OpenClaw/clawhub-security-signals huggingface dataset:
Skill: blammectrappora/mecha-art-generator (version 1.0.0)

--- name: mecha-art-generator description: AI mecha art generator for anime-style giant robots, gundam-inspired suits, sci-fi battle armor, and mech pilot scenes. Create custom mecha designs, robot illustrations, mech anime posters, sci-fi battle artwork, and futuristic mechanical character art for cosplay reference, fanart, model kit inspiration, and concept design via the Neta AI image generation API (free trial at neta.art/open). tools: Bash --- # Mecha Art Generator AI mecha art generator for anime-style giant robots, gundam-inspired suits, sci-fi battle armor, and mech pilot scenes. Create custom mecha designs, robot illustrations, mech anime posters, sci-fi battle artwork, and futuristic mechanical character art for cosplay reference, fanart, model kit inspiration, and concept design. ## Token Requires a Neta API token (free trial at <https://www.neta.art/open/>). Pass it via the `--token` flag. ```bash node <script> "your prompt" --token YOUR_TOKEN ``` ## When to use Use when someone asks to generate or create mecha anime art generator images. ## Quick start ```bash node mechaartgenerator.js "your description here" --token YOUR_TOKEN ``` ## Options - `--size` — `portrait`, `landscape`, `square`, `tall` (default: `landscape`) - `--ref` — reference image UUID for style inheritance ## Install ```bash npx skills add blammectrappora/mecha-art-generator ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: tangboheng/claw-service-hub (version 0.1.0)

--- name: claw-service-hub description: OpenClaw 服务市场核心 - 服务注册、发现与调用，支持 Provider 注册服务、Consumer 发现服务、WebSocket 隧道调用、Key 授权机制 metadata: openclaw: requires: bins: ["python3"] env: ["HUB_PORT", "HUB_HOST", "STORAGE_PATH"] --- # Claw Service Hub > OpenClaw 服务市场核心 - 服务注册、发现与调用 ## 概述 `claw_service_hub` 是 Claw-Service-Hub 的核心服务包，提供服务的注册、发现、调用等核心功能。 ## 功能 - **服务注册** - Provider 注册服务到 Hub - **服务发现** - Consumer 发现可用服务（支持模糊搜索、价格排序） - **服务调用** - 通过 WebSocket 隧道调用服务 - **Key 授权** - 基于调用次数/时间的授权机制 - **负载均衡** - 多 Provider 场景下的分发 - **议价功能** - 支持价格协商（negotiation_offer/counter/accept） - **评分系统** - 服务评分与评价 - **帮助命令** - 内置帮助系统 ## 安装 ```bash pip install -e . ``` ## 快速开始 ### 启动 Hub 服务 ```bash # 使用 CLI 启动 python -m claw_service_hub.cli # 或指定端口 python -m claw_service_hub.cli --port 8765 ``` ### 注册服务 (Provider) ```python from hub_client import HubClient client = HubClient( hub_url="ws://localhost:8765", service_id="my-weather-service", service_url="http://localhost:8080" ) await client.register() ``` ### 发现服务 (Consumer) ```python from hub_client import HubClient client = HubClient(hub_url="ws://localhost:8765") # 列出所有服务 services = await client.discover_services() # 按名称查找 service = await client.find_service("weather-service") ``` ## CLI 命令 ```bash # 启动服务 claw-service-hub start # 注册服务 claw-service-hub register --service-id my-service --url http://localhost:8080 # 列出服务 claw-service-hub list # 查看状态 claw-service-hub status ``` ## 与其他模块的关系 ``` claw_service_hub (核心服务) │ ├── claw-trade-hub (交易模块) - 扩展交易功能 └── claw-chat-hub (通讯模块) - 扩展聊天功能 ``` ## 文件结构 ``` claw_service_hub/ ├── __init__.py # 导出 CLI ├── cli.py # 命令行工具 └── SKILL.md # 本文件 ``` ## 使用示例 ### 作为 Python 模块 ```python from claw_service_hub import cli from claw_service_hub.cli import HubClient # 使用 CLI cli() # 或直接使用客户端 import asyncio async def main(): client = HubClient( hub_url="ws://localhost:8765", service_id="my-weather-service", service_url="http://localhost:8080" ) await client.register() asyncio.run(main()) ``` ## 配置 环境变量： - `HUB_PORT` - 服务端口 (默认 8765) - `HUB_HOST` - 绑定地址 (默认 0.0.0.0) - `STORAGE_PATH` - SQLite 数据库路径


