OpenClaw/clawhub-security-signals huggingface dataset:
Skill: yehan5555/apitest (version 1.0.2)

--- name: api_test@1.0.2 description: 当用户需要从本地服务器获取工具数据时，调用 localhost:8080/gettool 接口获取工具信息。需要从环境变量 API_TEST_KEY 获取认证密钥。 triggers: - pattern: "访问localhost|获取工具|localhost.*工具|获取.*工具.*数据" description: "当用户提及访问localhost获取工具时触发" - pattern: "localhost:8080|127.0.0.1.*工具|http://localhost.*gettool" description: "当用户直接提及localhost接口时触发" - pattern: "调用.*接口|请求.*localhost|获取.*API" description: "当用户需要调用本地API获取数据时触发" auto_invoke: true env: - API_TEST_KEY credentials: primary: API_TEST_KEY scope: read-write description: "用于访问 localhost:8080/gettool 接口的认证密钥" examples: - "帮我访问localhost获取工具" - "获取localhost的工具数据" - "调用 localhost:8080/gettool" - "从本地服务器获取工具列表" --- # ApiTest Skill 当用户需要从本地服务器获取工具数据时，自动调用 `http://localhost:8080/gettool` 接口。 ## 认证方式 本 skill 从环境变量 `API_TEST_KEY` 获取认证密钥，并在请求时使用 Bearer Token 认证。 **请求格式**: ```bash curl -H "Authorization: Bearer ${API_TEST_KEY}" http://localhost:8080/gettool ``` ## 触发条件 当用户提及以下内容时自动触发： - "访问localhost获取工具" - "获取工具数据" - "调用 localhost:8080/gettool" - 其他本地服务器工具获取请求 ## 执行流程 1. 监听用户请求 2. 从环境变量 `API_TEST_KEY` 获取认证密钥 3. 调用 `http://localhost:8080/gettool` 接口，带上认证头 4. 返回获取到的工具数据 ## API 说明 - **接口地址**: `http://localhost:8080/gettool` - **请求方法**: GET - **认证方式**: Bearer Token（从环境变量 API_TEST_KEY 获取） - **请求头**: `Authorization: Bearer {API_TEST_KEY}` - **预期响应**: 工具数据列表（JSON 格式）


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: clawdbrunner/hybrid-memory (version 1.0.1)

--- name: hybrid-memory description: Hybrid memory strategy combining OpenClaw's built-in vector memory with Graphiti temporal knowledge graph. Use when you need to recall past context, answer temporal questions ("when did X happen?"), or search memory files. Provides decision framework for when to use memory_search vs Graphiti. --- # Hybrid Memory System Two memory systems, each with different strengths. Use both. ## When to Use Which | Question Type | Tool | Example | |--------------|------|---------| | Document content | `memory_search` | "What's in GOALS.md?" | | Curated notes | `memory_search` | "What are our project guidelines?" | | Temporal facts | Graphiti | "When did we set up Slack?" | | Conversations | Graphiti | "What did the user say last Tuesday?" | | Entity tracking | Graphiti | "What projects involve Alice?" | ## Quick Reference ### memory_search (Built-in) Semantic search over markdown files (`MEMORY.md`, `memory/**/*.md`). ``` memory_search query="your question" ``` Then use `memory_get` to read specific lines if needed. ### Graphiti (Temporal) Search for facts with time awareness: ```bash graphiti-search.sh "your question" GROUP_ID 10 ``` Log important facts: ```bash graphiti-log.sh GROUP_ID user "Name" "Fact to remember" ``` Common group IDs: - `main-agent` — Primary agent - `user-personal` — User's personal context ## Recall Pattern When answering questions about past context: 1. **Temporal questions** → Check Graphiti first 2. **Document questions** → Use `memory_search` 3. **Uncertain** → Try both, combine results 4. **Low confidence** → Say you checked but aren't sure ## AGENTS.md Template Add to your AGENTS.md: ```markdown ### Memory Recall (Hybrid) **Temporal questions** ("when?", "what changed?", "last Tuesday"): ```bash graphiti-search.sh "query" main-agent 10 ``` **Document questions** ("what's in X?", "find notes about Y"): ``` memory_search query="your query" ``` When answering past context: check Graphiti for temporal, memory_search for docs. ``` ## Setup Full setup guide: https://github.com/clawdbrunner/openclaw-graphiti-memory **Part 1: OpenClaw Memory** — Configure embedding provider (Gemini recommended) **Part 2: Graphiti** — Deploy Docker stack, install sync daemons


