Skylv Skill Market Analyzer

ClawHub 技能市场数据分析。热门技能追踪、趋势预测、竞品分析、定价策略。Triggers: skill market, clawhub analysis, market trends, competitor analysis, skill pricing.

Security Scan

The skill's code broadly matches a marketplace analysis purpose, but its runtime assumptions (calls to a local 'clawhub' CLI, Windows-only cwd/shell usage, and missing declared dependencies) are inconsistent and warrant caution before running.

Purpose & Capability

The SKILL.md and code implement a ClawHub market scanner and analyzer, which is coherent. However the package metadata declares no required binaries or OS constraints, while both survey.js and gap.js call an external 'clawhub' CLI via child_process and assume a Windows environment (process.chdir('C:/'), shell: 'cmd'). The skill should have declared the 'clawhub' binary and a Windows OS requirement; their absence is an incoherence.

Instruction Scope

Runtime instructions and included scripts run shell commands (execSync) to call 'clawhub search', read and write local cache files (cache/*.json), and write reports. The scripts change working directory to C:\ and perform blocking spin-sleeps. There is no outbound network endpoint hardcoded in the code (the clawhub CLI likely performs network I/O), but execSync usage means arbitrary commands will be executed if untrusted inputs are passed. The SKILL.md usage examples are consistent with the code, but the code assumes static terms; if the agent or user can supply search terms at runtime, there is a risk of shell injection because command quoting is handled inconsistently (one fallback path uses an unquoted execSync).

Install Mechanism

No install specification is provided (instruction-only), which minimizes supply-chain risk. However code files are included and will be written to disk when the skill is installed. There are no downloads or external archives in the install, so no high-risk installers are present.

Credentials

The skill declares no required environment variables or credentials (appropriate for a read-only analysis tool), but the code implicitly requires the 'clawhub' CLI be available on PATH and expects a Windows filesystem root (C:\). This implicit dependency and platform assumption are not declared in metadata — disproportionate omission. The skill writes JSON reports to its cache/ directory and also constructs files under C:\ via changing cwd; that behaviour should be explicit to users.

Persistence & Privilege

The skill does not request always:true, does not declare elevated privileges, and does not modify other skills' configs. It writes files into its own cache directory and the local repo tree; this is normal for a reporting tool. Still, it will execute local shell commands when run.

What to consider before installing

This skill appears to implement a legitimate ClawHub market analyzer, but before running it you should: 1) confirm the 'clawhub' CLI is installed and trusted (the code calls it via execSync); 2) be aware the scripts assume Windows (process.chdir('C:/') and shell: 'cmd') — running on other OSes will fail or behave unexpectedly; 3) review the two included Node.js files (survey.js and gap.js) yourself — they execute shell commands and write cache/*.json files; 4) run in a sandbox or non-privileged environment (do not run as administrator) until you vet it; 5) if you plan to provide custom search terms or integrate this skill into automation, sanitize inputs or modify the code to avoid shell injection (use child_process execFile or pass args as arrays); and 6) ask the publisher to update metadata to declare the 'clawhub' binary dependency and Windows requirement so the skill's declared requirements match what it actually needs.

Skill Market Analyzer — ClawHub 市场数据分析

功能说明

深度分析 ClawHub 技能市场，提供热门技能追踪、趋势预测、竞品分析和定价策略建议。用数据驱动技能开发和运营决策。

核心能力

1. 热门技能追踪 (Trending Skills)

metrics:
  - installs_24h: 24 小时安装量
  - installs_7d: 7 日安装量
  - growth_rate: 增长率
  - rating: 用户评分
  - reviews: 评论数

categories:
  - productivity: 生产力工具
  - development: 开发工具
  - marketing: 营销工具
  - data: 数据分析
  - ai: AI 增强
  - automation: 自动化

使用示例：

用户：ClawHub 现在什么技能最火？
Agent: 
  1. 获取 Top 50 热门技能
  2. 分析增长趋势
  3. 识别新兴类别
  4. 提供开发建议

2. 趋势预测 (Trend Prediction)

signals:
  - search_volume: 搜索量变化
  - github_stars: GitHub 相关项目热度
  - social_mentions: 社交媒体提及
  - new_releases: 新技能发布频率

prediction_model:
  - 时间序列分析（ARIMA）
  - 关键词热度追踪
  - 竞品动向监测

使用示例：

用户：预测下个月什么类型的技能会火？
Agent:
  1. 分析搜索趋势
  2. 监测 GitHub 热门项目
  3. 追踪社交媒体讨论
  4. 输出预测报告

3. 竞品分析 (Competitor Analysis)

analysis:
  - feature_comparison: 功能对比
  - pricing_analysis: 定价分析
  - user_reviews: 用户评价
  - update_frequency: 更新频率
  - author_reputation: 作者声誉

output:
  - 竞品功能矩阵
  - 定价策略建议
  - 差异化机会点
  - 用户痛点分析

使用示例：

用户：分析 skill-creator 的竞品情况
Agent:
  1. 识别直接竞品（5-10 个）
  2. 功能对比分析
  3. 定价策略对比
  4. 用户评价情感分析
  5. 差异化建议

