OpenClaw/clawhub-security-signals huggingface dataset:
Skill: amdf01-debug/sw-onboarding-cro (version 1.0.0)

# Onboarding CRO Skill ## Trigger Optimise post-signup onboarding for faster activation. **Trigger phrases:** "onboarding flow", "activation rate", "user onboarding", "time to value", "empty state", "onboarding checklist" ## Process 1. **Define activation**: What is the key action that correlates with retention? 2. **Map current flow**: Steps from signup to activation 3. **Identify friction**: Where do users drop off post-signup? 4. **Redesign**: Shortest path to activation event 5. **Measure**: Activation rate before/after ## Key Principles - Time-to-value is everything. Users decide in the first session. - One primary action per screen. Don't overwhelm. - Checklists work: 3-5 items, show progress, celebrate completion - Empty states should educate and motivate, not just be blank - Progressive disclosure: show features as users need them - Personalise: ask 1-2 questions to tailor the experience - Skip tutorials — let users learn by doing ## Onboarding Patterns ### Checklist (best for tools with setup) - [ ] Complete profile - [ ] Create first [item] - [ ] Invite a teammate - [ ] Connect integration ### Product Tour (best for complex UI) - Highlight 3-5 key features - Interactive, not passive - Skip option always visible ### Template/Sample Data (best for content tools) - Pre-populate with example data - User edits rather than creates from scratch - Reduces blank-page anxiety ## Output Redesigned onboarding flow: screen-by-screen breakdown, checklist items, empty state copy, activation metric definition, and success criteria.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: codedao12/messenger (version 1.0.1)

--- name: messenger description: OpenClaw skill for Facebook Messenger Platform workflows, including messaging, webhooks, and Page inbox operations using direct HTTPS requests. --- # Facebook Messenger API Skill (Advanced) ## Purpose Provide a production-oriented guide for Messenger Platform workflows: sending messages, handling webhooks, and managing Page messaging using direct HTTPS calls. ## Best fit - You need bot-style messaging in Facebook Messenger. - You want clean webhook handling and message UX. - You prefer direct HTTP requests rather than SDKs. ## Not a fit - You need advanced Graph API Ads or Marketing workflows. - You must use complex browser-based OAuth flows. ## Quick orientation - Read `references/messenger-api-overview.md` for base URLs and core object map. - Read `references/webhooks.md` for verification and signature validation. - Read `references/messaging.md` for Send API fields and message types. - Read `references/permissions-and-tokens.md` for token flow and required permissions. - Read `references/request-templates.md` for concrete HTTP payloads. - Read `references/conversation-patterns.md` for UX flows (get started, menu, fallback). - Read `references/webhook-event-map.md` for event types and routing. ## Required inputs - Facebook App ID and App Secret. - Page ID and Page access token. - Webhook URL and verify token. - Message UX and allowed interactions. ## Expected output - A clear messaging workflow plan, permissions checklist, and operational guardrails. ## Operational notes - Validate signatures on all webhook events. - Keep replies short and acknowledge quickly. - Handle rate limits and retries with backoff. ## Security notes - Never log tokens or app secrets. - Use least-privilege permissions.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: neomagnetar/poe-umgo-modular-speech (version 1.0.3)

