python3 scripts/fetch_suggestions.py "keyword" --sources google,youtube # English / US region python3 scripts/fetch_suggestions.py "keyword" --lang en --region us # Expand mode: fetch suggestions of suggestions (10x more keywords) python3 scripts/fetch_suggestions.py "keyword" --sources google --expand # JSON output (for piping or further processing) python3 scripts/fetch_suggestions.py "keyword" --json ``` ## Options | Flag | Default | Description | |------|---------|-------------| | `--sources` | `all` | `all` or comma-separated: `google`, `youtube`, `amazon`, `ddg` | | `--lang` | `de` | Language code: `de`, `en`, `tr`, `fr`, `es`, ... | | `--region` | `de` | Region: `de`, `us`, `tr`, `gb`, ... | | `--expand` | off | Fetches 2nd-level suggestions from Google (base keywords → ~10x results) | | `--json` | off | Outputs JSON instead of formatted text | ## Sources - **Google** — Broadest coverage, best for general web search intent - **YouTube** — Video content ideas, tutorials, how-to queries - **Amazon** — Product/buying intent keywords (works best for product niches) - **DuckDuckGo** — Privacy-focused users, tech/dev audience ## Workflow 1. Start with a seed keyword and `--sources all` 2. Identify which source is most relevant for the use case 3. Use `--expand` on the most promising source for deeper research 4. Export with `--json` to process or display the results ## Notes - No rate limits enforced, but add delays for large batch jobs (the script adds 0.2s between expand calls) - Amazon suggestions may be empty for non-product keywords — expected behavior - YouTube returns fewer results for niche/regional keywords - `--expand` only works on Google (most reliable for 2nd-level fetching)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: niracler/nini-writing-inspiration (version 0.1.2)

--- name: writing-inspiration description: Use when user needs help starting or continuing writing (not diary). Triggers include「不知道写什么」「帮我构思」「写游记」「记录 TIL」「写点什么」. For diary writing, use diary-assistant instead. metadata: {"openclaw":{"emoji":"💡"}} --- # Writing Inspiration 帮助用户在不知道写什么或如何写下去时，通过启发式提问引导写作。 **注意**：日记写作请使用 `diary-assistant` skill，本 skill 专注于游记、TIL、通用文章。 ## 核心原则 | 原则 | 说明 | |------|------| | **不要一股脑问完** | 设计启发问题 TodoList，逐个提问 | | **确认再继续** | 用户想好 → 写好 → 改好 → 确认后，才到下一个问题 | | **启发而非代写** | 用提问启发思考，不替用户决定内容方向 | ## 文章类型识别 | 用户意图 | 类型 | 框架 | |----------|------|------| | 「写游记」「记录旅行」 | 游记 | 出发 → 旅途 → 反思 | | 「记录 TIL」「今天学到」 | TIL | 背景 → 过程 → 方案 → 收获 | | 「写点什么」「帮我构思」 | 通用 | 起因 → 观点 → 展开 → 收尾 | ## 流程 ```text 识别文章类型 → 列出启发问题 → 逐个提问 → 用户回答 → 确认 → 下一个 → 整理成文 ``` ### 提问节奏 ```text Claude: 「为什么想去这个地方？」 用户: [写下回答] Claude: [确认/追问] → 「好的，下一个问题：到达时第一眼看到什么？」 ``` ## 游记框架 ### 结构 ```text 1. 出发前 - 为什么去这个地方？ - 有什么期待？ 2. 旅途中 - 按时间线或地点组织 - 感官体验（看到、听到、闻到、尝到） - 文化冲击 / 意外发现 - 心理变化 3. 回来后 - 最深刻的印象是什么？ - 有什么改变了你的认知？ - 会推荐别人去吗？为什么？ ``` ### 启发问题 **感官** - 到达时第一眼看到什么？ - 有什么气味让你印象深刻？ - 吃到了什么特别的东西？ **体验** - 有什么让你惊讶的地方？ - 有什么和你预期不同的？ - 有什么尴尬或有趣的经历？ **反思** - 这次旅行让你对什么有了新认识？ - 如果重来一次，你会怎么安排？ - 有什么遗憾或惊喜？ ## TIL 框架 ### 结构 ```text 1. 背景 - 遇到了什么问题？ - 为什么需要解决？ 2. 过程 - 尝试了什么方法？ - 踩了什么坑？ 3. 解决方案 - 最终怎么解决的？ - 关键代码/配置是什么？ 4. 收获 - 学到了什么？ - 下次遇到类似问题怎么办？ ``` ### 启发问题 - 这个问题是怎么发现的？ - 你最开始以为是什么原因？ - 搜索了什么关键词？ - 哪个回答/文档帮到了你？ - 有没有什么坑需要提醒未来的自己？ ## 通用文章框架 ### 结构 ```text 1. 起因 - 为什么想写这个话题？ - 有什么触发点？ 2. 观点 - 你的核心观点是什么？ - 有什么支撑这个观点的经历/数据？ 3. 展开 - 有哪些角度可以讨论？ - 有没有反面观点？ 4. 收尾 - 你希望读者带走什么？ - 有什么行动建议？ ``` ### 启发问题 **破题** - 如果用一句话概括这篇文章，你会怎么说？ - 你最想让读者知道的一点是什么？ **展开** - 有没有具体的例子可以说明？ - 有没有人会反对你的观点？他们会怎么说？ - 你是怎么得出这个结论的？ **收尾** - 写完这篇文章，你自己有什么新认识？ - 有没有什么还没想清楚的地方？ ## 详细参考 完整框架和更多启发问题见 [frameworks.md](references/frameworks.md)。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: codenova58/arxiv-papers (version 1.0.0)

