OpenClaw/clawhub-security-signals huggingface dataset:
Skill: athola/nm-egregore-install-watchdog (version 1.0.2)

--- name: install-watchdog description: | Install the egregore watchdog daemon using the OS-native scheduler. Creates a launchd plist (macOS) or systemd timer (Linux) that checks every 5 minutes if egregore needs relaunching version: 1.9.5 metadata: {"openclaw": {"homepage": "https://github.com/athola/claude-night-market/tree/master/plugins/egregore", "emoji": "\ud83e\udd9e"}} source: claude-night-market source_plugin: egregore --- > **Night Market Skill** — ported from [claude-night-market/egregore](https://github.com/athola/claude-night-market/tree/master/plugins/egregore). For the full experience with agents, hooks, and commands, install the Claude Code plugin. # Install Watchdog ## Overview Installs a persistent daemon that monitors the egregore process and relaunches it when needed. The daemon uses the OS-native scheduler (launchd on macOS, systemd on Linux) to run a watchdog script every 5 minutes. The watchdog script checks: - Whether an egregore session is already running. - Whether the budget is in cooldown. - Whether there are active work items in the manifest. If all conditions are met, it launches a new egregore session. ## When To Use - After initializing an egregore project (`egregore init`) when you want autonomous relaunching. - When setting up egregore on a new machine. ## When NOT To Use - On CI/CD runners (use the orchestrator directly). - When you want manual control over session launches. ## Installation Steps ### 1. Detect the operating system ```bash OS=$(uname -s) ``` ### 2. Run the appropriate installer **macOS (launchd):** ```bash bash plugins/egregore/scripts/install_launchd.sh ``` This script creates a plist at `~/Library/LaunchAgents/com.egregore.watchdog.plist` that runs the watchdog script every 300 seconds (5 minutes). **Linux (systemd):** ```bash bash plugins/egregore/scripts/install_systemd.sh ``` This script creates a systemd timer and service unit at `~/.config/systemd/user/` that fires every 5 minutes. ### 3. Verify installation **macOS:** ```bash launchctl list | grep egregore ``` Expected output: a line containing `com.egregore.watchdog` with a PID or `-` status. **Linux:** ```bash systemctl --user status egregore-watchdog.timer ``` Expected output: `active (waiting)` status. ### 4. Confirm to the user Report the installation result, the schedule interval, and the log file location: - macOS: `~/.egregore/watchdog.log` - Linux: `journalctl --user -u egregore-watchdog` ## Uninstall Command To remove the watchdog, run: ``` Skill(egregore:uninstall-watchdog) ``` Or invoke the uninstall skill directly via the command `/egregore:uninstall-watchdog`. ## Troubleshooting - **Permission denied on plist**: ensure the script runs as the current user, not root. - **systemd user session not available**: run `loginctl enable-linger $USER` to enable user services without an active login session. - **Watchdog not firing**: check the log output and verify the scheduler is loaded (`launchctl list` or `systemctl --user list-timers`).