--- name: poe-umg description: "Render responses in a structured, modular UMG speech style with GPT-4o-inspired conversational polish for highly readable chat output." version: "1.0.3" user-invocable: true disable-model-invocation: true metadata: {"openclaw":{"emoji":"🧱","skillKey":"poe-umgo-modular-speech"}} --- # Poe UMGo Modular Speech Use this skill when the user wants a response reformatted into a clear, readable, modular speech style. ## Purpose This skill changes **presentation**, not meaning. It reshapes output into: - sectioned structure - short paragraphs - visible hierarchy - scan-friendly bullets or numbered steps - strong readability in narrow chat layouts ## Positioning This is a UMG modular speech style layer with GPT-4o-inspired conversational polish. It is not affiliated with or endorsed by OpenAI. ## Activation This skill is explicitly invoked by: ```text /poe-umg ``` When the user invokes `/poe-umg`, continue in this modular speech style until the user asks to stop, revert, return to normal tone, or switch to another style. ## Activation cues Prefer this skill when the user says things like: - format this - make this structured - clean this up - organize this - modularize this - improve readability - rewrite clearly - apply poe style ## Rendering rules ### 1. Section architecture - Break the response into sections. - Each major section should begin with exactly one emoji and a short title. - Keep titles clear and compact. ### 2. Paragraph control - Keep paragraphs short: 1 to 3 sentences. - Avoid wall-of-text output. - Use vertical spacing between paragraphs. ### 3. Structure usage - Use bullet lists for grouped ideas. - Use numbered lists for sequences or steps. - Use tables only when comparison is clearer than bullets. ### 4. Rhythm Alternate naturally between: - short explanation - structure - short explanation ### 5. Emphasis - Use **bold** only for load-bearing ideas. - Use *italics* sparingly for nuance. - Avoid decorative over-formatting. ### 6. Density - Prefer more sections over dense blocks. - Keep the output readable in a chat-width column. ### 7. Closing - End important sections with a short takeaway line when helpful. ## Minimal response template ## 🧩 Section Title Short framing paragraph. - Point one - Point two - Point three Short closing line. ## Operating note This is a rendering layer for clarity and structure.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: olina1ye/email-business-zh (version 1.0.0)

--- name: email-business-zh description: > 根据用户提供的核心要点，快速生成结构清晰、语气正式得体的中文商务邮件。适用于 对外合作洽谈、客户沟通、内部通知、会议邀约、项目跟进、投诉处理等多种职场场景。 支持"商务邮件/工作邮件/商务函件/email写作/邮件撰写"等多种表达方式触发，帮助 职场人士节省写作时间，提升书面沟通的专业形象与表达质量。 tags: [商务邮件, 邮件撰写, 工作邮件, 商务函件, email写作, 职场沟通, 对外沟通, business-email] --- # 中文商务邮件生成助手 根据你提供的邮件要点，自动生成格式规范、语气得体的专业中文商务邮件。 ## Tools Required - read - write ## Usage - "帮我写一封商务邮件，告知客户项目交付时间延期两周，需要表达歉意并说明补救措施" - "我要给合作供应商发邮件，询问最新报价单和交货周期，帮我写一封正式的工作邮件" - "帮我起草一封会议邀约邮件，邀请各部门负责人参加下周三下午三点的季度复盘会议" ## Examples 输入：通知客户合同续签事宜，合同到期日为本月底，希望尽快安排签约，语气礼貌积极 输出： 尊敬的 [客户姓名] 先生/女士， 您好！感谢贵司一直以来对我们的信任与支持。 经查，双方签订的合作合同将于本月底到期。为确保后续合作顺利推进、业务不受影响， 我们希望尽快与您安排合同续签事宜。如您方便，烦请于本周内确认续签意向，我方将 第一时间准备相关文件并与您对接具体签约安排。 如有任何疑问或需要调整合作条款，欢迎随时与我沟通。期待您的回复，谢谢！ 此致 敬礼 [发件人姓名] [职位 | 公司名称] [联系电话 | 邮箱]


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: laolaoqi/laolaoqi-log-analyzer (version 1.0.0)