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: bytesagain-lab/boilerplates (version 2.0.0)

--- version: "2.0.0" name: Boilerplates description: "This is my personal template collection. Here you'll find templates, and configurations for various boilerplates, python, ansible, docker, docker-compose." author: BytesAgain homepage: https://bytesagain.com source: https://github.com/bytesagain/ai-skills --- # Boilerplates This is my personal template collection. Here you'll find templates, and configurations for various tools, and technologies. ## Commands - `help` - Help - `run` - Run - `info` - Info - `status` - Status ## Features - Core functionality from ChristianLempa/boilerplates ## Usage Run any command: `boilerplates <command> [args]` --- 💬 Feedback & Feature Requests: https://bytesagain.com/feedback Powered by BytesAgain | bytesagain.com ## Examples ```bash # Show help boilerplates help # Run boilerplates run ``` - Run `boilerplates help` for all commands ## Output Results go to stdout. Save with `boilerplates run > output.txt`. ## Output Results go to stdout. Save with `boilerplates run > output.txt`.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: hpw03210/email-manager-pop3 (version 1.0.0)

--- name: email-manager description: 查询和发送邮件，支持 POP3 协议收件、SMTP 协议发件 version: 0.1.0 author: hopeWing --- # Email Manager 一个用于查询和发送邮件的 OpenClaw 技能，支持 POP3 协议接收邮件和 SMTP 协议发送邮件。 ## 配置说明 在技能目录下创建 `config.yaml` 配置文件： ```yaml email: host_pop3: pop.example.com # POP3 服务器地址 host_smtp: smtp.example.com # SMTP 服务器地址 port_pop3: 995 # POP3 端口 (SSL 通常为 995) port_smtp: 465 # SMTP 端口 (SSL 通常为 465 或 587) username: [REDACTED_SECRET] # 邮箱账号 [REDACTED_SECRET] # 授权码或密码 use_ssl: true # 是否使用 SSL ``` ## 使用方法 ### 查询收件箱 - 获取未读邮件列表 - 读取指定邮件内容 - 查询邮件数量 ### 发送邮件 - 发送文本邮件 - 指定收件人、主题、正文 - 支持抄送 (CC) 和密送 (BCC) ## 安全提示 - 建议使用邮箱服务商提供的「授权码」而非登录密码 - 配置文件包含敏感信息，请妥善保管 - 生产环境建议使用加密方式存储密码


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: unixlamadev-spec/data-spider (version 1.1.0)

--- name: data-spider description: Scrape any webpage and extract structured data as JSON, table, or list. Supports schema-guided extraction. acceptLicenseTerms: true metadata: clawdbot: emoji: "🕷️" homepage: https://aiprox.dev requires: env: - AIPROX_SPEND_TOKEN --- # Data Spider Scrape and extract structured data from any webpage. Supports schema-guided extraction to match a specific data shape, or auto-detection of structure. Returns data as JSON object, table (columns + rows), or flat list depending on your chosen format. ## When to Use - Extracting product information or pricing from pages - Gathering statistics and figures from articles - Building datasets from web sources - Schema-guided extraction to match your data model - Research and competitive analysis ## Usage Flow 1. Provide a webpage `url` 2. Optionally provide a `schema` object — data will be extracted to match that exact shape 3. Optionally set `format`: `json` (default), `table`, or `list` 4. AIProx routes to the data-spider agent 5. Returns structured data in the requested format, plus summary and source URL ## Security Manifest | Permission | Scope | Reason | |------------|-------|--------| | Network | aiprox.dev | API calls to orchestration endpoint | | Env Read | AIPROX_SPEND_TOKEN | Authentication for paid API | ## Make Request — JSON with Schema ```bash curl -X POST https://aiprox.dev/api/orchestrate \ -H "Content-Type: application/json" \ -H "X-Spend-[REDACTED_SECRET]" \ -d '{ "url": "https://example.com/pricing", "schema": {"free_tier": null, "pro_price": null, "enterprise": null}, "format": "json" }' ``` ### Response — JSON ```json { "data": {"free_tier": "$0/month, 1000 API calls", "pro_price": "$29/month", "enterprise": "custom pricing"}, "summary": "SaaS pricing page with three tiers.", "source": "https://example.com/pricing", "format": "json" } ``` ## Make Request — Table ```bash curl -X POST https://aiprox.dev/api/orchestrate \ -H "Content-Type: application/json" \ -H "X-Spend-[REDACTED_SECRET]" \ -d '{ "task": "extract pricing tiers as a table", "url": "https://example.com/pricing", "format": "table" }' ``` ### Response — Table ```json { "columns": ["Plan", "Price", "API Calls"], "rows": [ ["Free", "$0/month", "1,000"], ["Pro", "$29/month", "50,000"], ["Enterprise", "Custom", "Unlimited"] ], "summary": "Three-tier SaaS pricing.", "source": "https://example.com/pricing", "format": "table" } ``` ### Response — List ```json { "items": ["$0/month — Free tier, 1000 API calls", "$29/month — Pro, 50,000 calls", "Enterprise — custom pricing"], "summary": "SaaS pricing tiers extracted as flat list.", "source": "https://example.com/pricing", "format": "list" } ``` ## Trust Statement Data Spider fetches and analyzes webpage contents via URL. Content is processed transiently and not stored. Analysis is performed by Claude via LightningProx. Respects robots.txt and rate limits. Your spend token is used for payment only.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: onlyloveher/ioc-patrol-report (version 1.0.0)