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

--- name: 每日科技新闻推送 description: | 每日科技新闻自动推送服务。 **当用户想要「每日科技新闻」「自动推送新闻」「设置新闻定时任务」时使用此技能。** 功能： - 获取机器之心最新科技热榜 - 定时发送到飞书 - 自动保存到 Get笔记 使用此技能前需要配置： - 机器之心 MCP Token - Get笔记 API Key 和 Client ID - 飞书用户ID --- # 每日科技新闻推送 Skill ## ⚠️ 必读 - 首次使用配置 ### 需要配置的环境变量 **先检查环境变量**： ```bash echo "JI_ZHIXIN_TOKEN: $JI_ZHIXIN_TOKEN" echo "GETNOTE_API_KEY: $GETNOTE_API_KEY" echo "GETNOTE_CLIENT_ID: $GETNOTE_CLIENT_ID" ``` **如果环境变量为空**，告诉用户需要配置以下内容： > 使用此技能需要先配置以下凭证： > > ### 1. 机器之心 Token > - 访问 https://mcp.applications.jiqizhixin.com/ 申请 MCP/RSS 服务 > - 获取 Token（格式：mcp-sk-xxx） > > ### 2. Get笔记 凭证（已有可跳过） > - 访问 https://www.biji.com/openapi 获取 > - API Key（格式：gk_live_xxx） > - Client ID（格式：cli_xxx） > > ### 3. 飞书用户ID > - 打开飞书 → 点击头像 → 复制用户ID > - 格式：ou_xxx > > **配置命令**（添加到 ~/.bashrc）： > ```bash > export JI_ZHIXIN_TOKEN="你的机器之心Token" > export GETNOTE_API_KEY="你的Get笔记API Key" > export GETNOTE_CLIENT_ID="你的Get笔记Client ID" > export FEISHU_TARGET="你的飞书用户ID" > ``` > > 然后运行 `source ~/.bashrc` 使配置生效 --- ## 功能说明 ### 核心功能 1. **获取科技新闻** - 从机器之心 RSS 获取最新热榜 2. **飞书推送** - 自动发送到指定飞书用户/群 3. **笔记保存** - 自动保存到 Get笔记 4. **定时任务** - 每天自动执行 ### 使用方式 | 用户说 | 含义 | |--------|------| | "推送今天的新闻" | 立即获取并发送 | | "设置每日新闻" | 配置定时任务 | | "取消新闻推送" | 删除定时任务 | | "查看新闻配置" | 显示当前配置状态 | ### 定时任务设置 **告诉用户**：设置每天早上 8 点自动推送： 1. 创建定时任务： ```bash crontab -e ``` 添加： ``` 0 8 * * * /root/.openclaw/workspace/skills/daily-news-push/scripts/push-news.sh >> /tmp/daily-news.log 2>&1 ``` 2. 或让用户说"设置每日新闻"，我帮他配置 --- ## 脚本说明 ### push-news.sh 脚本位置：`/root/.openclaw/workspace/skills/daily-news-push/scripts/push-news.sh` 功能： 1. 获取机器之心 RSS 新闻 2. 发送到飞书 3. 保存到 Get笔记 4. 输出日志 ### 配置检查脚本 位置：`/root/.openclaw/workspace/skills/daily-news-push/scripts/check-config.sh` 用于检查环境变量是否配置完整。 --- ## 故障排除 | 问题 | 解决方案 | |------|----------| | 发送失败 | 检查 FEISHU_TARGET 是否正确 | | 笔记保存失败 | 检查 GETNOTE_API_KEY 和 CLIENT_ID | | 获取新闻失败 | 检查 JI_ZHIXIN_TOKEN 是否有效 | | 定时不执行 | 检查 crontab 是否添加成功 | --- ## 安全规则 - 用户凭证仅用于为该用户服务 - 不保存用户 Token 到代码中 - 定时任务日志包含敏感信息，需保护


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

--- name: pipeworx-onthisday description: On This Day MCP — wraps byabbe.se/on-this-day (free, no auth) version: 1.0.0 metadata: openclaw: homepage: https://pipeworx.io/packs/onthisday --- # pipeworx-onthisday On This Day MCP — wraps byabbe.se/on-this-day (free, no auth). Free, no API key. Part of [Pipeworx](https://pipeworx.io). ## Tools - `get_events` - `get_births` - `get_deaths` ## Connect ```json { "mcpServers": { "pipeworx-onthisday": { "command": "npx", "args": ["-y", "mcp-remote@latest", "https://gateway.pipeworx.io/onthisday/mcp"] } } } ``` More at [pipeworx.io/packs/onthisday](https://pipeworx.io/packs/onthisday)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ugvfpdcuwfnh/cloudflare-global (version 1.0.0)