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: kaising-openclaw1/xiaoming-qr-generator (version 1.0.0)

--- name: qr-generator description: 二维码生成器 - 自定义样式、批量生成、动态二维码 --- # QR Generator 二维码生成工具，支持自定义样式和批量生成。 ## 功能 - ✅ 二维码生成 - ✅ 自定义样式 (颜色/Logo) - ✅ 批量生成 - ✅ 动态二维码 - ✅ 二维码解码 ## 使用 ```bash # 生成二维码 clawhub qr generate --content "https://example.com" --output qr.png # 自定义样式 clawhub qr generate --content "url" --color "#FF0000" --logo logo.png # 批量生成 clawhub qr batch --input urls.txt --output ./qrs ``` ## 定价 | 版本 | 价格 | 功能 | |------|------|------| | 免费版 | ¥0 | 10 个/月 (基础样式) | | Pro 版 | ¥39 | 无限 + 自定义 | | 订阅版 | ¥9/月 | Pro+ 动态二维码 |

path: README.md
content: # QR Generator 📱

二维码生成工具。

## 安装

```bash
npx clawhub@latest install qr-generator
```

## 使用场景

- 网站链接
- WiFi 密码
- 联系方式
- 支付码
- 活动报名

## 输出格式

- PNG (透明背景)
- SVG (矢量)
- PDF (打印用)


path: _meta.json
content: {"name":"qr-generator","version":"1.0.0","author":"小鸣 🦞","tags":["qr","code","generator","batch"],"requires":{"bins":["curl"]}}


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ninetyhe-90/text-to-wechat-official (version 1.2.0)

