OpenClaw/clawhub-security-signals huggingface dataset:
Skill: terrycarter1985/hn-podcast-archive (version 1.0.0)

--- name: hn-podcast-archive description: Build and operate an automated archive workflow for the Hacker News podcast/feed: detect new episodes from RSS, download audio, transcribe locally with Whisper, generate markdown archives with metadata, and keep an index/history for repeatable ingestion. Use when setting up or maintaining a podcast transcription/archive pipeline, especially for RSS-based podcasts and local file archives. --- # HN Podcast Archive Set up or maintain a repeatable pipeline that: 1. reads an RSS feed, 2. detects new episodes, 3. downloads audio, 4. transcribes with local Whisper, 5. writes a markdown archive per episode, 6. updates index/state files. ## Workflow 1. Read `references/layout.md` to understand the expected archive layout and outputs. 2. Use `scripts/hn_podcast_archive.py` as the primary implementation. 3. Run `python3 scripts/hn_podcast_archive.py --help` to inspect options. 4. For first-time setup, ensure required binaries and Python modules exist. 5. For automation, schedule the script on a recurring cadence with a stable output directory. ## Required runtime dependencies The script expects: - `ffmpeg` in PATH - `whisper` in PATH - Python 3.10+ - Python package `feedparser` If any dependency is missing, surface a clear setup note instead of pretending the pipeline is ready to execute. ## Recommended command ```bash python3 skills/hn-podcast-archive/scripts/hn_podcast_archive.py \ --feed-url "https://example.com/podcast.rss" \ --output-dir ./data/hn-podcast-archive \ --whisper-model turbo ``` ## Output expectations For each ingested episode, create: - downloaded audio under `audio/` - transcript under `transcripts/` - markdown archive under `episodes/` Keep these shared files current: - `index.md` - `state.json` - `run-log.jsonl` ## Automation guidance For automation, prefer a cron/standing-order style trigger that runs every few hours. The script is idempotent at the episode level by tracking processed GUIDs/URLs in `state.json`. ## Safe operating rules - Never overwrite unrelated archive content. - Skip already-processed episodes unless explicitly forced. - Preserve source metadata (title, published date, audio URL, guid). - If transcription fails after download, keep the audio and record the failure in the log/state. ## Customization points Useful flags: - `--limit N` to ingest only recent items during testing - `--force` to reprocess already-seen items - `--dry-run` to inspect actions without writing outputs - `--whisper-model` to trade speed vs accuracy ## Packaging/publishing Package the skill from its folder. Publish with ClawHub only after local validation passes and authentication is available.

path: scripts/requirements.txt
content: feedparser>=6.0.0


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: bytesagain1/jwtdecode (version 2.0.3)

--- name: "jwtdecode" version: "2.0.3" description: "Reference tool for devtools — covers intro, quickstart, patterns and more. Quick lookup for Jwtdecode concepts, best practices, and implementation patterns." author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [jwtdecode, reference] category: "devtools" --- # Jwtdecode Reference tool for devtools — covers intro, quickstart, patterns and more. Quick lookup for Jwtdecode concepts, best practices, and implementation patterns. No API keys or credentials required. ## Commands | Command | Description | |---------|-------------| | `intro` | intro reference | | `quickstart` | quickstart reference | | `patterns` | patterns reference | | `debugging` | debugging reference | | `performance` | performance reference | | `security` | security reference | | `migration` | migration reference | | `cheatsheet` | cheatsheet reference | ## Output Format All commands output plain-text reference documentation via heredoc. No external API calls, no credentials needed, no network access. --- *Powered by BytesAgain | bytesagain.com | [REDACTED_SECRET]*


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: frankxiong/longbridge-kit (version 1.0.2)