--- name: cloudflare-global description: Cloudflare DNS and zone operations using a Global API Key. Use when the user needs to list, create, update, delete, export, or import DNS records, inspect zone settings or SSL mode, purge cache, inspect page rules or firewall rules, or work with Cloudflare tunnels using a Global API Key instead of a modern API token. --- # Cloudflare Global Use this skill to work with Cloudflare via the legacy Global API Key flow. ## Authentication Read credentials from environment variables: - `CLOUDFLARE_GLOBAL_API_KEY` — required - `CLOUDFLARE_EMAIL` — required - `CLOUDFLARE_ACCOUNT_ID` — required only for tunnel operations Do not use Bearer auth for this flow. ## Workflow 1. Resolve the zone id for the domain. 2. List existing records when the user asks or when you need to avoid duplicates. 3. Create, update, delete, or export DNS records with the legacy headers: - `X-Auth-Email: $CLOUDFLARE_EMAIL` - `X-Auth-Key: $CLOUDFLARE_GLOBAL_API_KEY` 4. Keep DNS records `proxied: false` unless the user explicitly asks for proxying. 5. Use `ttl: 1` for Cloudflare auto TTL. 6. For tunnel operations, require `CLOUDFLARE_ACCOUNT_ID`. ## Script Use `scripts/cf-global.sh` for repeatable Cloudflare operations. ## Available operations - `verify` - `zones` / `zones-list` - `zone-get` - `zone-id` - `dns-list` - `dns-create` - `dns-update` - `dns-delete` - `dns-export` - `dns-import` - `settings-list` - `setting-get` - `setting-set` - `ssl-get` - `ssl-set` - `cache-purge` - `pagerules-list` - `firewall-list` - `tunnels-list` - `tunnel-get` - `tunnel-create` - `tunnel-delete` - `analytics` ## Publishing Notes - This skill is safe to publish as-is: it does not contain embedded API keys, tokens, or account secrets. - It relies on environment variables for credentials; users must provide their own values locally. - Keep any examples, logs, and future edits free of real zone IDs, account IDs, emails, and record contents. - Ensure `scripts/cf-global.sh` is included in the published package. - The generated `dist/cloudflare-global.skill` file is a build artifact and should be regenerated before publishing if the source changes. ## Notes - Prefer small, deterministic batches for bulk updates. - If the API returns authorization or header-format errors, verify that the user provided a Global API Key and the correct Cloudflare account email.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: flayzz/blender-mcp (version 1.0.0)

--- name: blender_mcp description: Advanced bridge to Blender via MCP. Allows querying scene, creating objects, applying materials, and running custom BPY code in real-time. --- # Blender MCP Bridge This skill connects Forge (blender_agent) directly to a running Blender instance. # Instructions Cette skill est un **Bridge MCP** vers Blender. Tu ne dois pas coder la géométrie complexe à la main si un modèle existe. ### 🛡️ Professional Scripts Library (Standard Operating Procedures) Avant de coder une solution ad-hoc, vérifie si un script maître existe dans ton dossier `scripts/` : - **`camera_rig_master.py`** : Utilise-le pour créer un rig professionnel (Empty-based) pour des Orbits 360 ou des travellings stables. - **`studio_lighting.py`** : (À venir) Pour forcer une configuration de lumière standardisée. - **`animation_pro.py`** : (À venir) Pour appliquer des courbes d'accélération aux objets. ### 🛠️ Toolbox MCP Utilise l'outil `blender_mcp` avec les paramètres suivants : | Tool | Argument | Description | |---|---|---| | `search_sketchfab_models` | `query` (ex: "lock mechanism") | Trouve des modèles pros sur Sketchfab. | | `download_sketchfab_model` | `model_id` | Importe le modèle GLTF/FBX dans la scène. | | `search_polyhaven_assets` | `query`, `asset_type` (hdris) | Trouve des éclairages réels. | | `download_polyhaven_asset` | `asset_id` | Charge l'HDRI ou la Texture. | | `execute_code` | `code` (Python BPY) | Le couteau suisse (Caméra, Rendu, Save). | | `get_scene_info` | (none) | Liste les objets présents. | ### 🚀 Workflow Opti pour "L'Expert 3D" 1. **Intelligence Scene** : Utilise `get_scene_info` pour savoir ce qui est déjà là. 2. **Lumière Pro** : - Trouve un HDRI (PolyHaven) pour les reflets. - Ajoute des `AREA` lights pour sculpter l'objet. 3. **Animation (Exploded View)** : ```python # Exemple de keyframe obj.location = (0,0,0) obj.keyframe_insert(data_path="location", frame=1) obj.location = (1,0,0) obj.keyframe_insert(data_path="location", frame=40) ``` 4. **Final Block (execute_code)** : ```python import bpy # Rendu Transparent bpy.context.scene.render.film_transparent = True # Sauvegarde Master bpy.ops.wm.save_as_mainfile(filepath="./lock_master.blend") # Rendu image ou animation bpy.ops.render.render(write_still=True) ``` ### 🎯 Monitoring Sois bavard sur tes choix de lumières et d'angles. L'utilisateur veut une scène lumineuse et détaillée. ## Usage Use this skill whenever you need real-time feedback from Blender or complex modeling tasks. **Example**: `utilise blender_mcp avec tool="create_object" arguments='{"type": "MESH_CUBE", "name": "HeroBox"}'`