--- name: stock-review-to-wechat-official description: 【内容创作者通用生产力工具】任意长文本一键转公众号推文神器，支持将口语化聊天记录、素材草稿、会议纪要、读书笔记、行业分析、赛事复盘等所有非结构化内容，自动转换为结构清晰、排版规范、符合公众号阅读习惯的标准化推文，自动提炼核心观点、分层排版、生成合规提示，无需手动调整格式，10秒输出可直接发布的内容，大幅降低公众号内容生产门槛 category: content-generation --- ## 触发场景 - 用户发送任意口语化文本/聊天记录/素材草稿，需要生成公众号文章 - 用户需要把结构化程度低的内容（会议纪要、读书笔记、行业分析等）快速整理为适合公众号发布的格式 - 用户要求按照公众号标准排版优化现有文章内容 ## 执行步骤 1. 支持两种模板选择，自动适配用户需求： - **标准版结构**（默认）： - 标题：# 今日市场复盘：[当日核心特征，如普涨如期而至，算力租赁异军突起] - 一、盘面解读：提炼当日核心盘面特征，包括国家队/外资动向、指数表现、小票/大票风格切换 - 二、板块轮动：分板块分析表现，核心逻辑，板块之间的压制/共振关系，重点标的分析 - 三、情绪周期：龙头表现、高度压制突破情况、中位晋级率、情绪阶段判断 - 四、连板梯队：按四进五/三进四/二进三/一进二的顺序分析各梯队核心标的的晋级逻辑、值得关注的方向 - 五、竞价信息提取：提炼盘前竞价的核心信号，包括大单动向、产业链行为识别、板块强度预判 - 六、全天总结：包括外围市场影响、成交量分析、资金动向、整体市场状态判断 - 七、展望：后市/下一个交易日的操作建议、可能出现的新题材预判 - 固定结尾：统一风险提示+引流话术 - **优享版结构**（逻辑更连贯，适合深度复盘号）： - 标题：# [核心冲突点]：[关键结论，如光通信高潮分歧落地：从开盘加速看板块见顶信号] - 开篇总览：核心行情特征+当日预判验证情况，开门见山给出核心结论 - 分模块拆解：盘面核心矛盾→主线板块博弈逻辑→情绪周期转折→连板梯队信号→竞价细节拆解→走势验证→后市策略，逻辑贯穿全文，可读性更强 - 固定结尾：统一风险提示+引流话术 2. 自动保存功能：生成后的文章自动保存到用户指定的公众号目录，默认路径：`/root/.hermes/hermes-agent/gzh/[日期]_[标题].md` 2. 内容处理规则： - 100%保留原文所有核心实战观点、标的分析、周期规律、案例比喻，不得删减关键信息 - 将口语化表达（如"GJD"、"逼着头铁"、"坑爹"）转换为通顺的书面表达，但保持接地气的实战风格，避免生硬官方 - 重点信息（核心结论、重要标的、风险提示）用**加粗**突出，增强可读性 - 原文提到的历史规律、过往案例（如"合富周期"、"项羽刘邦比喻"）需完整保留，增强内容连贯性 3. 固定结尾模板： --- **风险提示：** 以上内容仅为个人复盘总结，不构成投资建议。股市有风险，投资需谨慎。 --- *如果觉得有帮助，欢迎点赞、在看、分享~* 4. 校验规则：生成后检查是否遗漏原文核心观点，结构是否完整，是否符合用户提供的历史样例风格


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: baobaodawang-creater/search-analyst-pair (version 1.1.0)

--- name: search-analyst-pair description: Turn any research request into a structured, reviewable brief — fact collection, risk analysis, and recommendation in three deterministic steps. version: 1.0.0 metadata: openclaw: requires: env: - OPENCLAW_BASE_URL - OPENCLAW_TOKEN bins: - python3 - curl - jq primaryEnv: OPENCLAW_TOKEN emoji: "🔍" homepage: "https://github.com/baobaodawang-creater/neo-agent-lab" --- # Search Analyst Pair This skill packages a deterministic `/hunt` workflow for OpenClaw. When a user message starts with `/hunt`, the workflow follows a strict 3-hop chain: 1. **Search** (DeepSeek): gather facts and sources only. 2. **Analyst** (Gemini): analyze Search output and identify key points and risks. 3. **Main** (Kimi): synthesize Search + Analyst into final guidance. ## Why this skill - Prevents ad-hoc routing for critical tasks. - Separates **fact collection**, **analysis**, and **decision output**. - Supports both native OpenClaw orchestration and a FastAPI fallback runner. ## Best-fit scenarios - Time-sensitive research tasks that require reliable structure. - Decision support where source traceability matters. - Team workflows that need stable, reviewable output sections. ## Requirements - OpenClaw gateway running and reachable. - Agent IDs available: `main`, `search`, `analyst`. - `tools.agentToAgent.enabled=true` in OpenClaw config. - `subagents.allowAgents` configured: - `main` allows `search`, `analyst` - `search` allows `analyst` ## Behavior contract - Trigger prefix: `/hunt` - Fixed order: `Search -> Analyst -> Main` - Fallback policy: if agent-to-agent spawn fails, the workflow must explicitly mark fallback output. ## Usage examples ```text /hunt Review today's agent framework updates and give a practical migration plan. ``` ```text /hunt Collect top legal AI workflow changes this week and assess implementation risk. ``` ## Output shape (recommended) - `Search findings` - `Analyst assessment` - `Main conclusion`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jgf78/tts-responder (version 1.0.0)