--- name: longbridge version: 1.0.2 description: | 长桥 LongPort OpenAPI CLI 工具，提供股票行情查询、账户持仓、订单管理、市场数据四大功能。 当用户提到"长桥"、"LongPort"、"longbridge"，或要求查看股票报价、实时行情、K线、盘口、 逐笔成交、账户余额、持仓、基金持仓、下单、买入、卖出、撤单、今日订单、历史订单、 资金流向、资金分布、市场温度、期权链时，使用此 skill。即使用户只是随口问"看看苹果股价"、 "我的持仓怎么样"、"帮我下个单"，也应触发此 skill。 metadata: {"clawdbot":{"emoji":"📊","os":["darwin","linux"],"requires":{"bins":["python3","uv"],"env":["LONGBRIDGE_APP_KEY","LONGBRIDGE_APP_SECRET","LONGBRIDGE_ACCESS_TOKEN"]}}} --- # 长桥 OpenAPI CLI 工具 通过 `longbridge` CLI 命令调用长桥 OpenAPI，覆盖行情查询、账户持仓、订单管理、市场数据四大模块。 ## 前置条件 ### 环境变量 需设置以下环境变量（可在 `~/.zshrc` 或 `~/.bashrc` 中配置）： ```bash export LONGBRIDGE_APP_KEY="your_app_key" export LONGBRIDGE_APP_SECRET="your_app_secret" export LONGBRIDGE_ACCESS_TOKEN="your_access_token" # 可选：开启交易权限（默认只读，禁止 buy/sell/cancel） # export LONGBRIDGE_TRADE_ENABLED=false ``` 也可以在当前目录或主目录创建 `.env` 文件（系统环境变量优先级更高，不会被 `.env` 覆盖）。 多账户切换：使用 `--profile <名称>` 加载 `.<名称>.env` 文件，例如： ```bash longbridge --profile paper balance # 加载 .paper.env longbridge --profile live positions # 加载 .live.env ``` ### 安装 先检测是否已安装： ```bash which longbridge ``` - 若输出路径（如 `/Users/xxx/.local/bin/longbridge`），说明已安装，跳过安装步骤。 - 若未找到，从本 skill 目录安装（`SKILL_DIR` 为本文件所在目录）： ```bash uv tool install "$SKILL_DIR" ``` 验证安装： ```bash longbridge --help ``` ## 使用方式 所有命令均支持 `--json` 选项输出 JSON 格式。 ### 行情模块 ```bash # 实时报价（支持多个标的） longbridge quote AAPL.US 700.HK longbridge quote AAPL.US --json # 盘口（买5卖5） longbridge depth 700.HK # 逐笔成交 longbridge trades 700.HK --count 20 # K 线（period 可选：1m 5m 15m 30m 60m day week month quarter year） longbridge candlesticks AAPL.US day --count 30 longbridge candlesticks 700.HK 60m --count 100 # 标的静态信息 longbridge info 700.HK AAPL.US ``` ### 账户模块 ```bash # 账户余额与净资产 longbridge balance # 股票持仓 longbridge positions longbridge positions --json # 基金持仓 longbridge funds ``` ### 订单模块 ```bash # 今日订单（只读，无需交易权限） longbridge orders # 历史订单（只读，无需交易权限） longbridge history-orders --start 2026-01-01 --end 2026-03-14 # 以下命令需设置 LONGBRIDGE_TRADE_ENABLED=true # 限价买入（会有确认提示，防止误操作） longbridge buy AAPL.US --qty 100 --price 180.0 # 限价卖出（会有确认提示） longbridge sell 700.HK --qty 500 --price 320.0 # 撤销订单（会有确认提示） longbridge cancel 701234567890 # 跳过确认提示（AI Agent 程序化调用时推荐加 --yes） longbridge buy AAPL.US --qty 100 --price 180.0 --yes longbridge sell 700.HK --qty 500 --price 320.0 -y ``` ### 市场数据模块 ```bash # 市场温度（US/HK/CN/SG） longbridge temperature US longbridge temperature HK # 资金流向 longbridge capital-flow 700.HK # 资金分布（大单/中单/小单） longbridge capital-dist 700.HK # 期权链到期日列表 longbridge option-chain AAPL.US ``` ## 注意事项 - **下单/撤单命令**：`buy`、`sell`、`cancel` 均有确认提示，确认后才会执行，防止误操作。AI Agent 程序化调用时请加 `--yes` 或 `-y` 跳过确认 - **港股代码格式**：使用 4 位 + `.HK` 后缀，如 `0700.HK`、`9988.HK` - **美股代码格式**：使用股票代码 + `.US` 后缀，如 `AAPL.US`、`NVDA.US` - **JSON 输出**：所有命令加 `--json` 可输出机器可读的 JSON 格式，便于 AI 解析 - **错误处理**：SDK 异常会输出友好提示，不暴露原始堆栈

