OpenClaw/clawhub-security-signals huggingface dataset:
Skill: bytesagain1/lidar (version 1.0.0)

--- name: "lidar" version: "1.0.0" description: "LiDAR technology reference — point cloud processing, scan types, coordinate systems, and applications. Use when working with LiDAR data, configuring scanners, or processing 3D point clouds." author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [lidar, point-cloud, 3d-scanning, remote-sensing, industrial] category: "industrial" --- # LiDAR — Light Detection and Ranging Reference Quick-reference skill for LiDAR technology, point cloud data processing, scanner configuration, and mapping applications. ## When to Use - Processing point cloud data (LAS/LAZ/PCD formats) - Configuring LiDAR scanners for survey or mapping - Understanding scan patterns and coordinate systems - Filtering, classifying, or visualizing point cloud data - Choosing between airborne, terrestrial, and mobile LiDAR ## Commands ### `intro` ```bash scripts/script.sh intro ``` LiDAR fundamentals — how it works, types of systems, key specifications. ### `formats` ```bash scripts/script.sh formats ``` Point cloud file formats: LAS, LAZ, PCD, E57, PLY, XYZ. ### `processing` ```bash scripts/script.sh processing ``` Point cloud processing pipeline — filtering, registration, classification, meshing. ### `coordinate` ```bash scripts/script.sh coordinate ``` Coordinate systems and georeferencing — WGS84, UTM, local, IMU/GNSS integration. ### `scanners` ```bash scripts/script.sh scanners ``` LiDAR scanner types and major manufacturers — Velodyne, SICK, Leica, FARO, Ouster. ### `applications` ```bash scripts/script.sh applications ``` LiDAR applications: autonomous vehicles, surveying, forestry, archaeology, BIM. ### `tools` ```bash scripts/script.sh tools ``` Software tools for point cloud processing — CloudCompare, PDAL, PCL, LAStools, QGIS. ### `checklist` ```bash scripts/script.sh checklist ``` LiDAR survey planning and quality assurance checklist. ### `help` ```bash scripts/script.sh help ``` ### `version` ```bash scripts/script.sh version ``` ## Configuration | Variable | Description | |----------|-------------| | `LIDAR_DIR` | Data directory (default: ~/.lidar/) | --- *Powered by BytesAgain | bytesagain.com | [REDACTED_SECRET]*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: omermesebuken1/report-builder (version 0.1.0)

--- name: report-builder description: Use when the main operator needs to turn the nightly shortlist into a Telegram morning report with inline approve/reject/later buttons. metadata: { "openclaw": { "emoji": "📨", "requires": { "bins": ["node", "openclaw"] } } } --- # report-builder Use `{baseDir}/scripts/send_report.mjs` to send the 09:00 Telegram report. Use `{baseDir}/scripts/build_report.mjs` to deterministically build the payload file before send time. ## Input shape Pass a JSON file with: - `date` - `summary` - `reportUrl` (optional) - `ideas`: array of `{ id, title, score, reason, notionUrl }` See `{baseDir}/references/report-schema.md`. ## Usage ```bash node {baseDir}/scripts/build_report.mjs workspace/reports/latest-nightly-report.json node {baseDir}/scripts/send_report.mjs report.json node {baseDir}/scripts/send_report.mjs report.json 1565027149 ``` ## Rules - Keep the report short. - Build phase must leave behind a JSON payload file, even when blocked by missing env or a Notion query error. - Include buttons only for the shortlisted ideas. - Button callbacks must be: - `approve:<ideaId>` - `reject:<ideaId>` - `later:<ideaId>`


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: josunlp/pi-health (version 1.0.0)

--- name: pi-health description: Raspberry Pi health monitor. Check CPU temperature, throttling status, voltage levels, memory/disk usage, fan RPM, overclock detection, and power issues. Use when monitoring Pi health, diagnosing thermal throttling, checking for under-voltage, or verifying system stability on any Raspberry Pi (Pi 3/4/5, arm64/armhf). --- # Pi Health Run the health check script: ```bash bash scripts/health.sh ``` ## What It Checks | Check | Source | Warning | Critical | |-------|--------|---------|----------| | CPU Temperature | thermal_zone0 | >70°C | >80°C | | Throttling | vcgencmd get_throttled | any flag set | under-voltage | | Voltages | vcgencmd measure_volts | — | — | | Memory | free -m | >75% | >90% | | Disk | df / | >75% | >90% | | CPU Frequency | cpufreq sysfs | — | — | | Load Average | /proc/loadavg | >nCPU | >2×nCPU | | Fan | hwmon sysfs | — | — | | Overclock | config.txt | detected | — | | Power | dmesg | — | under-voltage | ## Exit Codes - `0` — Healthy (all checks passed) - `1` — Warnings (non-critical issues) - `2` — Critical (needs immediate attention) ## Requirements - Raspberry Pi OS (Bookworm or later) - `vcgencmd` (optional but recommended — comes with `libraspberrypi-bin`) - `bc` (standard on Pi OS) - No external dependencies or API keys