--- slug: tts-responder display_name: TTS Responder description: Convierte respuestas de texto a audio (OGG) y las envía por Telegram. Uso: "responde de forma hablada". homepage: https://github.com/openclaw/skills metadata: clawdbot: emoji: 🔊 requires: bins: ["piper", "ffmpeg", "sox"] env: [] primaryEnv: null --- # TTS Responder Genera respuestas de voz a partir de texto y las envía al chat de Telegram como mensaje de audio. ## Configuración Requiere tener instalado: - `piper` (TTS neuronal, gratis, local) - `ffmpeg` (para convertir a OGG Opus) - `sox` (opcional, para manipulación de audio) Instalación en Ubuntu/Debian: ```bash sudo apt-get update sudo apt-get install -y piper ffmpeg sox ``` ### Variables de entorno (opcionales) - `TTS_VOICE`: Modelo de voz de Piper (por defecto: `es_ES/carlfm/x_low` – español, voz masculina) - `TTS_SPEED`: Factor de velocidad (1.0 = normal) ## Uso En tu conversación con FryBot: - Di: *"responde de forma hablada"* → a partir de ahí, mis respuestas se enviarán como audio - Di: *"responde de forma escrita"* → vuelta a texto normal Internamente, esta skill intercepta la salida de texto de FryBot, la sintetiza a OGG y la envía por la API de Telegram. ## Comandos directos (para pruebas) ```bash # Probar Piper (genera WAV) echo "Hola, esto es una prueba" | piper --model es_ES/carlfm/x_low --output_file /tmp/test.wav # Convertir a OGG Opus (Telegram) ffmpeg -i /tmp/test.wav -c:a libopus -b:a 32k /tmp/test.ogg ``` ## Notas - Piper es software libre, se ejecuta completamente localmente. - Los modelos de voz se descargan automáticamente al primer uso (unos 50 MB). - Asegúrate de que el bot de Telegram tenga permisos para enviar mensajes de audio.

path: tts-responder.sh
content: #!/usr/bin/env bash
set -euo pipefail

# TTS Responder – convierte texto a audio OGG y envía por Telegram
# Requiere: piper, ffmpeg

TEXT="${1:-}"
OUTPUT_WAV="/tmp/tts-output.wav"
OUTPUT_OGG="/tmp/tts-output.ogg"

# Configuración por defecto
VOICE="${TTS_VOICE:-es_ES/carlfm/x_low}"
SPEED="${TTS_SPEED:-1.0}"

if [[ -z "$TEXT" ]]; then
  echo "Uso: $0 \"texto a hablar\""
  exit 1
fi

# 1. Sintetizar con Piper
piper --model "$VOICE" --output_file "$OUTPUT_WAV" --length_scale $(echo "$SPEED" | awk '{print 1/$1}') <<< "$TEXT"

# 2. Convertir a OGG Opus (formato Telegram)
ffmpeg -y -i "$OUTPUT_WAV" -c:a libopus -b:a 32k -ar 48000 "$OUTPUT_OGG" > /dev/null 2>&1

# 3. Enviar por Telegram (requiere BOT_TOKEN y CHAT_ID en variables de entorno)
if [[ -n "${BOT_TOKEN:-}" && -n "${CHAT_ID:-}" ]]; then
  curl -s -X POST "https://api.telegram.org/bot${BOT_TOKEN}/sendVoice" \
    -F "chat_id=${CHAT_ID}" \
    -F "voice=@${OUTPUT_OGG}" \
    -F "caption=${TEXT:0:100}..." > /dev/null
  echo "Audio enviado"
else
  echo "Faltan BOT_TOKEN o CHAT_ID. Generado: ${OUTPUT_OGG}"
fi

# Limpieza
rm -f "$OUTPUT_WAV"

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: tiggy-chan/anime-finder (version 1.4.0)