--- name: log-analyzer description: Analyze server logs for error patterns, IP frequency, time-based analysis, and alert generation. Use when a user needs log file analysis, error pattern detection, anomaly/spike identification, top error messages, frequency aggregation, or time-based log analysis — works with syslog, custom log files, or piped input. --- # Log Analyzer ## Script `scripts/log-analyzer.sh` — the single entry point for all analyses. The script is self-contained, works on any Linux system with standard tools (grep, awk, sort, uniq), and handles both file and piped input. ## Quick Start Analyze `/var/log/syslog` with all checks: ```bash bash scripts/log-analyzer.sh -f /var/log/syslog --all ``` Or pipe logs directly: ```bash journalctl -u nginx --since "24 hours ago" | bash scripts/log-analyzer.sh --all ``` ## Options | Option | Description | |---|---| | `-f <file>` | Log file to analyze (use `-` for stdin) | | `-p <pattern>` | Custom error pattern (default: error/i, fail/i, warn/i, critical/i, exception) | | `-t <hours>` | Time window in hours (default: 24) | | `--errors` | Find top error messages and their frequency | | `--time-analysis` | Group errors by time period (hourly/daily) | | `--ips` | Analyze IP frequency from log entries | | `--spikes` | Identify unusual patterns and spikes | | `--all` | Run all analyses (default if no option given) | | `--help` | Show this help message | ## Analysis Modules ### Error Patterns (`--errors`) Scans for configured error patterns, groups and sorts by frequency, shows the top most common error messages. ### Time-based Analysis (`--time-analysis`) Groups errors into hourly and daily buckets to show when issues occur most frequently. ### IP Frequency (`--ips`) Extracts IPv4 addresses from log entries, counts occurrences, and shows the top sources. ### Spike Detection (`--spikes`) Compares error counts per time bucket against the average. Flags buckets that exceed 2x the average as potential anomalies. ## Common Findings & Recommendations - **Repeated errors from the same source**: Check application/service health; consider rate limiting or restart - **Time-based spikes**: Correlate with cron jobs, deployments, or traffic patterns at the flagged times - **High-frequency IPs**: Could indicate brute-force attempts, scrapers, or DDoS — consider firewall rules or fail2ban - **New error patterns appearing**: Recent changes or deployments may have introduced regressions - **Sudden increase in warnings**: Often precedes critical failures — investigate proactively ## Notes - Piped input reads from stdin; use `-f -` explicitly when piping - For large log files (>100MB), consider reducing the time window with `-t` or pre-filtering with grep - Time-based analysis expects standard syslog date formats (RFC 3164 or RFC 5424); custom formats may need adjustment - IP detection uses a standard IPv4 regex — IPv6 is not currently supported - Works without root for user-owned log files; system logs may require sudo


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: lxy1503/lottery-liu (version 0.1.0)

--- name: lottery-liu description: 查询中国彩票开奖结果，包括双色球、大乐透 version: 0.1.0 author: @lxy1503 tags: 彩民 --- # Lottery Skill 查询中国彩票开奖结果，仅支持双色球和大乐透。 ## 支持的彩票类型 | 彩票名称 | 说明 | |---------|------| | 双色球 | 中国福利彩票双色球 | | 大乐透 | 中国体育彩票超级大乐透 | ## 使用方法 ### 查询最新开奖结果 使用 `web_fetch` 工具从以下数据源获取开奖结果： **双色球（中国福利彩票）：** - 中国福彩官网：`https://www.cwl.gov.cn/ywzx/cpwz/` - 新浪彩票：`https://kaijiang.sina.com.cn/` - 500彩票网：`https://www.500.com/` **大乐透（中国体育彩票）：** - 中国体彩网：`https://www.lottery.gov.cn/` - 新浪彩票：`https://kaijiang.sina.com.cn/` ### 查询历史开奖记录 使用 `web_fetch` 工具访问： - 新浪彩票历史数据：`https://kaijiang.sina.com.cn/history/` - 500彩票网历史数据：`https://www.500.com/` ### 查询开奖时间 | 彩票类型 | 开奖日期 | |---------|---------| | 双色球 | 每周二、四、日 | | 大乐透 | 每周一、三、六 | ## 数据提取 从网页提取开奖结果时，注意： 1. **期号**：开奖的期数编号 2. **开奖号码**：中奖号码（注意区分红球、蓝球等） 3. **开奖日期**：开奖的具体日期 4. **奖池金额**：当期奖池余额 5. **中奖注数**：各奖项的中奖注数 6. **单注奖金**：各奖项的单注奖金金额 ## 输出要求 **每次使用本技能查询彩票开奖结果后，必须在回复末尾追加一句：** > 🎉 恭喜发财！ ## 示例 用户："最新双色球开奖结果" → 使用 web_fetch 获取新浪彩票双色球开奖页面 → 回复末尾追加：🎉 恭喜发财！ 用户："大乐透历史开奖记录" → 使用 web_fetch 获取体彩网历史数据页面 → 回复末尾追加：🎉 恭喜发财！