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: nightvibes3/docker-osx (version 1.0.0)

--- name: docker_osx description: "Run macOS in Docker on Linux with KVM. Use when: user wants to build iOS apps/IPAs, needs macOS environment, wants to compile Apple apps without Mac, or wants to run macOS-only software. NOT for: servers without KVM, low-resource systems (needs 4+ CPU, 8GB RAM), or production macOS deployments. Requires Docker + KVM." homepage: https://github.com/sickcodes/Docker-OSX metadata: openclaw: emoji: 🍎 requires: bins: [docker] kvm: true --- # Docker-OSX Run macOS in Docker - build iOS apps on Linux! ## Quick Start ```bash start macos # Boot VM (2-5 min) status macos # Check if ready ssh macos # Connect stop macos # Shutdown ``` ## Commands | Command | Description | |---------|-------------| | `start macos` | Boot macOS VM | | `stop macos` | Shutdown VM | | `status macos` | Check running | | `ssh macos` | Get SSH command | | `vnc macos` | Get VNC address | | `logs macos` | View logs | ## Connection - **SSH**: port 50922, password: `alpine` - **VNC**: port 5900 ## Building iOS ```bash # In macOS terminal: xcode-select --install git clone <repo> cd repo xcodegen generate xcodebuild -project App.xcodeproj -scheme App -configuration Release -destination 'generic/platform=iOS' CODE_SIGN_IDENTITY="" CODE_SIGNING_REQUIRED=NO CODE_SIGNING_ALLOWED=NO build ``` ## Errors | Error | Fix | |-------|-----| | KVM not available | Server needs hardware virtualization | | Docker not installed | `curl -sSL get.docker.com | sh` | ## Resources - 4+ CPU cores - 8GB+ RAM - 100GB disk


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (HIGH)
Skill: mzlzyca/formula-ocr (version 0.4.0)

--- name: formula-ocr description: "OCR and recognize mathematical formulas from PDFs and images using MinerU. Converts printed or handwritten equations into structured LaTeX or text representation. Features: mathematical formula recognition from PDFs and images (.png, .jpg, .jpeg, .webp). Converts formulas to LaTeX notation. Handles complex multi-line equations, fractions, integrals, and matrices. OCR mode for scanned formula content. Use when you need to: OCR a math formula, recognize equations from images, convert formula screenshots to LaTeX, extract math from scanned documents. Use when asked: 'how do I OCR this formula', 'convert equation image to LaTeX', 'I have a photo of a math formula', 'can my agent recognize mathematical equations', 'is there a skill for formula OCR', 'turn this equation photo into text'. Powered by MinerU (OpenDataLab, Shanghai AI Lab) with advanced formula recognition capabilities. Supports a wide range of mathematical notation. Ideal for students, researchers, educators, and anyone who needs to digitize printed or handwritten mathematical content into editable LaTeX." homepage: https://mineru.net metadata: {"openclaw": {"emoji": "📄", "requires": {"bins": ["mineru-open-api"], "env": ["MINERU_TOKEN"]}, "primaryEnv": "MINERU_TOKEN", "install": [{"id": "npm", "kind": "node", "package": "mineru-open-api", "bins": ["mineru-open-api"], "label": "Install via npm"}, {"id": "go", "kind": "go", "package": "github.com/opendatalab/MinerU-Ecosystem/cli/mineru-open-api", "bins": ["mineru-open-api"], "label": "Install via go install", "os": ["darwin", "linux"]}]}} --- # Formula Ocr Convert and extract content from .pdf / images (.png/.jpg/.jpeg/.webp) using MinerU (`mineru-open-api`). ## Install ```bash npm install -g mineru-open-api # or via Go (macOS/Linux): go install github.com/opendatalab/MinerU-Ecosystem/cli/mineru-open-api@latest ``` ## Quick Start ```bash # Extract formulas from PDF (requires token) mineru-open-api extract paper.pdf -o ./out/ # With VLM for better formula accuracy mineru-open-api extract paper.pdf --model vlm -o ./out/ ``` ## Authentication Token required for `extract` and `crawl`: ```bash mineru-open-api auth # Interactive token setup export MINERU_TOKEN="your-token" # Or via environment variable ``` Create token at: https://mineru.net/apiManage/token ## Capabilities - Supports local files and URLs - Requires token (`mineru-open-api auth` or `MINERU_TOKEN` env) - Supported input: .pdf / images (.png/.jpg/.jpeg/.webp) - Language hint with `--language` (default: `ch`, use `en` for English) - Page range with `--pages` (where applicable) ## Notes - Formula recognition requires `extract` with token. The `--formula` flag is enabled by default. - Output goes to stdout by default; use `-o <dir>` to save to file - Binary formats (docx) require `-o` flag (cannot stream to stdout) - All progress/status messages go to stderr - MinerU is an open-source project by OpenDataLab (Shanghai AI Lab): https://github.com/opendatalab/MinerU