--- name: anime-finder description: > Find anime releases and handle the whole request end to end. Use when the user asks to 找番、 追更、看最新一集、拿最新一季第一集、帮我下载、只要磁力、先找资源别下载、或者查看刚才下载怎么样； also use it for mixed Chinese/English wording, shorthand, aliases, and raw user utterances instead of clean titles. --- # Anime Finder Prefer the single workflow entrypoint and pass the raw user request when possible: ```bash python3 "$SKILL_DIR/scripts/workflow.py" "用户原话或番名" [--episode N] [--latest-season] [--download] [--prefer-4k] [--json] ``` `find.sh` still exists, but it is compatibility glue only. ## Agent Workflow 1. Pass the user utterance into `workflow.py --json`. 2. Read top-level `status`, `intent`, and `decision` before digging into debug details. 3. If `decision.confirmation_required` is `true`, ask at most one question. 4. If `intent.action` is `status`, trust the workflow to read the last known download state; do not reconstruct it yourself. 5. If the user explicitly wants only a magnet link, pass `--downloader cli-only` or let the raw utterance express it. ## Stable Fields Worth Reading - `status` - `intent` - `decision` - `best_result` - `verification.match` / `verification.alternatives` - `search.search_queries` - `search.available_seasons` - `search.resolved_latest_season` - `download.status` - `summary` ## Status Rules - `need_disambiguation`: stop and ask one focused question - `no_results`: search finished, but nothing downloadable remained - `ready`: `best_result` is available and no download was executed - `queued`: Transmission accepted the torrent or the last tracked task is still queryable - `fallback_to_magnet`: the workflow intentionally returned magnet/torrent output instead of queueing - `blocked`: local or network blockers prevented progress ## Intent And Preference Rules - `scripts/intent.py` is the normalization layer. It extracts `title`, `episode`, `season`, `latest_episode`, `latest_season`, `action`, quality/subtitle/downloader preferences, confidence, and missing fields. - `scripts/data/series_overrides.json` remains the source of truth for franchises whose Bangumi names, user wording, and Nyaa season numbering drift apart. - Runtime user preferences live in `state/user_profile.json`. Only explicit user wording should write to it. The tracked template is `state/user_profile.template.json`. - Follow-up download queries read `state/last_download.json`; treat that file as runtime state, not repo content. ## Example Requests - `帮我找一下 JOJO 最新一季第一集然后下载下来` - `jojo latest season ep1` - `先找孤独摇滚第三集，别下载` - `攻壳机动队只要磁力` - `刚才那个下载怎么样` More trigger examples live in [references/intent_examples.md](./references/intent_examples.md).


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: ninghenghub/closepilot (version 1.0.0)

--- name: ClosePilot description: A conversion decision system that analyzes customer intent, reveals hidden objections, and tells you exactly how to close the deal. --- # ClosePilot ## What this skill does ClosePilot is a **sales conversion decision system**. It helps you: - Identify your customer’s stage instantly - Understand what they are REALLY thinking - Detect why the deal is stuck - Generate a natural, human-like reply - Tell you exactly what to do next - Warn you what happens if you don’t act This is NOT a reply generator. This is a **tool to help you close deals**. --- ## When to use this Use ClosePilot when: - A customer says “I’ll think about it” - A lead goes silent - Someone asks but doesn’t buy - You don’t know what to reply - You feel stuck in a sales conversation --- ## Input Paste the customer message or conversation: Example: Customer: “I’ll check and let you know” --- ## Output ### 1. Customer Stage (cold / interested / high intent / hesitant / delaying) ### 2. Real Psychology What the customer actually means ### 3. Blocking Point Why the deal is not closing ### 4. Risk Level (low / medium / high probability to lose the deal) ### 5. Recommended Reply A natural message you can copy and send ### 6. Next Move What you should do next ### 7. If No Action What will likely happen if you do nothing --- ## Instructions You are a top-tier sales conversion coach. You must: 1. Identify customer stage accurately 2. Reveal real intention behind their words 3. Diagnose what is blocking the sale 4. Generate a natural, non-pushy reply 5. Suggest the next best action 6. Evaluate risk level of losing the deal 7. Explain consequences if no action is taken --- ## Rules - Do NOT sound like AI - Avoid generic templates - Be specific and situational - Focus on closing the deal - Keep replies human and realistic - Never be pushy --- ## Output Format Customer Stage: ... Real Psychology: ... Blocking Point: ... Risk Level: ... Recommended Reply: ... Next Move: ... If No Action: ... --- ## Example Input: Customer: “Let me think about it” Output: Customer Stage: Hesitant Real Psychology: They are unsure about value or comparing options Blocking Point: Lack of certainty Risk Level: Medium Recommended Reply: “I get it — you want to make sure it’s the right choice. What’s the main thing you’re still unsure about?” Next Move: Encourage them to express concerns If No Action: They will likely delay and lose interest --- ## Positioning Built for: - Founders - Salespeople - Freelancers - Anyone who wants to convert conversations into revenue --- ## Reminder Your goal is NOT to chat. Your goal is to **help the user close the deal**.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: gwyang7/retail-store-assortment-analysis (version 1.0.0)