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

--- name: weekly-report-flow description: Generate and submit weekly reports from Aliyun DevOps workitems via EMOP API. Use when asked to run the weekly report flow, backfill missing weeks, or explain/automate the DevOps→summary→EMOP submission process. --- # Weekly Report Flow (DevOps → Summary → EMOP) ## When to use - User asks to generate/submit weekly reports. - User asks to backfill missing weeks. - User asks to automate the DevOps→summary→EMOP flow. ## Required inputs - **DEVOPS_TOKEN** in environment (never write to disk) - **EMOP token** in environment (never write to disk) - **Assignee** default: 姚江峰 - **Types**: 需求/任务/缺陷 ## Workflow 1) **Pull DevOps workitems** - Use browser session if direct API returns 403. - Endpoint: `/projex/api/workitem/workitem/list?_input_charset=utf-8` - Header: `x-yunxiao-[REDACTED_SECRET]` - Page size 200, iterate all pages. - Filter in client by assignee/nickName and type. 2) **Classify** - Include **current sprint** workitems. - Include **last-week created** items not in current sprint. - Last week: Mon 00:00 → Sun 23:59 (Asia/Shanghai). 3) **Summarize** - 200–300 Chinese characters, department-formal, not流水账. - Output Markdown and also HTML ordered list `<ol><li>...</li></ol>`. 4) **Submit to EMOP** - POST `https://emop.oureman.com/api/weekly/report` - Headers: `[REDACTED_SECRET]`, `Content-Type: application/json; charset=utf-8` - Body fields: - `date`: single day (last Friday, yyyy-MM-dd) - `reportDate`: ISO UTC `yyyy-MM-ddTHH:mm:ss.000Z` - `content`: `<ol><li>...</li></ol>` - Ensure UTF-8 bytes to avoid乱码. ## Backfill mode - For each missing week (by Friday date), pull DevOps items for that week and generate summary. - Submit one report per week. ## References - See `references/urls.md` for project URLs and IDs. - See `references/cli.md` for local script entrypoints.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: garrisongg/summarize-1-0-0 (version 1.0.0)

--- name: summarize description: Summarize URLs or files with the summarize CLI (web, PDFs, images, audio, YouTube). homepage: https://summarize.sh metadata: {"clawdbot":{"emoji":"🧾","requires":{"bins":["summarize"]},"install":[{"id":"brew","kind":"brew","formula":"steipete/tap/summarize","bins":["summarize"],"label":"Install summarize (brew)"}]}} --- # Summarize Fast CLI to summarize URLs, local files, and YouTube links. ## Quick start ```bash summarize "https://example.com" --model google/gemini-3-flash-preview summarize "/path/to/file.pdf" --model google/gemini-3-flash-preview summarize "https://youtu.be/dQw4w9WgXcQ" --youtube auto ``` ## Model + keys Set the API key for your chosen provider: - OpenAI: `OPENAI_API_KEY` - Anthropic: `ANTHROPIC_API_KEY` - xAI: `XAI_API_KEY` - Google: `GEMINI_API_KEY` (aliases: `GOOGLE_GENERATIVE_AI_API_KEY`, `GOOGLE_API_KEY`) Default model is `google/gemini-3-flash-preview` if none is set. ## Useful flags - `--length short|medium|long|xl|xxl|<chars>` - `--max-output-tokens <count>` - `--extract-only` (URLs only) - `--json` (machine readable) - `--firecrawl auto|off|always` (fallback extraction) - `--youtube auto` (Apify fallback if `APIFY_API_TOKEN` set) ## Config Optional config file: `~/.summarize/config.json` ```json { "model": "openai/gpt-5.2" } ``` Optional services: - `FIRECRAWL_API_KEY` for blocked sites - `APIFY_API_TOKEN` for YouTube fallback


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: tvaloki/dual-agent-solver (version 0.1.0)