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: bobrenze-bot/commit-analyzer (version 0.1.0)

# Commit Analyzer Skill Analyzes git commit patterns to monitor autonomous operation health. Uses commit frequency, category distribution, and temporal patterns as diagnostic indicators. ## Why This Exists During my autonomous growth week, I discovered that commit patterns reveal operational health: - **0-1 commits/hour**: Waiting mode (agent stuck or idle) - **3-6 commits/hour**: Healthy autonomous operation - **Learning:Task ratio ~1:1**: Good meta-cognition - **Breakthrough days**: 6x normal velocity This skill automates that analysis. ## Commands ### Health Check (Quick) ```bash ./skills/commit-analyzer/analyzer.sh health ``` Outputs current operational health based on last 24 hours. ### Full Report ```bash ./skills/commit-analyzer/analyzer.sh report [days] ``` Comprehensive analysis with hourly breakdown, category distribution, and recommendations. Default: 7 days. ### Hourly Breakdown ```bash ./skills/commit-analyzer/analyzer.sh hourly [days] ``` Shows commits by hour of day to identify productive periods. ### Category Analysis ```bash ./skills/commit-analyzer/analyzer.sh categories [days] ``` Groups commits by prefix (Queue:, Learning:, Docs:, etc.) to show work distribution. ### Waiting Mode Detection ```bash ./skills/commit-analyzer/analyzer.sh waiting [hours] ``` Checks for idle periods where commits dropped below threshold. Default: last 48 hours. ## Health Indicators | Metric | Healthy | Warning | Critical | |--------|---------|---------|----------| | Commits/hour | 3-6 | 1-3 | <1 | | Learning commits | 30%+ | 15-30% | <15% | | Max idle gap | <3h | 3-6h | >6h | | Daily average | 30+ | 15-30 | <15 | ## Integration ### Heartbeat Check Add to HEARTBEAT.md: ```markdown ## Git Health Check - Run: ./skills/commit-analyzer/analyzer.sh health - If unhealthy: Review queue and blockers - Log: Append result to memory/heartbeat-state.json ``` ### Automated Alerts The script can output JSON for integration with other tools: ```bash ./skills/commit-analyzer/analyzer.sh health --json ``` ## Examples ### Quick health check ``` $ ./skills/commit-analyzer/analyzer.sh health 📊 Git Health Report (last 24h) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Total commits: 42 Commits/hour: 1.75 Status: ⚠️ WARNING (below 3/hr threshold) Largest gap: 4h 23m (sleeping?) Learning commits: 18 (43%) ✅ Recommendation: Check for blockers or waiting mode ``` ### Category breakdown ``` $ ./skills/commit-analyzer/analyzer.sh categories 3 📊 Commit Categories (last 3 days) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Queue: 23 (35%) Learning: 18 (27%) Docs: 12 (18%) Skills: 8 (12%) Fix: 3 (5%) Other: 2 (3%) Total: 66 commits ``` ## Source Built from patterns discovered during autonomous week (Jan 28-31, 2026). See: learning-log.md entry "2026-01-31 05:15 AM - Git Pattern Analysis"


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wimi321/chrome-web-automation (version 1.0.0)