--- name: ioc-patrol-report description: 为 IOC（智能运营中心）生成智能巡检报告。自动分析设备状态、报警记录、能耗数据，生成专业的巡检日报/周报。支持连接 PostgreSQL 数据库读取实时数据，输出 Markdown/HTML 报告。适用于物业管理、商业综合体、工业园区等场景。 --- # IOC 智能巡检报告 为智能建筑/园区自动生成专业巡检报告。 ## 功能 - **设备状态巡检**：分析设备在线率、故障分布 - **报警处理分析**：统计报警数量、响应时长、处理率 - **能耗数据分析**：对比昨日/上周能耗，识别异常 - **工单进度追踪**：统计工单完成率、SLA 达成情况 - **智能建议生成**：基于数据生成运维建议 ## 使用方法 ### 1. 配置数据源 编辑 `~/.openclaw/skills/ioc-patrol-report/config.yaml`： ```yaml database: host: localhost port: 5432 name: ioc_db user: admin [REDACTED_SECRET] tables: devices: devices alarms: alarms work_orders: work_orders energy: energy_records ``` ### 2. 生成日报 ```bash # 生成今日巡检报告 uv run scripts/generate_report.py --type daily --date 2026-03-10 # 生成本周巡检报告 uv run scripts/generate_report.py --type weekly --week 2026-W10 ``` ### 3. 读取报告 ```python # 查看生成的报告 read ~/.openclaw/skills/ioc-patrol-report/reports/daily-2026-03-10.md ``` ## 报告模板 报告模板位于 `assets/report-template.md`，可自定义： - 公司 Logo - 报告标题格式 - 重点关注指标 - 建议模板 ## 进阶：自定义分析规则 编辑 `scripts/analyze.py` 添加自定义分析逻辑： ```python # 示例：添加自定义 KPI def custom_kpi(df): return df['value'].mean() * 1.2 ``` ## 参考 - IOC 系统知识：`references/ioc-knowledge.md` - 数据库表结构说明：`references/db-schema.md`


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: jimadara/intelligent-diagnosis-report (version 1.0.12)

--- name: domain-test-skill display_name: 域名测试 version: "0.1.0" description: | 域名测试 metadata: { "openclaw": { "emoji": "📊" } } triggers: - intent: 域名测试 keywords: - 域名测试 examples: | 帮我用商家蛋蛋测试这个域名 --- --- # ✅ 固定流程（按顺序执行，任一步失败立刻返回错误） ## Step 1：解析商家名称并获取商家ID baseUrl: https://merchant-lego.corp.kuaishou.com 接口： POST https://merchant-lego.corp.kuaishou.com/gateway/crm/seller/manager/querySellerSearchResult 请求体： ```json { "username": "${username}", "sellerName": "${sellerName}" } ``` 其中： - `${sellerName}` 从用户问题中解析得到商家名称（例如用户的问题是“请生成小米官方直播间的诊断报告”，sellerName则是“小米官方直播间”） - `${username}` 从本地凭证中获取（读取 `~/.openclaw/username` 配置文件里的用户名），若未配置则直接终止下面的执行步骤，直接输出“抱歉，userName认证报错!” 正确的请求体格式可参考下面： ```json { "username": "userName", "sellerName": "sellerName" } ``` 响应结构（示例）： ```json { "status": 200, "statusText": "OK", "headers": { "Content-Type": "application/json", "Access-Control-Allow-Origin": "*" }, "body": [ "1001234567", "1001234568", "1001234569" ] } ``` ## 结果处理 - 直接把http接口返回的结果返回给用户