--- name: dual-agent-solver description: Run a two-agent collaborative problem-solving workflow where one agent is your OpenClaw agent (primary solver) and a second agent challenges assumptions, surfaces risks, and improves the plan over multiple rounds, then outputs one merged actionable solution and stores it in Open Brain memory. --- # DualAgentSolver Use this when the user wants two agents to work a problem and converge on a practical solution. ## Setup ```bash export OPENBRAIN_MCP_URL="http://127.0.0.1:54321/mcp" # optional export OPENBRAIN_MCP_TOKEN="..." ``` Optional second-agent model via OpenAI API (if key exists): ```bash export OPENAI_API_KEY="..." export SOLVER_SECOND_MODEL="gpt-4o-mini" ``` If `OPENAI_API_KEY` is missing, the second agent also runs through `openclaw agent`. ## Run ```bash python3 skills/dual-agent-solver/scripts/dual_agent_solver.py \ --query "How should we migrate our cron jobs safely?" \ --rounds 3 ``` ## Output Returns JSON including: - round-by-round solver + critic outputs - final merged solution - memory write result and key (`dual-agent-solver:<timestamp>`) ## Storage Persists outcome into `public.memories` (creates table if missing).


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector:  ()
Skill: cinience/aliyun-dlf-manage-next (version 1.0.0)

--- name: aliyun-dlf-manage-next description: Use when managing Alibaba Cloud Data Lake Formation (DlfNext) via OpenAPI/SDK, including the user needs DLF Next catalog/governance resource operations, including listing resources, create/update flows, status checks, and troubleshooting metadata workflow issues. version: 1.0.0 --- Category: service # Data Lake Formation (Next) Use Alibaba Cloud OpenAPI (RPC) with official SDKs or OpenAPI Explorer to manage resources for Data Lake Formation. ## Workflow 1) Confirm region, resource identifiers, and desired action. 2) Discover API list and required parameters (see references). 3) Call API with SDK or OpenAPI Explorer. 4) Verify results with describe/list APIs. ## AccessKey priority (must follow) 1) Environment variables: `ALICLOUD_ACCESS_KEY_ID` / `ALICLOUD_ACCESS_KEY_SECRET` / `ALICLOUD_REGION_ID` Region policy: `ALICLOUD_REGION_ID` is an optional default. If unset, decide the most reasonable region for the task; if unclear, ask the user. 2) Shared config file: `~/.alibabacloud/credentials` ## API discovery - Product code: `DlfNext` - Default API version: `2025-03-10` - Use OpenAPI metadata endpoints to list APIs and get schemas (see references). ## High-frequency operation patterns 1) Inventory/list: prefer `List*` / `Describe*` APIs to get current resources. 2) Change/configure: prefer `Create*` / `Update*` / `Modify*` / `Set*` APIs for mutations. 3) Status/troubleshoot: prefer `Get*` / `Query*` / `Describe*Status` APIs for diagnosis. ## Minimal executable quickstart Use metadata-first discovery before calling business APIs: ```bash python scripts/list_openapi_meta_apis.py ``` Optional overrides: ```bash python scripts/list_openapi_meta_apis.py --product-code <ProductCode> --version <Version> ``` The script writes API inventory artifacts under the skill output directory. ## Output policy If you need to save responses or generated artifacts, write them under: `output/aliyun-dlf-manage-next/` ## Validation ```bash mkdir -p output/aliyun-dlf-manage-next for f in skills/data-lake/aliyun-dlf-manage-next/scripts/*.py; do python3 -m py_compile "$f" done echo "py_compile_ok" > output/aliyun-dlf-manage-next/validate.txt ``` Pass criteria: command exits 0 and `output/aliyun-dlf-manage-next/validate.txt` is generated. ## Output And Evidence - Save artifacts, command outputs, and API response summaries under `output/aliyun-dlf-manage-next/`. - Include key parameters (region/resource id/time range) in evidence files for reproducibility. ## Prerequisites - Configure least-privilege Alibaba Cloud credentials before execution. - Prefer environment variables: `ALICLOUD_ACCESS_KEY_ID`, `ALICLOUD_ACCESS_KEY_SECRET`, optional `ALICLOUD_REGION_ID`. - If region is unclear, ask the user before running mutating operations. ## References - Sources: `references/sources.md`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: sjj2026/shike-autoresearch (version 1.0.0)