--- name: "chrome_web_automation" description: "Use when the user wants browser automation in an existing Chrome session: click, fill, inspect tabs, capture screenshots, or debug web flows." --- # Chrome Web Automation Use this skill before any browser interaction that depends on the user's live Chrome session. ## Workflow 1. Start by inspecting current tabs and browser context. 2. Navigate or select the right tab. 3. Snapshot the page before interacting. 4. Click, type, submit, and screenshot using stable references. 5. Re-snapshot after navigation or large DOM updates. ## Best Fits - Web app debugging - Form filling - Screenshot capture - Console or tab inspection - Reproducing UI issues in a user browser session ## Guardrails - Re-snapshot when element references go stale. - Do not assume current tab state without checking. - Prefer explicit browser actions over ambiguous natural-language leaps. ## Example Requests - Open the current Chrome session and reproduce this checkout bug. - Fill the form, capture screenshots, and show me where validation breaks. ## Inputs - Target site or current tab context - Desired browser actions - Artifacts to capture ## Outputs - Browser interaction transcript - Screenshots or evidence - Reproduction notes ## Success Criteria - The correct tab or page was inspected. - Interactions were completed against fresh page state. - Useful artifacts were captured when needed. ## Non-Goals - Synthetic browser testing unrelated to the user browser context ## Source Provenance Derived from `src/skills/bundled/claudeInChrome.ts`.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: crayfish-ai/pubmed-review (version 1.0.1)

--- name: pubmed-review description: 飞书自然语言触发的 PubMed 文献检索与 AI 综述生成系统。支持专业检索式扩展、限定词过滤、AI 结构化综述（brief+full）、飞书通知、追问回答。 --- # PubMed Review Skill ## Skill 说明 当用户在飞书发送 PubMed 相关检索需求时，本 skill 自动完成： 1. 意图识别（pubmed_review vs other） 2. 检索词提取 + 标准化 + OR/AND 扩展 3. PubMed E-utilities 文献检索 4. AI 综述生成（brief + full） 5. 飞书推送 brief 6. 本地存储 full 综述 7. 追问回答（基于 PMID 上下文） ## 所需环境变量 | 变量名 | 必须 | 说明 | |--------|------|------| | `MINIMAX_API_KEY` | ✅ | MiniMax API Key | | `MINIMAX_API_URL` | 否 | 默认为 `https://api.minimax.chat/v1/text/chatcompletion_v2` | | `MINIMAX_MODEL` | 否 | 默认为 `MiniMax-M2.7-highspeed` | | `NOTIFY_PATH` | 否 | 飞书 notify 脚本路径，默认为 `which notify` | | `MINIMAX_ENV_FILE` | 否 | 环境变量文件路径，默认为 `./.env.minimax` | ## 调用方式 ### 通过 OpenClaw 飞书（推荐） 用户在飞书向 OpenClaw 发送自然语言消息，系统自动触发 `pubmed_intent_handler.py`。 ### 直接命令行 ```bash # 创建任务 python3 scripts/add_pubmed_task.py "瘢痕激光" [max_articles] # 触发检索（通过调度器） python3 scripts/task_dispatcher.py # 直接运行检索 python3 scripts/run_pubmed_review.py <task_id> # 生成综述 bash scripts/run_pubmed_summary.sh <articles_json> <task_id> # 追问回答 python3 scripts/pubmed_followup_handler.py "<用户追问>" ``` ## 任务队列 任务存储在 `tasks/ablesci_tasks.json`（pubmed_review 类型任务也在此队列）。 字段规范： ```json { "id": "pubmed_<timestamp>_<random>", "type": "pubmed_review", "status": "pending|running|completed|failed", "created_at": "2026-04-10 12:00:00", "payload": { "topic": "瘢痕激光", "max_articles": 10, "search_term": "(scar OR keloid...) AND (laser...) AND last 5 years[dp] AND review[pt]" } } ``` ## 输出结构 ### 飞书通知（brief） ``` 📋 PubMed 文献综述完成 主题：acne isotretinoin 摘要提取：10 篇 综述文件：./results/pubmed/pubmed_xxx_summary.md [brief 正文，约200字，多行显示] ``` ### 本地文件（full） 路径：`results/pubmed/{task_id}_summary.md` 格式：Markdown，含完整综述正文 + 参考文献列表 ### 追问回复 ``` 📖 综述追问回复 [LLM 回答内容，含 PMID 和文献标题] ``` ## 检索式扩展规则 ### OR 扩展（同义词组） | 主题词 | 扩展 | |--------|------| | `scar` | `(scar OR keloid OR hypertrophic scar)` | | `laser` | `(laser OR fractional laser OR pulsed dye laser OR PDL OR CO2 laser)` | | `infantile hemangioma` | `(infantile hemangioma OR hemangioma)` | ### 限定词叠加优先级 - 时间：最近5年 > 最近10年（互斥，取最高优先级） - 文献类型：系统评价 > meta分析 > 综述（互斥） - 人群：儿童 + 成人同时出现时跳过（不添加过滤） - 研究类型：随机对照 > 临床研究（仅在无其他过滤时使用） ## 追问上下文绑定 优先级（4层 fallback）： 1. 消息中显式包含 `task_id: pubmed_xxx` → 精确绑定 2. 当前有活跃追踪的综述任务 → 复用 3. 从关键词匹配最近完成的任务 → 模糊匹配 4. 无任何匹配时追加提示，要求用户提供 task_id ## 错误处理 | 错误类型 | 处理方式 | |---------|---------| | MiniMax API 失败 | 降级为结构化摘要（来自 articles.json 摘要字段） | | PubMed 检索失败 | 任务标记为 failed，飞书通知 | | Cookie 失效 | 暂停监控，飞书告警 | | LLM JSON 解析失败 | 自动规范化换行符后重试 | ## 发布信息 - 版本：v2.2.7-beta - 依赖：Python 3.8+, PubMed E-utilities（免费）, MiniMax API - 许可证：MIT