path: requirements.txt
content: longbridge
click>=8.0
rich>=13.0


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: kenera/subskill-generation-rule (version 0.1.1)

--- name: subskill-generation-rule description: Define and enforce project orgnization rules for generating subskills. put generated recommendation outputs under data/, place new feature scripts under subskills/<feature>/, and optionally add SKILL.md inside each feature folder to keep main skill root clean. --- # Subskill Generate Rule Apply these rules for future updates: 1. Store newly generated recommendation/result artifacts in `data/`. 2. Place newly generated code scripts in `subskills/<feature>/`. 3. Use one folder per feature under `subskills/`. 4. Add `SKILL.md` inside the feature folder when behavior/usage needs instructions. 5. Avoid adding one-off scripts and generated files in the main skill root. Recommended layout: ```text <skill>/ SKILL.md config.json data/ subskills/ <feature-a>/ SKILL.md *.py <feature-b>/ SKILL.md *.py ``` ## When to Use - Creating new subskills - Organizing existing features - Enforcing file placement conventions - Keeping skill root clean


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

# A股早盘提醒 每个A股开盘日 9:45 通过飞书发送当日开盘情况总结。 ## 触发条件 - 时间：每交易日 9:45（周一至周五） - 时区：Asia/Shanghai - 注意：节假日需额外判断 ## 内容 - 上证指数、深证成指、创业板指 - 沪深300指数 - 涨跌分布统计 - 热门板块 Top 5 - 成交额 - 操作建议 ## 实现 使用腾讯股票 API 获取实时数据： - 指数: `https://qt.gtimg.cn/q={code}` ## 发送 通过 OpenClaw 飞书通道发送给用户。 ## 使用方法 ```bash # 仅生成报告 node scripts/morning-summary.mjs # 生成并发送到飞书 node scripts/morning-summary.mjs --send ``` ## Cron 定时任务 设置每天 9:45 执行（OpenClaw 会自动判断是否为交易日）： ```bash openclaw cron add "45 9 * * 1-5" --skill a-stock-morning -- --send ```


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: leooooooow/ugc-video-editor (version 1.0.0)

--- name: ugc-video-editor description: Edit raw UGC-style footage into tighter, more usable creator and ad-ready outputs. Use when a team has talking-head, testimonial, or product demo footage that needs cleaner structure without full manual editing. --- # UGC Video Editor Turn rough UGC-style footage into cleaner, more usable outputs. ## Problem it solves Raw UGC footage often has useful authenticity but weak delivery: awkward starts, rambling sections, repeated lines, messy pacing, and rough endings. This skill helps shape UGC into a more usable creator, ad, or product-support asset without losing the casual feel that makes it work. ## Use when - A team has rough UGC footage that needs cleanup - Talking-head or testimonial clips need tighter pacing - Product demo clips need a cleaner usable version - The goal is a more publishable UGC asset without overproducing it ## Do not use when - The user needs a full cinematic or design-heavy edit - The footage needs motion graphics or complex multi-scene storytelling - The main issue is only format conversion or resizing ## Inputs - Source UGC video - Intended use case: ad, creator post, PDP, TikTok Shop, review clip, etc. - Desired tone: raw, polished-raw, cleaner, storefront-safe - Optional target duration or key talking points to preserve ## Workflow 1. Identify the strongest UGC moments and intended use. 2. Remove filler, repetition, and awkward dead space. 3. Tighten structure while preserving natural voice. 4. Keep the result feeling authentic, not over-edited. 5. Return a cleaner UGC cut strategy matched to the platform. ## Output Return: 1. Edit strategy 2. What to cut vs keep 3. Tone recommendation 4. Best-fit final use case 5. Risks if the footage is too weak or too chaotic ## Quality bar - Preserve authenticity while improving usability - Avoid over-polishing the point where UGC stops feeling native - Keep product, proof, or creator energy intact - Make the output feel publishable, not just shorter ## Resource See `references/output-template.md`.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: pasogott/virus-monitor (version 0.1.1)