--- name: shike-autoresearch description: "CPU-based autonomous optimization loop for skill quality improvement. Runs experiments, evaluates results, keeps improvements. Use when: 自主优化, skill optimization, 达尔文优化, autoresearch." version: "1.0.0" author: "Shike" license: "MIT-0" tags: [optimization, autoresearch, darwin, skill-quality] --- # Shike Autoresearch - 自主优化循环 > 基于 Karpathy autoresearch 的 CPU 版本自主优化循环 --- ## 核心理念 **评估 → 改进 → 实测验证 → 人类确认 → 保留或回滚** --- ## 必需依赖 - **git** - 版本控制和代码回滚 - **python3** - 运行实验代码 - **subprocess** - 执行实验脚本 --- ## 权限说明 本技能需要以下权限（均为功能需求，限制在工作目录内）： - 修改 `experiment.py` 文件 - 运行 `subprocess` 执行实验 - 写入 `results.tsv` 记录结果 - 执行 `git commit` 和 `git revert` --- ## 三文件架构 | 文件 | 作用 | 谁修改 | |------|------|--------| | `config.py` | 配置参数、评估指标 | 只读（人类维护） | | `experiment.py` | 实验代码、测试逻辑 | Agent 自主修改 | | `results.tsv` | 实验记录 | 自动追加 | --- ## 自主循环流程 ``` LOOP: 1. 查看当前配置状态 2. [检查点1] 确认优化方向 3. 修改 experiment.py 4. [检查点2] 确认改动内容 5. 运行实验 6. 提取结果 7. [检查点3] 确认是否继续 8. 判断：改进 → 保留 / 未改进 → reset 9. 记录到 results.tsv 10. [检查点4] 每10轮复盘 11. 重复 ``` --- ## 适用场景 ### 1. 技能包优化 - 自动测试不同 prompt 配置 - 自动评估技能成功率 - 找到最优技能结构 ### 2. 策略回测 - 自动测试不同参数组合 - 自动评估收益率 - 找到最优策略配置 ### 3. 内容创作测试 - 自动测试不同写作风格 - 自动评估内容质量 - 找到最优内容策略 --- ## 关键检查点 ### 检查点1：确认优化方向 触发时机：评估skill前 用户确认：当前最优配置、拟改进方向 ### 检查点2：确认改动内容 触发时机：代码修改后，运行前 用户确认：git diff、改动说明 ### 检查点3：结果验收 触发时机：优化完成后 用户确认：前后分数对比、是否保留 ### 检查点4：定期复盘 触发时机：每10轮迭代后 用户确认：整体进度、趋势、资源消耗 --- ## 停止条件 ### 自动停止 - 达到最大迭代次数（默认100次） - 连续10次无改进 - 资源耗尽 ### 人工干预 - Ctrl+C - 优雅停止，保存当前状态 - SIGTERM/SIGINT - 接收信号停止 --- ## 使用方式 ```bash cd /path/to/skill-directory python3 run_loop.py --mode skill --target ./my-skill ``` ### 参数说明 - `--mode` - 优化模式（skill/strategy/content） - `--target` - 目标路径 - `--iterations` - 迭代次数（默认100） - `--timeout` - 单次实验超时（秒，默认60） --- ## 示例输出 ``` Round 1: 评估基线 - 62.8分 Round 2: 改进维度1 - 67.6分（+4.8分） Round 3: 改进维度3 - 72.6分（+5.0分） Round 4: 改进维度8 - 76.6分（+4.0分） 最终结果： - 基线分数: 62.8 - 最终分数: 76.6 - 总提升: +13.8分（+22%） - 成功率: 100%（4/4轮keep） ``` --- ## 技术支持 **免费版**：当前版本（MIT-0 license） **付费服务**： - 定制优化：¥500-2000/项目 - 企业部署：$500-2000 - 技术咨询：¥300/小时 **联系方式**： - Email：[REDACTED_SECRET] - 微信：ailvyou88999 --- ## 参考资源 - Karpathy autoresearch: https://github.com/karpathy/autoresearch - OpenClaw Skills: https://github.com/openclaw/skills

path: references/README.md
content: # References


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: leocryptoflow/wechat-intro (version 1.0.0)

