OpenClaw/clawhub-security-signals huggingface dataset:
Skill: charlie-morrison/pr-review-assistant (version 1.0.1)

# PR Review Assistant Automated pull request review with structured feedback on code quality, security, performance, and best practices. Use when reviewing PRs, preparing code for review, or setting up automated review workflows. ## Usage ```bash # Review current branch changes against main python3 scripts/pr_review.py # Review specific PR (requires gh CLI) python3 scripts/pr_review.py --pr 42 # Review staged changes only python3 scripts/pr_review.py --staged # Review with specific focus areas python3 scripts/pr_review.py --focus security,performance ``` ## Review Categories The assistant evaluates code across 6 dimensions: ### 1. Correctness - Logic errors, off-by-one, null handling - Missing edge cases - Incorrect type usage ### 2. Security - Injection vulnerabilities (SQL, XSS, command) - Hardcoded secrets or credentials - Insecure deserialization - Missing input validation ### 3. Performance - N+1 queries, unnecessary loops - Memory leaks, unbounded growth - Missing indexes on queried fields - Inefficient algorithms ### 4. Maintainability - Dead code, unused imports - Functions doing too much - Unclear naming - Missing or excessive comments ### 5. Testing - Are new code paths covered? - Missing edge case tests - Test quality (assertions, mocking) ### 6. Best Practices - Framework-specific patterns - Error handling conventions - API design consistency - Documentation updates needed ## Output Format ```markdown ## PR Review Summary **Risk Level:** 🟢 Low / 🟡 Medium / 🔴 High ### Must Fix (blocking) - [file:line] Description of critical issue ### Should Fix (non-blocking) - [file:line] Description of improvement ### Consider (optional) - [file:line] Suggestion for better approach ### Positive Notes - What was done well ``` ## Parameters | Parameter | Description | Default | |-----------|-------------|---------| | `--pr` | GitHub PR number | None (uses diff) | | `--base` | Base branch to compare | `main` | | `--staged` | Review staged changes only | false | | `--focus` | Comma-separated focus areas | All | | `--severity` | Minimum severity to report | `low` | | `--format` | Output format: `markdown`, `json`, `github-comment` | `markdown` | | `--max-files` | Max files to review | 50 | ## AI Enhancement When used as an agent skill, the AI reviewer: - Understands project context from surrounding code, not just the diff - Identifies patterns across multiple changed files - Suggests specific code fixes, not just descriptions of problems - Learns from repository conventions and applies them consistently - Generates review comments in the project's preferred style


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: xiaojiou176/agent-exporter-archive-governance-workbench (version 1.0.0)

--- name: agent-exporter-archive-governance-workbench description: Use when an agent needs to wire the local agent-exporter MCP bridge, publish a local archive shell, save retrieval reports, or read governance evidence and policy packs from a repo checkout. This packet teaches the local stdio bridge path and the archive/retrieval/governance lane split without overclaiming hosted or listed-live status. triggers: - agent-exporter - archive shell - governance evidence - semantic retrieval - hybrid retrieval - local stdio mcp --- # agent-exporter Archive Governance Workbench Use this skill when an agent needs to attach the local `agent-exporter` bridge from a repo checkout and operate the archive/retrieval/governance workflow through a host-native packet. ## What this skill teaches - how to attach the local stdio MCP bridge - how to prove the bridge on a safe first-success path - how to publish a local archive shell - how to save local retrieval reports - how to read governance evidence without pretending there is a hosted platform ## Product truth - `agent-exporter` is a local-first archive and governance workbench - the current primary surface is still `CLI-first` - this public skill packet is a secondary public lane - the bridge exposes read-mostly archive, retrieval, and governance tools - the browser pages organize proof; they do not execute retrieval or onboarding ## First-success flow 1. Follow `references/INSTALL.md` and wire the bridge from a repo checkout. 2. Call [REDACTED_SECRET] to prove the bridge can answer a no-risk governance read. 3. If you have a workspace with transcript HTML receipts, call `publish_archive_index` on that workspace. 4. Only after the bridge works, move to retrieval or evidence comparison. ## Preferred evidence order 1. `references/INSTALL.md` 2. `references/DEMO.md` 3. `references/CAPABILITIES.md` 4. `references/TROUBLESHOOTING.md` ## Example prompts - "Wire the local agent-exporter bridge and show me the available governance policies." - "Publish the archive shell for this workspace and tell me where the archive shell landed." - "Run semantic retrieval for this workspace and save a local report." - "Compare two saved integration evidence snapshots and explain the readiness delta." ## Truth language - Good: "local stdio bridge" - Good: "secondary public lane" - Good: "archive and governance workbench" - Forbidden: "hosted platform" - Forbidden: "listed-live skill" without fresh host read-back - Forbidden: "full CLI exposed through MCP" ## Read next - `references/README.md` - `references/INSTALL.md` - `references/CAPABILITIES.md` - `references/DEMO.md` - `references/TROUBLESHOOTING.md`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: dinghaibin/touch-tool (version 1.0.0)