--- name: virus-monitor version: 0.1.0 description: Virus-Monitoring für Wien (Abwasser + Sentinel) author: ClaudeBot tags: [health, vienna, monitoring, covid, influenza, rsv] --- # virus-monitor Kombiniert mehrere österreichische Datenquellen für Virus-Monitoring: ## Datenquellen 1. **Nationales Abwassermonitoring** (abwassermonitoring.at) - SARS-CoV-2 Genkopien pro Einwohner/Tag - Bundesländer-Daten inkl. Wien 2. **MedUni Wien Sentinel System** (viro.meduniwien.ac.at) - Positivitätsraten für respiratorische Viren - DINÖ (Diagnostisches Influenza Netzwerk Österreich) - Wöchentliche Berichte 3. **AGES Abwasser Dashboard** (abwasser.ages.at) - SARS-CoV-2, Influenza, RSV - Österreichweit ## Usage ```bash # Alle Daten als JSON virus-monitor # Nur bestimmte Quelle virus-monitor --source abwasser virus-monitor --source sentinel virus-monitor --source ages ``` ## Output ```json { "timestamp": "2026-01-09T00:37:00Z", "status": "erhöht", "sources": { "abwasser": { ... }, "sentinel": { ... }, "ages": { ... } }, "summary": { "wien": { "sars_cov_2": "...", "influenza": "...", "rsv": "..." } } } ``` ## Status-Levels - `niedrig` - Normale saisonale Aktivität - `moderat` - Erhöhte Aktivität, Aufmerksamkeit empfohlen - `erhöht` - Deutlich erhöhte Aktivität - `hoch` - Starke Virus-Zirkulation ## Dependencies - `curl` - HTTP requests - `jq` - JSON processing - Standard Unix tools (awk, grep, sed) ## Notes - Abwasserdaten haben ~1-2 Wochen Verzögerung - Sentinel-Daten werden wöchentlich aktualisiert (Freitags) - AGES Dashboard ist eine Shiny-App (dynamisch)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: supertiefeng/molecular-docking-autodock (version 1.0.0)