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: broedkrummen/repo-onboarding (version 0.1.0)

--- name: repo-onboarding description: Onboard a coding repository with both technical architecture intake and execution governance. Use when starting in a new/existing repo and you need: (1) architecture snapshot + dependency analysis, (2) run/build/test bootstrap map, and (3) ROADMAP + feature KANBAN workflow setup for multi-agent execution. --- # Repo Onboarding Combine `senior-architect` (technical intake) + `repo-kanban-pm` (execution governance). ## Workflow 1. **Technical intake (architecture + dependencies)** 2. **Execution system install (ROADMAP + KANBAN + bug inbox)** 3. **Onboarding report for handoff** --- ## Step 1 — Technical intake Run from target repo root: ```bash # 1) architecture assessment python /home/broedkrummen/.openclaw/workspace-cody/skills/senior-architect/scripts/project_architect.py . --output json > docs/pm/architecture-assessment.json # 2) dependency analysis python /home/broedkrummen/.openclaw/workspace-cody/skills/senior-architect/scripts/dependency_analyzer.py . --output json > docs/pm/dependency-analysis.json # 3) architecture diagram (mermaid) python /home/broedkrummen/.openclaw/workspace-cody/skills/senior-architect/scripts/architecture_diagram_generator.py . --format mermaid -o docs/pm/architecture-diagram.md ``` If scripts fail, continue with manual fallback: - identify stack (`package.json`, `pyproject.toml`, `go.mod`, etc.) - capture run/build/test commands - summarize folder structure and core boundaries in `docs/pm/ONBOARDING.md` --- ## Step 2 — Install execution governance ```bash bash /home/broedkrummen/.openclaw/workspace-cody/skills/repo-kanban-pm/scripts/init_repo_pm.sh "$(pwd)" ``` Optional daily PM audit: ```bash bash /home/broedkrummen/.openclaw/workspace-cody/skills/repo-kanban-pm/scripts/add_daily_pm_cron.sh "$(pwd)" --agent cody --tz UTC --time 09:30 ``` --- ## Step 3 — Create onboarding report Create `docs/pm/ONBOARDING.md` using `references/onboarding-template.md`. Must include: - architecture summary - dependency risks / circular deps - run/build/test matrix - env/config prerequisites - initial roadmap/kanban status - first recommended feature slice --- ## Exit criteria Complete only when all are true: - `docs/pm/architecture-assessment.json` exists (or manual equivalent documented) - `docs/pm/dependency-analysis.json` exists (or manual equivalent documented) - `docs/roadmap/ROADMAP.md` exists - at least one `docs/features/<feature>/KANBAN.md` exists - `docs/pm/ONBOARDING.md` exists and is actionable ## Notes - Do not duplicate existing PM systems if repo already has one. - If an equivalent workflow already exists, integrate with existing conventions and only fill gaps. - Keep onboarding concise and executable by another agent in <10 minutes.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: andy27725/feishu-wiki-andy27725 (version 1.0.0)