--- name: touch-tool description: Create empty files or update file timestamps. Use for file creation, timestamp management, and build system operations. --- # Touch - File Timestamp Manager Create empty files or update the access and modification timestamps of existing files. If the file doesn't exist, it is created empty. ## Usage ```bash touch-tool [options] <file...> ``` ## Options - `-a`: Change access time only - `-m`: Change modification time only - `-t STAMP`: Use specified time instead of current time - `-c`: Don't create file if it doesn't exist ## Examples ```bash touch-tool newfile.txt touch-tool -a oldfile.log touch-tool -t 202605011200 file.txt ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jmin1113/jmin-meeting-notes (version 1.0.0)

--- name: meeting-notes-generator description: 会议记录智能整理工具。输入会议要点或文字记录，自动生成结构化会议纪要、待办事项、决策记录。适用于职场人士、会议组织者、项目管理者。 --- # Meeting Notes Generator（会议纪要生成器） ## 这是什么 把零散的会议记录或要点丢给我，我帮你整理成： - 结构化会议纪要 - 清晰的待办事项（带负责人） - 明确的决策记录 - 下次会议议程 --- ## 使用方法 ### 方法一：粘贴原始记录 把会议记录的文字、笔记、甚至语音转文字的内容直接发给我，说「整理成会议纪要」。 ### 方法二：口述要点 直接对我说：「会议纪要：讨论了 A 事项，决定做 B，负责人是张三，下周三之前完成」。 我来整理成标准格式。 ### 方法三：输入主题 告诉我会议主题和讨论内容，我帮你生成会议纪要模板，你填内容后我再整理。 --- ## 输出格式示例 ``` ## 会议纪要：XXX 项目进度同步 **时间：** 2026年3月31日 14:00-15:00 **参会人：** 张三（主持）、李四、王五 **记录人：** [你的名字] --- ### 讨论事项 1. **项目进度** - 现状：已完成第一阶段开发 - 风险：测试资源不足 - 负责人：张三 2. **下周计划** - 完成联调测试 - 输出测试报告 - 负责人：李四 --- ### 决策记录 | 决策 | 原因 | 确认人 | |------|------|--------| | 上线时间延后 3 天 | 测试不充分 | 张三、李四 | --- ### 待办事项 | 事项 | 负责人 | 完成时间 | |------|--------|----------| | 完成测试报告 | 李四 | 4月3日 | | 确认上线排期 | 张三 | 4月4日 | | 通知客户延期 | 王五 | 4月5日 | --- ### 下次会议 时间：4月7日 14:00 议程：上线前最终确认 ``` --- ## 适用场景 - 周会/月会纪要 - 项目评审会议 - 客户沟通记录 - 一对一会议总结 - 头脑风暴记录整理 --- ## 支持语言 中文、英文、粤语均可识别和输出。 --- ## 包含内容 ``` meeting-notes-generator/ ├── SKILL.md # 主文件 ├── references/ │ └── templates.md # 各类型会议纪要模板 └── README.md ```


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

--- name: wechat-browser-reader description: "Read WeChat Official Account articles (mp.weixin.qq.com) via Chrome DevTools browser automation. Use when user provides a WeChat article URL and other extractors fail due to captcha, JS encryption, or anti-scraping. Requires Chrome remote debugging (port 9222) to be running. Handles captcha verification, JS content decryption, and content extraction." --- # WeChat Browser Reader Read WeChat articles via Chrome DevTools when HTTP-based extractors fail. ## Prerequisites - Chrome with remote debugging enabled: `google-chrome --no-first-run --remote-debugging-port=9222 --user-data-dir=/tmp/chrome-debug-profile` - OpenClaw browser tools available (navigate_page, evaluate_script, etc.) ## Workflow ### 1. Navigate ``` navigate_page(url=<article_url>) ``` ### 2. Handle Captcha (if present) If the page shows "环境异常" / "去验证": ``` take_snapshot() # find the "去验证" element click(uid=<verify_button_uid>) ``` If the page shows "轻触查看原文" (non-WeChat container): ``` evaluate_script(() => document.querySelector('.wx_expand_article_button_wrap')?.click()) ``` ### 3. Wait for Content WeChat articles use JS encryption. Content may take 3-5 seconds to decrypt after page load. ``` wait_for(text=["activity-name", "js_content"], timeout=15000) ``` ### 4. Extract Content ```javascript evaluate_script(() => { const title = document.getElementById('activity-name')?.innerText || ''; const author = document.getElementById('js_name')?.innerText || ''; const content = document.getElementById('js_content')?.innerText || ''; return { title, author, contentLength: content.length, content }; }) ``` If `activity-name` is empty but `js_content` exists, content is loaded — just extract it. If both are empty after 10+ seconds, try: ```javascript evaluate_script(() => new Promise(resolve => { setTimeout(() => { const el = document.getElementById('js_content'); resolve({ exists: !!el, htmlLen: el?.innerHTML?.length || 0, text: el?.innerText || '' }); }, 5000); })) ``` ### 5. Return to User Summarize or present the article content. Key fields: - **title**: article title - **author**: account name - **content**: full article text ## Common Issues | Symptom | Cause | Fix | |---------|-------|-----| | "环境异常" | Captcha triggered | Click "去验证", wait for redirect | | "轻触查看原文" | Non-WeChat browser | Click the button or use JS click | | Empty content after load | JS decryption not complete | Wait 3-5 seconds, retry extraction | | Chrome not connected | Remote debugging not running | Start Chrome with `--remote-debugging-port=9222` | | Page stuck on loading | Network or rendering issue | Reload page, check network conditions | ## Tips - Always use `evaluate_script` with `setTimeout` (3-5s) for reliable content extraction — WeChat's JS decryption is async - If captcha keeps appearing, the IP may be rate-limited — wait a few minutes - The approach works because a real Chrome browser executes WeChat's decryption scripts, unlike HTTP-only fetchers