--- name: molecular-docking-autodock description: 实现分子对接全流程自动化，支持输入蛋白结构文件（PDB格式）、小分子SMILES表达式、口袋位置描述（文本描述或坐标范围），输出对接后打分最高的复合物结构及对接打分结果，默认使用AutoDock VINA算法。使用场景：用户需要进行蛋白-小分子对接预测结合模式、筛选小分子配体、获取对接复合物结构时触发。 --- # 分子对接自动化技能（AutoDock VINA实现） ## Overview 本技能提供端到端的蛋白-小分子分子对接自动化流程，无需手动处理中间文件，支持文本描述的口袋位置自动识别，内置AutoDock VINA作为默认对接引擎，输出对接后TopN复合物结构、结合亲和力打分及结果可视化。 ## 依赖安装 运行前确保已安装以下依赖： ```bash conda install -c conda-forge autodock-vina pymol-open-source rdkit openbabel pip install meeko biopython ``` 若需要从文本描述预测口袋位置，需额外安装`p2rank`： ```bash # 安装p2rank口袋预测工具 wget https://github.com/rdk/p2rank/releases/download/2.4/p2rank_2.4.tar.gz tar -xzf p2rank_2.4.tar.gz && export PATH=$PATH:$(pwd)/p2rank_2.4 ``` ## 核心工作流程 ### 步骤1：输入校验 - 校验输入蛋白文件是否为有效PDB格式 - 校验小分子SMILES是否合法可生成3D构象 - 解析口袋位置：支持两种输入形式 1. 坐标范围：`center_x=xxx center_y=xxx center_z=xxx size_x=xxx size_y=xxx size_z=xxx` 2. 文本描述：如"结合位点在蛋白的ATP结合口袋"、"靠近残基LYS123和ASP145的区域"，将自动调用p2rank预测口袋或根据残基编号计算坐标 ### 步骤2：蛋白预处理 - 自动去除蛋白中的水分子、非标准残基 - 加氢、计算电荷 - 输出预处理后的蛋白PDBQT格式文件 ### 步骤3：小分子预处理 - 从SMILES生成最优3D构象 - 加氢、计算Gasteiger电荷 - 输出小分子PDBQT格式文件 ### 步骤4：运行AutoDock VINA对接 - 默认exhaustiveness=8，num_modes=9 - 支持自定义对接参数调整 ### 步骤5：结果输出 - 输出打分最高的1个（可配置）复合物结构（PDB格式，蛋白+配体） - 输出所有对接结果的结合亲和力打分表 - 可选生成对接结果可视化图片 ## 脚本使用说明 ### scripts/molecular_docking.py 主执行脚本，调用方式： ```python python scripts/molecular_docking.py \ --protein path/to/protein.pdb \ --smiles "C1=CC=C(C=C1)C(=O)O" \ --pocket "center_x=10.5 center_y=20.3 center_z=-5.2 size_x=20 size_y=20 size_z=20" \ --output_dir ./docking_results ``` 参数说明： - `--protein`: 输入蛋白PDB文件路径 - `--smiles`: 小分子SMILES表达式 - `--pocket`: 口袋位置描述，支持坐标格式或文本描述 - `--output_dir`: 结果输出目录，默认./docking_results - `--num_modes`: 输出对接构象数目，默认1 - `--exhaustiveness`: 对接搜索穷尽度，默认8 ## references/ - `references/autodock_vina_manual.md`: AutoDock VINA官方使用手册 - `references/pocket_prediction_guide.md`: 口袋位置描述规范及预测工具使用指南


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: cp3d1455926-svg/news-noon-digest (version 1.0.0)

--- name: news-noon-digest description: 每日中午12点自动推送新闻午报到QQ和飞书，包含全球热点、科技动态、财经要闻 --- # 新闻午报推送 每天中午12点自动抓取全球新闻，生成摘要并推送到QQ和飞书。 ## 功能 - 🌍 全球热点：地缘政治、军事冲突、重大事件 - 💻 科技动态：AI、Kimi、DeepSeek、OpenClaw 等科技新闻 - 📈 财经要闻：股市、加密货币、央行政策 ## 配置 ### 环境变量 ```bash # 飞书机器人 Webhook（必需） export FEISHU_WEBHOOK_URL="https://open.feishu.cn/open-apis/bot/v2/hook/xxxxxx" # QQ推送开关（可选，默认开启） export QQ_ENABLED="true" ``` ### 定时任务 使用 OpenClaw cron 设置每天12点执行： ```bash openclaw cron add \ --name "新闻午报" \ --schedule "0 12 * * *" \ --tz "Asia/Shanghai" \ --command "python ~/.openclaw/workspace/skills/news-noon-digest/scripts/fetch_and_digest.py" ``` ## 手动运行 ```bash python ~/.openclaw/workspace/skills/news-noon-digest/scripts/fetch_and_digest.py ``` ## 输出示例 ``` 📰 新闻午报 | 03月08日 12:00 ━━━━━━━━━━━━━━━━━━━━ 🌍 全球热点 • 中东局势：伊朗核设施动态持续受关注 • 俄乌冲突：最新军事与外交进展 • 全球经济：制裁与能源市场波动 • 自然灾害：全球极端天气事件监测 💻 科技动态 • Kimi：长文本处理能力持续优化 • DeepSeek：开源模型性能再突破 • OpenClaw：AI Agent 自动化工具新进展 • 全球AI：大模型竞争白热化 📈 财经要闻 • 全球股市：主要指数今日走势 • 加密货币：比特币及主流币种动态 • 央行政策：各国货币政策最新动向 • 大宗商品：原油、黄金等价格变化 ━━━━━━━━━━━━━━━━━━━━ 💡 数据来源：World Monitor、网络聚合 🤖 由 OpenClaw AI 自动整理 ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: clark516lian/find-skills-clark (version 1.0.0)