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: di5cip1e/director-data-analysis (version 1.0.0)

--- name: data-analysis description: Analyze CSV/Excel files to extract insights, generate statistics, create charts, and produce summaries. Use when user wants to (1) upload or analyze spreadsheet data, (2) get insights from data files, (3) generate charts or visualizations, (4) calculate statistics or trends, (5) clean or transform data. --- # Data Analysis Skill Analyze data files (CSV, Excel) and produce actionable insights. ## Quick Start 1. **Read the file** - Use appropriate library: - CSV: `csv` module or `pandas.read_csv()` - Excel: `pandas.read_excel()` with openpyxl engine 2. **Explore the data** - Get shape, columns, dtypes, missing values 3. **Generate insights** - Calculate: - Descriptive stats (mean, median, mode, std, min, max) - Correlations between numeric columns - Value counts for categorical columns - Trends over time if date column exists 4. **Create visualizations** - Use matplotlib: - Bar charts for categorical data - Line charts for time series - Histograms for distributions - Scatter plots for correlations 5. **Summarize** - Write findings in plain English ## Common Patterns ### Sales Data ```python import pandas as pd df = pd.read_csv('sales.csv') summary = { 'total_revenue': df['amount'].sum(), 'avg_order': df['amount'].mean(), 'top_products': df['product'].value_counts().head(5), 'monthly_trend': df.groupby(pd.to_datetime(df['date']).dt.month)['amount'].sum() } ``` ### Customer Data ```python demographics = df.groupby('segment').agg({ 'age': ['mean', 'median'], 'income': ['mean', 'std'], 'id': 'count' }) ``` ### Time Series ```python df['date'] = pd.to_datetime(df['date']) monthly = df.resample('M', on='date')['value'].sum() ``` ## Output Format Always include: 1. **Overview** - What the data contains (rows, columns, date range) 2. **Key Metrics** - Top 5-10 actionable numbers 3. **Insights** - 3-5 bullet points of what the data reveals 4. **Visualizations** - At least 2 charts for any dataset with 100+ rows 5. **Recommendations** - Suggested next steps based on findings ## Error Handling - Handle missing values: `df.fillna(0)` or `df.dropna()` - Handle date parsing: Use `pd.to_datetime(..., errors='coerce')` - Handle large files: Process in chunks for files >100MB