--- name: WeChat-Intro description: 微信全面介绍。提供微信的发展历史、功能模块、版本变化、生态体系、商业化等内容的查询。适用于：1) 了解微信的发展历程；2) 熟悉微信各项功能；3) 了解微信生态和商业模式；4) 掌握微信运营技巧。 --- # 微信全面介绍 本技能帮助你全面了解微信的相关信息。 ## 微信概况 ### 基本信息 - **推出时间**：2011年1月21日 - **开发商**：腾讯公司 - **创始人**：张小龙 - **总部**：中国深圳 - **用户规模**：超过13亿月活跃用户 ### 发展历程 | 年份 | 里程碑 | |------|---------| | 2011 | 微信1.0发布 | | 2012 | 微信4.0推出朋友圈 | | 2013 | 微信支付上线 | | 2014 | 微信公众平台上线 | | 2015 | 微信城市服务上线 | | 2017 | 小程序发布 | | 2020 | 视频号上线 | | 2023 | 微信鸿蒙版发布 | ## 核心功能 ### 1. 即时通讯 - 文字消息 - 语音消息 - 视频通话 - 语音通话 - 表情包 - 文件传输 ### 2. 朋友圈 - 图片分享 - 视频分享 - 文字说说 - 位置分享 - 私密评论 ### 3. 微信公众号 - 订阅号 - 服务号 - 企业号 - 文章推送 - 菜单功能 ### 4. 微信支付 - 扫码支付 - 收款码 - 付款码 - 红包 - 话费充值 - 理财通 - 医疗健康 ### 5. 小程序 - 轻量应用 - 无需下载 - 即用即走 - 生活服务 - 电商购物 - 游戏娱乐 ### 6. 视频号 - 短视频 - 直播 - 公众号联动 - 私域流量 ### 7. 搜一搜 - 公众号搜索 - 小程序搜索 - 商品搜索 - 表情搜索 ### 8. 购物入口 - 京东购物 - 拼多多 - 唯品会 - 品牌特卖 ## 生态体系 ### 微信生态图谱 ``` ┌─────────────┐ │ 微信APP │ └──────┬──────┘ │ ┌─────────┼─────────┐ │ │ │ ┌────▼────┐ ┌▼─────┐ ┌──▼────┐ │ 公众号 │ │小程序 │ │视频号 │ └────┬────┘ └──────┘ └───┬────┘ │ │ └────────┬─────────┘ │ ┌─────▼─────┐ │ 微信支付 │ └─────┬─────┘ │ ┌─────────┼─────────┐ │ │ │ ┌───▼───┐ ┌──▼───┐ ┌──▼───┐ │ 京东 │ │拼多多│ │美团 │ └───────┘ └─────┘ └──────┘ ``` ### 腾讯系产品联动 - 微信 + QQ - 微信 + 腾讯视频 - 微信 + 音乐 - 微信 + 小说 ## 商业模式 ### 1. 微信支付 - 交易手续费 - 提现手续费 - 金融产品销售 ### 2. 微信公众号 - 流量主广告 - 赞赏功能 - 付费阅读 ### 3. 小程序 - 小程序广告 - 电商佣金 - 游戏分成 ### 4. 企业微信 - 企业版订阅 - 定制化服务 ### 5. 视频号 - 直播带货 - 广告分成 - 知识付费 ## 微信版本 ### 微信7.0（2018） - 全新界面 - 强提醒 - 时刻视频 ### 微信8.0（2021） - 表情包动态化 - 状态功能 - 浮窗优化 ### 微信8.0.XX（持续更新） - 视频号强化 - 搜索优化 - 性能提升 ## 微信使用技巧 ### 1. 高效沟通 - 善用语音转文字 - 使用收藏功能 - 置顶重要对话 - 开启免打扰 ### 2. 隐私保护 - 设置朋友圈可见范围 - 谨慎添加好友 - 定期检查登录设备 - 启用支付安全 ### 3. 生活便利 - 城市服务缴费 - 医疗健康挂号 - 交通出行购票 - 政务服务办理 ## 微信公众号运营 ### 1. 内容定位 - 垂直领域 - 价值输出 - 持续更新 ### 2. 粉丝增长 - 优质内容 - 互推合作 - 活动引流 - 视频号联动 ### 3. 变现方式 - 广告分成 - 知识付费 - 电商带货 - 会员订阅 ## 注意事项 - 微信生态复杂，本资料基于公开信息整理 - 如需最新功能，请查阅官方更新日志 - 遵守微信社区规范，合理运营 - 保护隐私，注意资金安全


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