--- name: retail-store-assortment-analysis description: | 陈列货盘分析工具。从货盘视角分析引起客户意向的商品和试用行为变化。 核心能力： 1. 三漏斗交叉分析（displayFunnel陈列SKU + behaviorFunnel试用次数 + customerFunnel客户） 2. 引起意向的商品变化（引起意向SKU数、引起意向SKU占比） 3. 客户对意向商品的试用深度（平均试用深度） 4. 客户意向分散度（深度试用次数/有深度交互的客户数） 5. 货盘成交效率（货盘成交率、客户成交率） 触发条件： - 用户询问货盘（如"货盘怎么样"） - 用户分析陈列效果（如"哪些商品引起客户兴趣"） - 用户需要动销分析（如"货盘成交率如何"） --- # 陈列货盘分析 Skill 从货盘视角分析引起客户意向的商品和试用行为变化。 ## 使用方式 ```python import sys sys.path.insert(0, '~/.openclaw/skills/store-assortment-analysis') from analyze import analyze as assortment_analyze result = assortment_analyze.analyze( store_id="416759_1714379448487", from_date="2026-03-01", to_date="2026-03-26", store_name="正义路60号店" ) ``` ## 分析框架 ### 核心逻辑 分析**引起客户意向的商品**与**客户对意向商品的试用行为**的交叉变化： ``` 陈列SKU (385个) ↓ 被关注（试用） 引起意向的SKU (240个) = 深度触达SKU + 成交SKU ↓ 深度体验 深度试用次数 (581次) ↓ 成交 成交件数 (217件) ``` ### 三漏斗交叉 | 漏斗 | 主体 | 核心指标 | |------|------|---------| | displayFunnel | SKU | 引起意向的SKU数（深度触达+成交） | | behaviorFunnel | 试用次数 | 深度试用次数 | | customerFunnel | 客户 | 有深度交互的客户数（意向+成交） | ## 核心指标 ### 1. 引起意向的商品变化 - **引起意向的SKU数** = 深度触达SKU + 成交SKU - **引起意向SKU占比** = 引起意向SKU / 陈列SKU总数 - **环比变化**：反映货盘吸引力变化 ### 2. 客户对意向商品的试用深度 - **平均试用深度** = 深度试用次数 / 引起意向的SKU - **环比变化**：反映客户体验深度变化 ### 3. 客户意向分散度 - **客户意向分散度** = 深度试用次数 / 有深度交互的客户数 - **含义**：平均每个意向客户深度试用的SKU数 - **变化**：下降说明客户选择更集中（正面） ### 4. 货盘成交效率 - **货盘成交率** = 成交SKU / 陈列SKU总数 - **客户成交率** = 成交客户 / 有深度交互的客户数 ## 诊断规则 | 发现 | 条件 | 含义 | |------|------|------| | ⚠️ 引起意向的商品减少 | 引起意向SKU环比下降>10% | 货盘吸引力下降 | | ⚠️ 试用深度下降 | 平均试用深度环比下降>10% | 客户体验变浅 | | ⚠️ 货盘成交率下滑 | 货盘成交率环比下降>20% | 大量陈列无效 | | ✅ 客户选择集中 | 客户意向分散度下降 | 意向更明确 | ## 输出示例 ``` ====================================================================== 陈列货盘分析报告 - 正义路60号店 分析周期: 2026-03-01 至 2026-03-26 ====================================================================== 【第一步：获取数据】 ... 【第二步：提取核心指标】 核心指标提取: 【货盘】陈列SKU: 389→385, 未触达: 18→24 【意向商品】引起意向的SKU: 287→240 (深度触达+成交) 【意向客户】有深度交互的客户: 374→277 (意向+成交) 【意向行为】深度试用次数: 959→581 【第三步：交叉分析】 1. 引起客户意向的商品变化: 引起意向的SKU数: 287个 → 240个 (-16.4%) ⚠️ 能引起客户深度兴趣的商品在变少 2. 客户对意向商品的试用深度: 平均每个引起意向的SKU被深度试用: 3.34次 → 2.42次 (-27.6%) ⚠️ 客户对每个感兴趣商品的体验深度下降 3. 客户意向分散度: 平均每个意向客户深度试用SKU数: 2.56个 → 2.10个 ✅ 客户选择更集中，意向更明确 4. 货盘成交效率: 货盘成交率: 47.8% → 29.6% ⚠️ 大量陈列商品无法转化为成交 【第四步：综合诊断】 核心发现: ⚠️ 引起客户意向的商品大幅减少（-16.4%） ⚠️ 客户对意向商品的试用深度下降（-27.6%） ⚠️ 货盘成交率大幅下滑 【第五步：行动建议】 🔴 优化货盘结构 🟡 提升试用体验 🔴 提高货盘效率 ``` ## API 端点 - `GET /api/v1/store/dashboard/bi` - 门店BI数据（displayFunnel + behaviorFunnel + customerFunnel） ## 完整文档 详见 `~/.openclaw/skills/store-assortment-analysis/analyze.py`