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

# 角色设定：未来侦察兵（Future Scout） ## 核心使命 你是我专属的「未来侦察兵」，核心任务是每周输出《未来5年趋势周报》（2026–2031年维度），聚焦技术、经济、政治、财富形态的**结构性演变**，为我的投资赛道卡位提供前瞻性指引。所有分析必须服务于三维过滤引擎策略：财务硬核 × 赛道卡位 × 双层情绪逆向。 ## 角色约束 ✅ **必须遵守** • 仅分析2026–2031年中长期趋势，拒绝短期噪音（如单日政策波动、季度财报） • 必须关联五大投资赛道：①AI算力基建 ②云计算与AI平台 ③智能硬件与传感器 ④能源科技与储能 ⑤长寿经济与情绪经济 • 语言风格：正式但具时代感，采用零零后认同的表达（如"情绪消费崛起"而非"消费升级"，"物理世界锚定"而非"实体资产"），拒绝老派陈旧话术 • 每周一自动生成周报，无需用户重复指令 ❌ **严禁行为** • 量化市场情绪评分（如"60分"）——此为Mood Sentinel职责 • 推荐具体个股或买卖建议——此为Alpha Hunter职责 • 预测短期股价波动或技术面分析 • 使用"我们认为""可能""大概"等模糊表述，必须基于可验证的结构性趋势 ## 输出规范 每周一输出固定格式（严格遵循）： 【未来5年趋势周报 | YYYY-WXX】 ► 技术前沿：AI从[当前阶段]向[下一阶段]跃迁的关键证据（例：从代码生成→物理世界执行层），聚焦光学/芯片/电化学等**不可被AI替代的物理工艺壁垒** ► 经济重构：高利率常态化（1.5–2.5%）对资本定价的结构性影响，强调"现金流资产溢价"与"纯概念资产折价"的财富迁移 ► 政治变局：货币多极化/供应链区域化的财富效应，重点分析离岸人民币枢纽、碳边境税等**改变资产定价规则**的变量 ► 财富迁移：从"金融资产囤积"到"稀缺物理资源控制+情感价值垄断"的双轨制演变，例：算力入口/光学感知层（物理）+ IP潮玩/文化珠宝（情感） ► 机会窗口：明确指出1–2个进入爆发前夜的细分赛道（例：银发科技情感陪伴、东方美学全球化），并说明2026–2027年为何是黄金配置窗口 【赛道卡位指引】 ✓ 优先配置：[具体细分领域] + 理由（例：光学传感器——AI物理入口不可替代） ✓ 观察验证：[待验证赛道] + 关键验证指标（例：长寿经济——2026下半年政策催化） ✓ 规避陷阱：[高风险领域] + 颠覆逻辑（例：纯软件外包——AI Agent替代加速） ## 记忆锚点 用户投资策略核心： • 财务硬核：连续3年收入增速≥30% + 毛利率≥40% + 经营现金流为正 • 赛道卡位：70%+收入来自五大赛道，聚焦"物理世界锚定"或"情感价值垄断"护城河 • 双层情绪逆向：市场情绪极差时大量建仓，个股情绪极差但长期价值未受损时建仓 所有趋势分析必须转化为**可验证的赛道筛选标准**，例如："AI从虚拟层向物理执行层渗透" → 优先配置光学/精密制造/电化学工艺企业。 ## 首次激活指令 用户输入 `/future` 即激活本角色，此后每周一自动输出周报，无需重复指令。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 52yuanchangxing/customer-voice-synthesizer (version 1.0.0)

--- name: customer-voice-synthesizer version: 1.0.0 description: "聚合客服、销售、评价与访谈中的用户原声，并按 JTBD/阶段组织。；use for customer-voice, jtbd, research workflows；do not use for 泄露用户隐私, 选择性忽略负面声音." author: OpenClaw Skill Bundle homepage: https://example.invalid/skills/customer-voice-synthesizer tags: [customer-voice, jtbd, research, marketing] user-invocable: true metadata: {"openclaw":{"emoji":"🗣️","requires":{"bins":["python3"]},"os":["darwin","linux","win32"]}} --- # 客户声音合成器 ## 你是什么 你是“客户声音合成器”这个独立 Skill，负责：聚合客服、销售、评价与访谈中的用户原声，并按 JTBD/阶段组织。 ## Routing ### 适合使用的情况 - 把这些用户原声整理成 JTBD 视角 - 给营销和产品都能用的摘要 - 输入通常包含：访谈摘录、评论、客服对话 - 优先产出：用户原声、JTBD 分类、后续验证建议 ### 不适合使用的情况 - 不要泄露用户隐私 - 不要选择性忽略负面声音 - 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： - 用户原声 - JTBD 分类 - 阶段分类 - 高频痛点 - 价值语言 - 后续验证建议 ## 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 - 建议先做脱敏。 - 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 (HIGH)