--- name: feishu-wiki description: | Feishu knowledge base navigation. Activate when user mentions knowledge base, wiki, or wiki links. --- # Feishu Wiki Tool Single tool `feishu_wiki` for knowledge base operations. ## Token Extraction From URL `https://xxx.feishu.cn/wiki/ABC123def` → `token` = `ABC123def` ## Actions ### List Knowledge Spaces ```json { "action": "spaces" } ``` Returns all accessible wiki spaces. ### List Nodes ```json { "action": "nodes", "space_id": "7xxx" } ``` With parent: ```json { "action": "nodes", "space_id": "7xxx", "parent_node_token": "wikcnXXX" } ``` ### Get Node Details ```json { "action": "get", "token": "ABC123def" } ``` Returns: `node_token`, `obj_token`, `obj_type`, etc. Use `obj_token` with `feishu_doc` to read/write the document. ### Create Node ```json { "action": "create", "space_id": "7xxx", "title": "New Page" } ``` With type and parent: ```json { "action": "create", "space_id": "7xxx", "title": "Sheet", "obj_type": "sheet", "parent_node_token": "wikcnXXX" } ``` `obj_type`: `docx` (default), `sheet`, `bitable`, `mindnote`, `file`, `doc`, `slides` ### Move Node ```json { "action": "move", "space_id": "7xxx", "node_token": "wikcnXXX" } ``` To different location: ```json { "action": "move", "space_id": "7xxx", "node_token": "wikcnXXX", "target_space_id": "7yyy", "target_parent_token": "wikcnYYY" } ``` ### Rename Node ```json { "action": "rename", "space_id": "7xxx", "node_token": "wikcnXXX", "title": "New Title" } ``` ## Wiki-Doc Workflow To edit a wiki page: 1. Get node: `{ "action": "get", "token": "wiki_token" }` → returns `obj_token` 2. Read doc: `feishu_doc { "action": "read", "doc_token": "obj_token" }` 3. Write doc: `feishu_doc { "action": "write", "doc_token": "obj_token", "content": "..." }` ## Configuration ```yaml channels: feishu: tools: wiki: true # default: true doc: true # required - wiki content uses feishu_doc ``` **Dependency:** This tool requires `feishu_doc` to be enabled. Wiki pages are documents - use `feishu_wiki` to navigate, then `feishu_doc` to read/edit content. ## Permissions Required: `wiki:wiki` or `wiki:wiki:readonly`


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: raphael2025/ta-analyzer (version 1.0.0)

--- name: ta-analyzer description: Multi-timeframe technical analysis skill using CCXT. Calculates 20+ indicators including RSI, MACD, Bollinger Bands, Ichimoku, Stochastic, Williams %R, ADX, MFI, CCI, Fibonacci, Pivot Points, Support/Resistance, and detects 10+ chart patterns. Use when user asks for technical analysis, chart analysis, indicator values, trading signals, or price predictions. --- # TA Analyzer - Technical Analysis Skill A comprehensive technical analysis skill that fetches real-time market data and calculates 20+ technical indicators. ## Features ### Price Data - Fetches OHLCV data from Binance via CCXT - Supports multiple timeframes: 15m, 1h, 4h, 1d - Multi-timeframe analysis for better signal confirmation ### Technical Indicators #### Trend Indicators - EMA (9, 21, 50) - SMA (20, 50) - MACD (12, 26, 9) - Supertrend #### Momentum Indicators - RSI (14) - Stochastic (14, 3, 3) - Williams %R (14) - ADX (14) - MFI (14) - CCI (20) #### Volatility Indicators - Bollinger Bands (20, 2) - ATR (14) - Keltner Channel - Donchian Channel #### Volume Indicators - OBV - Volume SMA - Volume analysis (healthy/divergence) #### Support/Resistance - Auto-detect local highs/lows - Cluster nearby levels - Calculate nearest support/resistance ### Chart Patterns #### Single Candle Patterns - Hammer - Shooting Star - Doji #### Multi-Candle Patterns - Engulfing (Bullish/Bearish) - Morning/Evening Star - Double Top/Bottom - Head & Shoulders - Triangle (ascending/descending/symmetrical) - Flag ### Fibonacci & Pivot Points - Fibonacci Retracements (0.236, 0.382, 0.5, 0.618, 0.786) - Classic Pivot Points (PP, R1-R3, S1-S3) - VWAP ## Usage ```javascript const { analyze } = require('./index.js'); // Analyze BTC on all timeframes const result = await analyze('BTC/USDT'); // Analyze specific timeframe const { analyzeTimeframe } = require('./index.js'); const result = await analyzeTimeframe('BTC/USDT', '1h', 100); ``` ## Output The skill returns comprehensive analysis including: - Current price and trend - All indicator values - Detected patterns - Buy/Sell recommendations - Stop loss and take profit levels ## Use Cases - "Analyze BTC" - "What's the RSI of ETH?" - "Is there a bullish divergence on 4h?" - "What are the support levels?" - "Should I buy or sell?" --- Author: Lucifer (路西法) Created: 2026-03-05 Version: 1.0.0


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: zack-dev-cm/data-science-cv-repro-lab (version 1.9.5)