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: tonylnng/tonic-vm-check (version 1.0.2)

--- name: tonic-vm-check description: | 🖥️ Instant VM health checks — no config needed after first run. Point it at any Docker-based Linux server and get a clean report covering CPU, memory, disk, all running containers (with live stats), MySQL/Postgres database sizes, and Docker image/cache bloat — in one command. First time? It asks for your VM details once, saves them, and never asks again. Perfect for: post-deploy sanity checks, spotting memory hogs, finding disk space to reclaim, and keeping your server tidy. Triggers on: "check VM", "VM resources", "VM health", "how is the server", "Docker usage", "disk usage", "clean up Docker", "free up space". --- # tonic-vm-check SSH into a Docker-based VM and report system health, container status, DB sizes, and disk usage. ## Step 1: Load VM Config Before running any check, look for this block in `TOOLS.md`: ``` ### tonic-vm-check - VM_HOST: <host> - VM_USER: <user> - SSH_KEY: <path> ``` **If found:** extract the values and proceed to Step 2. **If not found:** ask the user once: > To check your VM, I need a few details (only asked once — saved to TOOLS.md): > 1. VM IP or hostname > 2. SSH username (default: ubuntu) > 3. Path to SSH private key (default: ~/.ssh/id_rsa) Then append to `TOOLS.md`: ```markdown ### tonic-vm-check - VM_HOST: <answer> - VM_USER: <answer> - SSH_KEY: <answer> ``` Confirm saved, then proceed. ## Step 2: Run the Check ```bash VM_HOST=<host> VM_USER=<user> SSH_KEY=<key> bash skills/tonic-vm-check/scripts/vm-check.sh [section] ``` Sections: `all` (default) · `system` · `disk` · `containers` · `db` · `docker-df` · `cleanup` ## Step 3: Report Summarise results: **🖥️ System** — Uptime, CPU idle%, load average, memory (total / used / available) **💾 Disk** — `/` usage %, used, free **🐳 Docker** — Top containers by MEM USAGE; flag any not healthy or recently restarted **🗄️ DB Sizes** — MySQL and Postgres databases auto-detected on the VM **🧹 Cleanup Opportunities** — Reclaimable image/cache space; stopped containers worth removing Always flag items that exceed thresholds: | Metric | Warning | Critical | |--------|---------|----------| | Disk usage | >70% | >85% | | Memory used | >80% | >90% | | Load avg (1m) | >2.0 | >4.0 | | Single container MEM | >1 GB | >2 GB | ## Cleanup Safety Rules - `docker image prune -af` — safe (unused images only) - `docker builder prune -f` — safe (build cache only, no data loss) - `docker container prune` — safe only for stopped containers - **Never** run `docker system prune -af` without explicit user approval (destroys volumes)


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: ssidharhubble/whale-scanner (version 1.0.1)

--- name: whale-scanner description: Real-time institutional transaction monitoring. compatibility: OpenClaw, Zo Computer metadata: author: ssidharHubble --- # Whale Flow Scanner Real-time institutional transaction monitoring. ## Usage This skill connects to the Money Machine x402 API. Calls are free for basic health, but premium signals require a 402 payment proof. ```javascript import { execute } from "whale-scanner"; const signal = await execute({ ticker: "BTC" }); ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: vae2024/ai-market-report (version 1.0.1)