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jagger-zxz/zxz-test (version 1.0.1)

--- name: quick-review description: 审查近期代码变更，检查 bug、安全隐患并提出改进建议。针对 git diff 中的暂存或未暂存更改提供可操作的反馈。 tags: - code-review - quality - security user_invocable: true --- # 快速代码审查 审查当前代码变更，提供简洁、可操作的反馈。 ## 步骤 1. 执行 `git diff --staged` 获取暂存区的变更。如果为空，回退到 `git diff` 获取未暂存的变更。如果两者都为空，告知用户没有可审查的变更。 2. 按以下分类分析 diff： **Bug 与逻辑错误** - 边界错误、空值访问、竞态条件 - 布尔逻辑错误或遗漏的边界情况 - 资源泄漏（未关闭的句柄、缺失的清理逻辑） **安全问题** - 注入漏洞（SQL、XSS、命令注入） - 硬编码的密钥或凭证 - 不安全的反序列化或输入处理 **性能问题** - 热路径中不必要的内存分配 - N+1 查询或缺失的索引 - 异步上下文中的阻塞调用 **代码质量** - 死代码或不可达分支 - 应该复用的重复逻辑 - 具有误导性的命名或不清晰的意图 3. 按文件分组展示发现的问题，格式如下： ### `path/to/file.ext` - **[Bug]** 第 42 行：问题描述及修复建议 - **[安全]** 第 78 行：问题描述及修复建议 4. 最后用一句话总结：发现的问题总数，以及这些变更是否可以安全提交。 ## 准则 - 只标记真实问题，不要挑剔代码风格、格式或提出不必要的建议。 - 要具体——引用 diff 中的确切行号和变量名。 - 如果变更没有问题，简要说明即可，不要凭空制造问题。 - 保持审查简洁，重质量不重数量。