--- name: arxiv-papers description: "Find and summarize arXiv.org preprints—keyword/category search, abstracts, PDF links. Use for literature scans, paper IDs, or quick orientation (not peer-review, not medical/legal advice)." --- # ArXiv Papers Use the **arXiv API** (and optional PDF fetch) to **locate** papers and **summarize abstracts** for the user. Treat results as **preprints**—not necessarily peer-reviewed or final. ## When to use - “What’s new on arXiv about …”, “Summarize arXiv:XXXX”, category browsing (e.g. `cs.AI`). - Quick **orientation** before deeper reading—not a substitute for reading the full paper in serious research. ## Limits (say explicitly when relevant) - **Coverage**: arXiv only; many venues are not there. - **Quality**: preprint ≠ endorsed truth; contradictory claims exist. - **Rate / ToS**: respect arXiv’s API guidelines; don’t hammer endpoints. ## Workflow 1. Run `scripts/search_arxiv.sh "<query>"` and parse the returned XML (`<entry>`, `<title>`, `<summary>`, PDF `<link>`). 2. Present **title, authors, id, abstract summary**, and link to abstract/PDF. 3. If the user wants depth, **PDF** may be fetched selectively—large files and parsing limits apply. 4. Optionally append notable papers to `memory/RESEARCH_LOG.md` (if your environment uses it): ```markdown ### [YYYY-MM-DD] TITLE - **Authors**: … - **Link**: … - **Summary**: … ``` ## Examples - Latest LLM reasoning papers on arXiv. - “What is paper `2512.08769` about?” ## Resources - `scripts/search_arxiv.sh` — thin wrapper over the arXiv API.

path: scripts/search_arxiv.sh
content: #!/usr/bin/env bash
# scripts/search_arxiv.sh
QUERY=$1
COUNT=${2:-5}
# Use curl to query ArXiv API
curl -sL "https://export.arxiv.org/api/query?search_query=all:$QUERY&start=0&max_results=$COUNT&sortBy=submittedDate&sortOrder=descending"


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: zhangifonly/trae-ide (version 1.0.1)

--- name: "Trae IDE" version: "1.0.0" description: "Trae AI IDE 助手，精通字节跳动 AI 编程工具、Builder 模式、多模型切换、中文优化" tags: ["ai", "ide", "trae", "bytedance", "coding"] author: "ClawSkills Team" category: "ai" --- # Trae IDE 助手 你是一个精通 Trae AI IDE 的编程助手。Trae 是字节跳动推出的免费 AI 编程工具。 ## 身份与能力 - 精通 Trae 的 AI 编程功能和工作流 - 熟悉 Builder 模式、Chat 模式、内联编辑等核心特性 - 掌握多模型切换和中文编程场景优化 - 了解 Trae 与 Cursor、Windsurf、VS Code 的差异 ## 核心功能 ### Builder 模式 类似 Cursor 的 Agent 模式，自动完成多步骤开发任务： - 分析需求 → 创建文件 → 编写代码 → 运行调试 - 自动执行终端命令 - 支持多文件同时修改 - 实时展示修改 diff ### Chat 模式 与 AI 对话获取编程帮助： - 代码解释和问答 - Bug 分析和修复建议 - 架构设计讨论 - 支持 @文件 引用上下文 ### 内联编辑（Inline Edit） 选中代码 → Ctrl+I → 描述修改需求： - 局部代码重构 - 添加注释和文档 - 类型标注补全 - 代码风格调整 ### 多模型支持 | 模型 | 说明 | |------|------| | Claude Sonnet 4 | 默认模型，综合能力强 | | GPT-4o | OpenAI 模型 | | Doubao | 字节豆包模型，中文优化 | ## 特色优势 ### 完全免费 - 所有 AI 功能免费使用，无需订阅 - 包括 Claude Sonnet 4 等高端模型 - 无每日使用次数限制 ### 中文优化 - 界面支持中文 - 中文提示词理解能力强 - 中文代码注释和文档生成质量高 ### VS Code 兼容 - 基于 VS Code 内核，插件生态兼容 - 熟悉的快捷键和操作方式 - 支持导入 VS Code 配置和插件 ## 与竞品对比 | 维度 | Trae | Cursor | Windsurf | VS Code+Copilot | |------|------|--------|----------|-----------------| | 价格 | 免费 | $20/月 | $15/月 | $10/月 | | Builder 模式 | 有 | 有 | 有 | 无 | | 中文支持 | 优秀 | 一般 | 一般 | 一般 | | 模型选择 | 多模型 | 多模型 | 单模型 | Copilot | | 插件生态 | VS Code | VS Code | VS Code | VS Code | ## 最佳实践 - Builder 模式适合从零开始的新项目 - Chat 模式适合理解和修改现有代码 - 内联编辑适合局部微调 - 中文描述需求时尽量具体，避免歧义 - 复杂项目建议先用 Chat 讨论架构，再用 Builder 执行 --- **最后更新**: 2026-03-22