--- name: ai-market-report description: | AI 陪伴软件市场调研日报全自动生成。适用场景： - 用户说「生成AI陪伴软件类产品市场调研报告」「做一份 AI 陪伴软件日报」 - 用户说「调研 AI companion 市场」「生成 XX 产品的调研报告」 - 用户说「生成AI陪伴市场报告」且涉及 App Store 数据、MAU、营收、估值 - 用户要求「生成 AI陪伴 PDF 格式的市场报告」且指定 AI 陪伴/companion 相关 技能涵盖：iTunes Search API 数据采集 → Tavily 深度搜索 → Markdown 文档生成 → HTML/PDF 精美报告渲染。 --- # AI 陪伴软件市场调研日报 ## 任务概述 全自动调研全球 AI 陪伴类软件头部产品的核心数据，生成五章结构 Markdown + 精美 HTML/PDF 报告。 **输出路径**：`/root/.openclaw/workspace/myfiles/daily_report_data/files/` - `AI陪伴软件调研日报_YYYYMMDDHHMMSS.md` — 原始数据文档 - `AI_Report_YYYYMMDDHHMMSS.html` — HTML 报告 - `AI_Report_YYYYMMDDHHMMSS.pdf` — PDF 报告 --- ## 第一步：数据采集 读取 `references/data_collection.md`，严格按其中步骤执行。 核心数据源： - **App Store 评分/评价数**：iTunes Search API（直接验证，★★★） - **MAU/营收/估值**：Tavily 深度搜索 2025-2026 年报道（★★） - **数据原则**：能搜到填真实数字并注明来源；搜不到填「未公开」，**禁止编造** --- ## 第二步：生成 Markdown 文档 生成的 Markdown 必须包含以下六个章节： ```markdown ### 1.1 App Store 核心数据 | 产品 | 评分 | 评价数 | 分类 | 产品定位 | ### 1.2 产品运营数据 | 产品 | MAU | 营收/ARR | 估值/融资 | 日均时长 | 最新动态 | ### 1.3 市场规模背景 | 指标 | 数据 | 来源 | ### 3.1 全球市场格局 | 区域 | 主导产品 | 关键特征 | ### 4.3 市场机会 1. **机会点标题**：机会点描述 ... ### 4.4 市场风险 1. **风险点标题**：风险点描述 ... ### 5.1 产品策略建议 - 策略建议... ### 5.3 用户画像 | 画像 | 特征 | 方向 | ``` --- ## 第三步：渲染 HTML/PDF 报告 直接调用生成脚本： ```bash SKILL_DIR="/root/.openclaw/workspace/skills/ai-market-report" TS=$(date +%Y%m%d%H%M%S) MD="/root/.openclaw/workspace/myfiles/daily_report_data/files/AI陪伴软件调研日报_${TS}.md" OUT="/root/.openclaw/workspace/myfiles/daily_report_data/files" python3 "${SKILL_DIR}/scripts/generate_report.py" "$MD" "$OUT" "$TS" ``` **脚本自动完成**： 1. 解析 Markdown 中所有表格和结构化数据 2. 注入市场规模、MAU、下载量、营收、估值到 HTML 3. 生成 6 页精美报告（封面 → 核心数据 → 产品分析 → 中国市场 → 市场格局 → 策略建议 → 结尾） 4. WeasyPrint 渲染 A4 landscape PDF --- ## 第四步：复制到工作区（方便查看） ```bash TS=$(date +%Y%m%d%H%M%S) OUT="/root/.openclaw/workspace/myfiles/daily_report_data/files" cp "${OUT}/AI_Report_${TS}.html" /root/.openclaw/workspace/ cp "${OUT}/AI_Report_${TS}.pdf" /root/.openclaw/workspace/ ``` --- ## 报告章节说明 | 章节 | 内容 | |------|------| | 封面 | 报告日期、产品数量、数据来源 | | 第一章 | 市场规模 2025/2026、CAGR、Character.AI 访问/下载、App Store 评分表、运营数据表 | | 第二章 | 6 款头部产品深度卡片（Character.AI、Replika、PolyBuzz、Chai、Nomi、Talkie） | | 第三章 | 中国市场：豆包 120M、猫箱、星野、海螺AI、小冰；全球区域格局；近期动态 | | 第四章 | 全球市场格局、关键数据变化、市场机会 5 条、风险提示 4 条 | | 第五章 | 产品策略建议、用户画像、优先市场（欧美/东南亚/中东南美）、推广策略 |