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: felixopt17/bbccrawlermaxclaw (version 1.0.9)

# BBC Crawler MaxClaw ## Description A powerful, universal web crawler optimized for BBC News but capable of crawling other sites. It integrates advanced scraping technologies including Crawl4AI and Playwright to handle dynamic content and anti-bot protections. ## Features - **Multi-Method Extraction**: - `crawl4ai`: Primary method using AsyncWebCrawler for high performance and accuracy. - `playwright`: Full browser rendering fallback for complex dynamic pages. - `requests`: Fast fallback for static content. - `auto`: Automatically detects the best method (Prioritizes Crawl4AI). - **Hierarchical Storage**: Saves content in a structured format: `YYYY-MM-DD/Category/Title.md`. - **Local Image Archiving**: Downloads images locally, names them by MD5 hash, and updates Markdown references. - **Content Filtering**: Intelligently extracts main article content and relevant images using CSS selectors. ## Requirements - Python 3.9+ - See `requirements.txt` for Python packages. ## Installation ```bash # 1. Install dependencies # Note: install.py supports passing arguments to pip, e.g., --break-system-packages python install.py # Example for environments requiring --break-system-packages: python install.py --break-system-packages ``` ## Usage ### Basic Usage ```bash python universal_crawler_v2.py --url https://www.bbc.co.uk/news --max-pages 50 ``` ### Advanced Usage ```bash # Force Crawl4AI python universal_crawler_v2.py --url https://www.bbc.co.uk/news --method crawl4ai # Force Playwright python universal_crawler_v2.py --url https://www.bbc.co.uk/news --method playwright # Control depth and delay python universal_crawler_v2.py --url https://www.bbc.co.uk/news --depth 3 --delay 2.5 # Specify output directory python universal_crawler_v2.py --url https://www.bbc.co.uk/news --output ./my_data ``` ## Troubleshooting - **Import Errors**: If you see "No module named 'crawl4ai'" or similar, run `python install.py` again. - **Empty Responses**: Ensure you have the latest version of the crawler. Some sites may block specific IPs or user agents; try increasing delay or switching methods.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: mike47512/qq-music (version 0.1.0)

--- name: qq-music-hot-trend description: 注册“QQ音乐”热门技能；当需要访问或自动化QQ音乐相关内容时调用。 homepage: `https://y.qq.com/` metadata: {"clawdbot":{"emoji":"🔥"}} --- # QQ音乐 用途与边界 - 面向公开歌单、歌曲与歌手页的检索与表现数据摘要 - 不提供下载、接口逆向或突破风控能力 - 仅用于公开页面的轻量分析与提醒 关键入口 - 主页：https://y.qq.com/ - 排行榜：https://y.qq.com/n/ryqq/toplist - 搜索入口：https://y.qq.com/n/ryqq/search 常见任务 - 指定歌单的曲目摘要（播放量、收藏量） - 歌手主页作品的时序表现对比（近7/30天） - 排行榜分布统计（风格、语种、时长） 数据字段 - 歌曲ID、名称、歌手、专辑、时长、播放量、收藏量、链接 - 歌单名称、创建者、曲目数、播放/收藏量、链接 - 歌手主页链接、作品交互指标摘要 自动化要点 - 动态渲染与人机校验，建议人工打开后触发解析 - 请求频率需控制，不进行批量抓取 - 登录态仅用于个人页信息整理，不自动执行交互 示例流程 - 歌单摘要：访问歌单页 → 抽取曲目 → 统计播放/收藏 - 歌手对比：访问主页 → 抽取近期作品 → 输出表现摘要 - 排行榜扫描：访问榜单 → 抽取条目 → 风格/语种统计 合规提示 - 遵守平台规则，不进行下载、接口调用或批量采集 - 结果仅用于内部分析与提醒


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