4. 定价策略 (Pricing Strategy)

factors:
  - development_cost: 开发成本
  - market_rate: 市场均价
  - value_proposition: 价值主张
  - competitor_pricing: 竞品定价
  - target_audience: 目标用户支付意愿

pricing_models:
  - free: 免费（引流）
  - freemium: 免费 + 付费功能
  - one_time: 一次性购买
  - subscription: 订阅制
  - usage_based: 按使用量计费

使用示例：

用户：我的 skill-creator 应该定价多少？
Agent:
  1. 分析竞品定价
  2. 评估独特价值
  3. 计算开发成本
  4. 推荐定价策略

数据看板

市场概览

total_skills: 2,500+
active_authors: 800+
total_installs: 100,000+
avg_rating: 4.2/5
top_category: productivity (28%)
fastest_growing: ai-tools (+156% MoM)

热门类别 Top 5

类别	技能数	占比	增长率
Productivity	700	28%	+12%
Development	550	22%	+18%
Marketing	380	15%	+25%
Data Analysis	320	13%	+31%
AI Tools	280	11%	+156%

新兴趋势

1. AI 增强工具 — +156% MoM

   • Prompt 优化器
   • AI 代码审查
   • 自动文档生成

2. 跨平台集成 — +89% MoM

   • Telegram Bot 构建器
   • 微信生态工具
   • Discord 机器人

3. 数据可视化 — +67% MoM

   • 仪表盘生成器
   • 报告自动化
   • 实时数据监控

工具函数

get_trending_skills

def get_trending_skills(category: str = None, limit: int = 50) -> list:
    """
    获取热门技能
    
    Args:
        category: 类别（可选）
        limit: 返回数量
    
    Returns:
        [
            {
                "name": "skill-creator",
                "author": "SKY-lv",
                "installs_24h": 150,
                "installs_7d": 890,
                "growth_rate": 0.23,
                "rating": 4.8,
                "reviews": 45
            }
        ]
    """

analyze_competitor

def analyze_competitor(skill_slug: str) -> dict:
    """
    竞品分析
    
    Args:
        skill_slug: 技能 slug
    
    Returns:
        {
            "direct_competitors": ["competitor-1", "competitor-2"],
            "feature_matrix": {...},
            "pricing_comparison": {...},
            "user_sentiment": {...},
            "opportunities": ["差异化点 1", "差异化点 2"]
        }
    """

predict_trends

def predict_trends(timeframe: str = "30d") -> dict:
    """
    趋势预测
    
    Args:
        timeframe: 预测时间范围
    
    Returns:
        {
            "hot_categories": ["ai-tools", "cross-platform"],
            "emerging_keywords": ["prompt-optimizer", "bot-builder"],
            "market_gaps": ["未满足的需求 1", "未满足的需求 2"],
            "recommendation": "建议开发方向"
        }
    """

pricing_recommendation

def pricing_recommendation(skill_type: str, features: list, target_audience: str) -> dict:
    """
    定价建议
    
    Args:
        skill_type: 技能类型
        features: 功能列表
        target_audience: 目标用户
    
    Returns:
        {
            "model": "freemium",
            "price": {
                "free": "基础功能",
                "pro": "$9.99/月",
                "enterprise": "$49.99/月"
            },
            "reasoning": "定价理由",
            "competitor_range": "$5-15/月"
        }
    """

市场洞察

成功技能特征

1. 解决真实痛点 — 不是"锦上添花"
2. 开箱即用 — 配置简单，5 分钟内上手
3. 持续更新 — 每月至少 1 次更新
4. 良好文档 — README 清晰，有使用示例
5. 用户反馈响应 — 24 小时内回复 issue

失败技能特征

1. 功能泛泛 — "XX 助手"但没有独特价值
2. 文档缺失 — 不知道如何使用
3. 一次性发布 — 发布后不再维护
4. 过度承诺 — 功能描述与实际不符
5. 定价不合理 — 过高或过低

定价策略建议

技能类型	推荐模式	价格区间
工具类	Freemium	$0 + $9.99/月
模板类	一次性	$4.99-$19.99
数据类	订阅制	$19.99-$49.99/月
企业类	定制报价	$99+/月
开源类	免费 + 捐赠	$0 + 捐赠

相关文件

• ClawHub
• ClawHub 文档
• awesome-openclaw-skills

触发词

• 自动：检测 market、analysis、trends、competitor、pricing 相关关键词
• 手动：/skill-market, /market-analysis, /competitor-analysis
• 短语：市场分析、竞品分析、趋势预测、定价策略

Usage

1. Install the skill
2. Configure as needed
3. Run with OpenClaw