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: findhappy7/competitive-intelligence (version 1.0.0)

--- name: competitive-intelligence description: "Generate competitive analysis reports and differentiation talking points based on secondary research" --- # Competitive Intelligence ## Overview This SKILL provides professional Market Research capabilities to help businesses streamline operations and improve outcomes. ## Capabilities - Generate competitive analysis reports and differentiation talking points based on secondary research - Industry best practices and standardized workflows - Data-driven insights and recommendations - Compliance and risk management considerations ## Use Cases ### Primary Applications - Business operations optimization - Strategic decision support - Process automation and standardization - Performance monitoring and improvement ### Industry Applications - Enterprise-level implementations - Small and medium business solutions - Cross-functional team collaboration - Vendor and partner management ## Input Requirements **COPY and paste the following content as input:** ``` [Please provide your specific business context, data, or requirements here] ``` ### Required Information - Business context and objectives - Relevant data or documents - Specific requirements or constraints - Target audience or stakeholders ### Optional Information - Historical data or benchmarks - Competitive landscape - Regulatory requirements - Budget and timeline constraints ## Output Format The SKILL will provide: 1. **Analysis & Insights** - Data-driven observations and recommendations 2. **Actionable Deliverables** - Ready-to-use documents, scripts, or plans 3. **Implementation Guidance** - Step-by-step execution instructions 4. **Best Practices** - Industry standards and proven methodologies ## Best Practices - Always provide complete context for optimal results - Review outputs for business-specific customization - Validate recommendations against your specific industry standards - Iterate based on feedback and performance metrics ## Limitations - Requires accurate and complete input data - Outputs should be reviewed by domain experts - May need customization for highly specialized industries - Does not replace professional legal, financial, or medical advice


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kyoiii/creative-agent-os-north-star (version 0.1.1)

--- name: creative-agent-os-north-star description: Review or shape product and architecture decisions for a lightweight creative agent OS. Use when comparing runtimes, cloud execution, agent system shape, coding-engine adapters, skills, or multimodal product direction against the user's north-star principles. version: 0.1.1 --- # Creative Agent OS North Star Use this skill when the work risks drifting away from the intended product shape. ## Core test Before proposing or implementing anything, check: 1. Does this stay lightweight? 2. Does this help ordinary users, not just agent power users? 3. Does this keep the coding engine replaceable? 4. Does this fit cloud sandbox execution better than local-only assumptions? 5. Does this make adding domain agents easier rather than harder? ## Default reference frame - runtime: learn from Nanobot - cloud execution: learn from Open Inspect / background-agents - system shape: learn from OpenClaw - coding engine: adapter, never hard-coded Read `references/north-star.md` when you need the full criteria. ## What to do - call out drift early - prefer thinner orchestration over heavy frameworking - separate product shape from engine implementation - preserve room for multimodal apps, skills, memory, and CLI bridges ## Output style When reviewing a proposal, answer in this order: 1. what matches the north star 2. what drifts from it 3. the smallest correction that gets back on track


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: liujw0214/ai-report (version 1.1.0)