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: tyzzt/wsl-powershell (version 1.0.1)

# WSL-PowerShell Controller Call Windows PowerShell from WSL to control Windows host from Linux environment. ## How It Works WSL mounts Windows drives to `/mnt/`, allowing direct execution of Windows binaries: - PowerShell: `/mnt/c/Windows/System32/WindowsPowerShell/v1.0/powershell.exe` - CMD: `/mnt/c/Windows/System32/cmd.exe` ## Usage ### Execute PowerShell Commands ```bash /mnt/c/Windows/System32/WindowsPowerShell/v1.0/powershell.exe -Command "Your-Command" ``` ### Execute PowerShell Scripts ```bash /mnt/c/Windows/System32/WindowsPowerShell/v1.0/powershell.exe -File "/mnt/c/path/to/script.ps1" ``` ## Common Examples ### System Information ```bash # Get system info powershell.exe -Command "Get-ComputerInfo" # Get process list powershell.exe -Command "Get-Process | Select-Object -First 10 Name,Id,CPU" # Get service status powershell.exe -Command "Get-Service | Where-Object {$_.Status -eq 'Running'} | Select-Object -First 10 Name,DisplayName" ``` ### File Operations ```bash # List directory powershell.exe -Command "Get-ChildItem C:\\Users" # Copy file powershell.exe -Command "Copy-Item C:\\source\\file.txt C:\\dest\\file.txt" # Create file powershell.exe -Command "New-Item -Path C:\\test.txt -ItemType File -Force" ``` ### Process Management ```bash # Start program powershell.exe -Command "Start-Process notepad.exe" # Stop process powershell.exe -Command "Stop-Process -Name notepad -Force" ``` ### Network Operations ```bash # Get network config powershell.exe -Command "Get-NetIPConfiguration" # Ping test powershell.exe -Command "Test-Connection -ComputerName google.com -Count 2" ``` ## Path Conversion WSL Path ↔ Windows Path: - WSL: `/mnt/c/Users/Tao` ↔ Windows: `C:\Users\Tao` - Use `wslpath` command: ```bash wslpath -w /mnt/c/Users # Output: C:\Users wslpath -u C:\\Users # Output: /mnt/c/Users ``` ## Notes 1. **Permissions**: Some operations require administrator privileges, use `-Verb RunAs` for elevated PowerShell 2. **Path Escaping**: Backslashes `\` in Windows paths must be escaped as `\\` 3. **Encoding**: PowerShell outputs UTF-16 by default, may need conversion 4. **Execution Policy**: Running scripts may require `Set-ExecutionPolicy` ## Security Tips - Use caution with system-level commands - Avoid deleting critical system files - Test commands before executing in production


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: geniusfox/fnnas-download (version 1.0.0)

--- name: fnnas-download description: Manage qBittorrent download tasks on 飞牛NAS - list torrents and add magnet links tools: Bash --- # NAS Download Manager Manage download tasks on the user's 飞牛NAS using qBittorrent API. ## Usage Execute the shell script at `~/.claude/skills/fnnas-download/nas-download.sh` with appropriate commands. ## Commands ### list List download tasks. By default shows only incomplete downloads (progress < 100%). ```bash ~/.claude/skills/fnnas-download/nas-download.sh list ``` Add `-all` flag to show all tasks: ```bash ~/.claude/skills/fnnas-download/nas-download.sh list -all ``` ### add Add a new download task. For magnet links: ```bash ~/.claude/skills/fnnas-download/nas-download.sh add 'magnet:?xt=...' ``` For torrent files: ```bash ~/.claude/skills/fnnas-download/nas-download.sh add /path/to/file.torrent ``` ## Connection Details - NAS Host: user@192.168.1.100 - Authentication: SSH key-based - qBittorrent: Unix socket at /home/user/qbt.sock ## Configuration Edit the variables at the top of `nas-download.sh`: - `NAS_HOST`: SSH connection string (user@host) - `QBT_SOCK`: Path to qBittorrent unix socket - `QBT_PASSWORD`: qBittorrent WebUI password


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