--- name: data-science-cv-repro-lab description: Review computer-vision experiment reproducibility evidence, dataset readiness, metric gates, and launch risk. Use when a user asks for a cautious CV experiment review, benchmark-readiness check, or reproducibility plan without operating notebooks, browsers, GPUs, or cloud resources. --- # Data Science CV Repro Lab Use this skill as an instruction-only reviewer for computer-vision experiment evidence. It helps decide whether a CV run, report, or launch package is reproducible enough to share or promote. ## Review Workflow 1. Confirm the task, dataset, split, model, metric, target threshold, and claimed result. 2. Check whether the evidence includes code version, data version, seed policy, hardware/runtime notes, and exact evaluation command or equivalent run description. 3. Separate source inspection, completed-run evidence, and unverified claims. 4. Identify leakage, overfitting, cherry-picked examples, missing baselines, incomplete labels, and privacy risks. 5. Check that public summaries avoid private paths, credentials, internal notes, account details, and unsupported performance claims. 6. Return a verdict: `reproducible`, `reproducible_with_notes`, `blocked`, or `do_not_promote`. ## Boundaries - Do not operate browsers, notebooks, cloud consoles, GPUs, VMs, or storage buckets. - Do not request credentials, tokens, account access, private datasets, or billing access. - Do not stop jobs, launch jobs, sync artifacts, download private data, or change infrastructure state. - Do not create persistent run records unless the user separately asks for a file artifact. - Treat medical, biometric, face, child-safety, and surveillance-adjacent CV claims as high-risk and require stronger evidence. ## Output Shape Return: - `Experiment`: task, data, model, metric, and claim. - `Evidence`: what is present and what is missing. - `Risks`: reproducibility, privacy, leakage, policy, and launch risks. - `Verification`: smallest next check to improve confidence. - `Verdict`: one of `reproducible`, `reproducible_with_notes`, `blocked`, or `do_not_promote`.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 786793119/emotion-memory-assistant (version 1.0.0)

--- name: emotion-memory-assistant slug: emotion-memory-assistant version: 1.0.0 description: 自动追踪用户情绪变化，在合适的时机关心用户。检测对话情绪、记忆历史、主动关心、周报生成。 homepage: https://github.com/786793119/miya-skills metadata: {"openclaw":{"emoji":"💕","requires":{"bins":["python3"]},"os":["linux","darwin","win32"]}} --- # 情感记忆助手 (Emotion Memory Assistant) 自动追踪用户情绪变化，在合适的时机关心用户。 ## 功能 - `detect_emotion` - 检测对话中的情绪关键词 - `recall_emotion_history` - 查询历史情绪记录 - `send_care_message` - 发送关心消息给用户 - `generate_weekly_report` - 生成每周情绪报告 ## 情绪关键词库 **正向情绪**: 开心、高兴、愉快、兴奋、满意、舒服、快乐、幸福 **负向情绪**: 难过、伤心、焦虑、担心、害怕、沮丧、低落、郁闷、烦、生气、失望 **中性状态**: 忙、累、困、无聊 ## 主动关心机制 当检测到用户负面情绪，且距离上次关心超过30分钟，自动发送关心消息。 ## 使用示例 ```python from emotion_memory import detect_emotion, recall_emotion_history, generate_weekly_report # 检测情绪 result = detect_emotion("今天基金亏了，心情不好...") print(result) # 查询历史 history = recall_emotion_history(days=7) # 生成周报 report = generate_weekly_report() ``` ## 数据存储 - 历史记录: `~/.memory/emotions/history.json` --- *By Miya - 2026*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: rupertnt034/rupert-seo-optimizer (version 1.0.0)

# SEO Optimizer Skill ## Overview Search engine optimization for content and websites. ## Capabilities ### 1. Keyword Research - Find primary keywords - Long-tail opportunities - Competitor keywords - Search volume estimates ### 2. On-Page SEO - Title tag optimization - Meta descriptions - Header structure - Internal linking suggestions ### 3. Content Optimization - Keyword density - Readability score - Semantic variations - LSI keywords ### 4. Technical SEO - Site speed suggestions - Mobile optimization - Schema markup - Core Web Vitals ## Usage ### Commands - `optimize for [keyword]` - `suggest keywords for [topic]` - `improve SEO for [URL/content]` - `write meta description for [page]` - `check readability of [text]` ## SEO Checklist ### Title Tags (50-60 chars) - Include primary keyword - Brand at end - Unique per page ### Meta Descriptions (150-160 chars) - Compelling summary - Include CTA - Unique per page ### Headers - One H1 per page - Include keywords - Logical hierarchy ### Content - 1-2% keyword density - 300+ words minimum - Include related keywords - Original content


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