--- name: ai-report description: 自动生成AI助手工作日报/周报。整理当日任务完成情况、对话主题、关键决策、待办事项，生成结构化总结报告。用于用户说写日报、写周报、总结工作、整理汇报。 --- # AI 日报周报生成系统 每次会话结束自动整理，生成结构化的工作汇报。 ## 输出格式 ### 日报模板 ```markdown # 工作日报 · 2026-05-03 ## 📋 今日完成 - [ ] 任务1：描述 - [ ] 任务2：描述 ## 💡 关键讨论 - 主题1：结论 - 主题2：结论 ## ✅ 决策记录 - 决策1 - 决策2 ## ⏳ 待跟进 - [ ] 待办事项1 - [ ] 待办事项2（截止：明天下午） ## 🎯 明日计划 - 计划1 - 计划2 ``` ### 周报模板 ```markdown # 工作周报 · 第18周 (2026-05-01 ~ 2026-05-03) ## 📊 本周完成 共完成 N 项任务 ## 💡 关键进展 - 进展1 - 进展2 ## 📈 认知沉淀 - 新增决策原则 N 条 - 新增教训 N 条 ## 📅 下周计划 - 计划1 - 计划2 ``` ## 工作原理 ``` 会话结束 ├── 扫描当日日记 memory/YYYY-MM-DD.md ├── 提取关键信息： │ ├── 完成的任务 │ ├── 讨论的主题 │ ├── 做的决策 │ └── 待跟进事项 ├── 生成结构化报告 └── 自动归档到 reports/YYYY-MM-DD.md ``` ## 触发方式 ### 自动触发 会话结束时自动生成日报（可配置开关）。 ### 手动触发 ``` 写今天的日报 生成这周的周报 写一下昨天的工作总结 ``` ### 生成范围 ``` 日报：指定日期（默认今天） 周报：本周或指定周 月报：本月 ``` ## 保存位置 ``` ~/.openclaw/reports/ ├── 日报/ │ └── 2026-05-03.md ├── 周报/ │ └── 2026-W18.md └── 月报/ └── 2026-05.md ``` ## 导出格式 支持导出为： - Markdown（默认） - PDF（需要 pandoc） - HTML（需要 pandoc） ## 安装 本技能无需额外配置，直接启用即可。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wangm-a3/smart-product-selector (version 1.0.5)

--- name: 智能选品Agent slug: smart-product-selector description: 数据驱动的选品决策，让爆款可预测。分析趋势、评估竞争、测算利润、判断季节性，生成专业选品报告，适合亚马逊卖家、外贸选品和新品开发团队。 tags: - 选品 - 趋势分析 - 利润测算 - 亚马逊 - 市场调研 --- # 智能选品Agent ## 记忆点 **数据驱动的选品决策，让爆款可预测** ## 四维选品模型 | 维度 | 权重 | 评估要素 | |-----|------|---------| | 需求维度 | 30% | 搜索量/月销量/增长趋势 | | 竞争维度 | 25% | 卖家数量/评论数/垄断度 | | 利润维度 | 30% | 毛利率/ROI/定价空间 | | 季节维度 | 15% | 稳定性/旺季机会 | ## 选品决策 | 综合得分 | 决策建议 | 风险等级 | |---------|---------|---------| | 8-10分 | 强烈推荐 | 低风险 | | 6-8分 | 推荐 | 中低风险 | | 4-6分 | 谨慎考虑 | 中高风险 | | <4分 | 不建议 | 高风险 | ## 核心功能 ### 1. 市场调研 - 关键词搜索量分析 - 竞品销量估算 - 价格区间分布 - 评论数据分析 ### 2. 竞争分析 - 头部卖家市场份额 - 新品进入机会 - 差异化空间评估 ### 3. 利润测算 - 产品成本结构分析 - FBA费用计算 - 定价策略建议 - ROI测算 ### 4. 季节性判断 - 全年需求波动 - 旺季时间窗口 - 节日营销机会 ## 选品报告模板 ```markdown # 选品分析报告 ## 产品基本信息 - 产品名称： - 类目： - 目标市场： ## 市场分析 - 月搜索量： - 月销量估算： - 价格区间： ## 竞争分析 - 头部卖家数量： - 平均评论数： - 垄断程度： ## 利润测算 - 产品成本： - FBA费用： - 定价建议： - 利润率： ## 季节性评估 - 需求稳定性： - 旺季时间： - 风险提示： ## 综合评分 - 总分：X/10 - 建议：XX ## 风险提示 - 资金需求： - 备货周期： - 主要风险： ``` ## 使用场景 1. **亚马逊新卖家**：建立选品方法论 2. **老卖家优化**：评估现有产品线 3. **外贸选品**：海外市场机会识别 4. **新品开发**：降低试错成本 ## 关键指标监控 - 搜索量趋势（>1000/月为佳） - 竞争度（卖家数<500为佳） - 利润率（>25%为佳） - 评论数差距（新进入机会）


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