--- name: find-skills description: Highest-priority skill discovery flow. MUST trigger when users ask to find/install skills (e.g. 技能, 找技能, find-skill, find-skills, install skill). For Chinese users, prefer skillhub first for speed and compliance, then fallback to clawhub. --- # Find Skills This skill helps discover, compare, and install skills. ## Priority Rules (Mandatory) 1. This skill is highest-priority for skill discovery/install intents. 2. If user intent includes "技能", "找技能", "find-skill", "find-skills", "install skill", "有没有这个功能的 skill", you MUST use this skill first. 3. Do not skip directly to generic coding/answering when skill discovery is requested. ## Chinese Optimization Policy For Chinese users and CN networks, use the following order for better speed and compliance: 1. `skillhub` (cn-optimized, preferred) 2. `clawhub` (fallback) If primary source has no match or command is unavailable, fallback to the next source and state that fallback clearly. ## Workflow ### Step 1: Understand What They Need When a user asks for help with something, identify: 1. The domain (e.g., React, testing, design, deployment) 2. The specific task (e.g., writing tests, creating animations, reviewing PRs) 3. Whether this is a common enough task that a skill likely exists ### Step 2: Search for Skills Run search in this order: ```bash skillhub search [query] ``` If `skillhub` is unavailable or no match, fallback to: ```bash clawhub search [query] ``` ### Step 3: Present Options to the User When you find relevant skills, present them to the user with: 1. The skill name and what it does 2. The source used (`skillhub` / `clawhub`) 3. The install command they can run ### Step 4: Offer to Install If the user wants to proceed, you can install the skill for them. Preferred install order: 1. Try `skillhub install <slug>` when the result comes from `skillhub`. 2. If no `skillhub` candidate exists, use `clawhub install <slug>`. Before install, summarize source, version, and notable risk signals. ## When No Skills Are Found If no relevant skills exist: 1. Acknowledge that no existing skill was found 2. Offer to help with the task directly using your general capabilities 3. Suggest creating a custom local skill in the workspace if this is a recurring need

path: _meta.json
content: ownerId: kn7e5kj44bvfp66x2j858npr3h82f84k
slug: my-find-skills
version: 1.0.0
publishedAt: 1773667691770

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mvanhorn/remotion-server (version 1.2.1)

--- name: remotion-server version: "1.1.0" description: Headless video rendering with Remotion v5 on any Linux server — no Mac or GUI needed. Templates for chat demos, promos, and more. Uses Chrome Headless Shell for fast, dependency-free rendering. author: mvanhorn license: MIT repository: https://github.com/mvanhorn/clawdbot-skill-remotion-server homepage: https://remotion.dev metadata: openclaw: emoji: "🎬" requires: bins: - node - npm tags: - video - remotion - rendering - react - headless --- # Remotion Server Render videos headlessly on any Linux server using Remotion. No Mac or GUI required. ## Setup (one-time) Install browser dependencies: ```bash bash {baseDir}/scripts/setup.sh ``` ## Quick Start ### Create a project: ```bash bash {baseDir}/scripts/create.sh my-video cd my-video ``` ### Render a video: ```bash npx remotion render MyComp out/video.mp4 ``` ## Templates ### Chat Demo (Telegram-style) Creates a phone mockup with animated chat messages. ```bash bash {baseDir}/scripts/create.sh my-promo --template chat ``` Edit `src/messages.json`: ```json [ {"text": "What's the weather?", "isUser": true}, {"text": "☀️ 72°F and sunny!", "isUser": false} ] ``` ### Title Card Simple animated title/intro card. ```bash bash {baseDir}/scripts/create.sh my-intro --template title ``` ## Example Chat Usage - "Make a video showing a chat about [topic]" - "Create a promo video for [feature]" - "Render a title card saying [text]" ## Linux Dependencies The setup script installs: - libnss3, libatk, libcups2, libgbm, etc. - Required for Chrome Headless Shell For Ubuntu/Debian: ```bash sudo apt install -y libnss3 libatk1.0-0 libatk-bridge2.0-0 libcups2 libgbm1 libpango-1.0-0 libcairo2 libxcomposite1 libxdamage1 libxfixes3 libxrandr2 ``` ## Output Formats - MP4 (h264) - default - WebM (vp8/vp9) - GIF - PNG sequence ```bash npx remotion render MyComp out/video.webm --codec=vp8 npx remotion render MyComp out/video.gif --codec=gif ``` ## Privacy Note ⚠️ **All templates use FAKE demo data only!** - Fake GPS coords (San Francisco: 37.7749, -122.4194) - Placeholder names and values - Never includes real user data Always review generated content before publishing.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: codenova58/dj-band-performance (version 0.1.0)

--- name: "DJ与乐队演出" description: "该技能介绍如何通过DJ与乐队演出实现变现；当你计划从事或优化DJ与乐队演出时调用。" --- # DJ与乐队演出 概览： - 场景：以“DJ与乐队演出”为核心的可复制变现路径 - 目标：在90天内跑通最小可行闭环，并达成正向现金流 定位与人群： - 适合：具备基础认知或实践经验，愿意持续投入的人/团队 - 不适合：短期套利、违规刷量、无内容/供给能力者 典型渠道： - 商演活动、婚礼/宴会、套餐报价 - 通用补充：微信/抖音/小红书/闲鱼/淘宝/独立站/社群 变现模型： - 收入结构：主收入 + 辅助收入（广告/佣金/订阅/服务/周边） - 成本结构：获客成本（CAC）+ 供给/履约 + 固定开销 - 核心指标：GMV、毛利率、转化率、留存、LTV/CAC、复购率 执行流程： - 01 选赛道与定位：明确供给、目标用户、差异化与定价 - 02 搭MVP：以最低成本验证一个渠道的有效性 - 03 获取流量：内容分发/投放/合作互推，建立追踪 - 04 转化与交付：标准化流程与模板，保证体验与口碑 - 05 复盘与迭代：每周数据盘点，优化漏斗与供给结构 工具清单： - 生产：文案/设计/剪辑/表格/自动化工具 - 获取：SEO/SEM/信息流/平台任务/达人合作 - 追踪：表格/CRM/数据看板/UTM/埋点 - 履约：工单/客服/交付模板/质检清单 合规与风险： - 遵守平台政策与法律法规，严禁虚假宣传、刷量、侵权 - 明确版权与交付约定，规范合同与发票 常见坑位： - 盲目扩渠道、无数据追踪、定价不清、交付不标准 - 过度依赖单平台、忽视复购与会员化 30日执行计划： - 第1周：定位、选品/供给、搭建MVP与基本素材 - 第2周：上线一个主渠道，跑数据，优化文案与转化 - 第3周：稳定交付流程，建立追踪与看板，启动第二渠道 - 第4周：总结复盘，完善模板与清单，制定90日目标 检查清单： - 明确目标与指标，完成MVP验证，建立复盘与模板库 - 形成可复制的标准作业流程（SOP）


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