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: freeman88-tch/video-craft-pro (version 1.0.0)

--- name: video-craft-pro description: "视频创作工坊：脚本+配音脚本+字幕三合一。融合 Video Script Creator + Voice Over Script + Caption 三大技能，一条龙搞定短视频创作全流程。适合短视频创作者、自媒体人、营销团队。" slug: video-craft-pro version: "1.0.0" author: "智美人团队" tags: - video - script - voiceover - caption - short video - content creation - tiktok - youtube - douyin - bilibili --- # VideoCraft Pro - 视频创作工坊 融合了 Video Script Creator（视频脚本）、Voice Over Script（配音脚本）、Caption/Subtitle（字幕）三大技能的优点，提供从创意到成片的完整视频创作流程。 ## 为什么用 VideoCraft Pro？ - **全流程**：创意→脚本→配音稿→字幕，一条龙 - **平台适配**：抖音/快手/YouTube Shorts/B站/Reels 各有专属模板 - **钩子优化**：前3秒钩子、完播率优化、互动引导 - **字幕同步**：自动生成时间轴字幕，支持中英文 ## 能力 ### 1. 视频脚本生成 (Script) - 从 `video-script-creator` 继承全部能力 - 平台专属脚本（抖音/快手/YouTube/B站） - 分镜指导、运镜建议、BGM标注 - 前3秒钩子、完播率优化 - 系列视频规划、数据复盘 ### 2. 配音脚本生成 (Voice Over) - 从脚本自动提取配音稿 - 语气标注（热情/沉稳/幽默/专业） - 语速标注（正常/快速/慢速） - 断句标注（用于TTS/录音） ### 3. 字幕/标题生成 (Caption) - 自动提取关键句作为字幕 - 标题建议（SEO优化） - 标签/话题推荐（hashtag） - 互动引导文案（CTA） - 双语字幕支持 ## 工作流程 ``` 用户需求 → 脚本生成 → 配音稿提取 → 字幕生成 ↓ 平台适配 ↓ 抖音/快手/YouTube/B站专属 ``` ## 使用方式 ``` video-craft-pro> 写个抖音脚本 [主题] → 生成完整分镜脚本+配音稿+字幕 video-craft-pro> 这个视频配字幕 [粘贴脚本] → 自动提取字幕+标题+标签 video-craft-pro> 改B站风格 [脚本ID] → 一键切换平台风格 ``` ## 已有技能融合点 | 源技能 | 融合点 | |--------|--------| | video-script-creator | 分镜脚本、平台适配、钩子优化 | | (voice over script 新创) | 配音提取、语气标注、语速控制 | | (caption 新创) | 字幕生成、标题优化、标签推荐 |


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: gptplusplus/salesclaw (version 3.0.2)

# SKILL.md - SalesClaw v3.0 医药销售业务本体数据库 v3.0（138张表）。 **核心理念**：用户问题 → NL2SQL → SQL 模板复用 → 归因分析 --- ## 数据库连接 | 项目 | 值 | |------|---| | Host | `localhost` | | Port | `3306` | | User | `ontology` | | Database | `salesclaw` | | 连接管理 | `tools/db.py`（`get_conn()` / `query_all()` / `query_one()`）| ```python from db import query_all, query_one rows = query_all("SELECT * FROM dim_products LIMIT 5") ``` --- ## 知识库 **路径**: `~/.openclaw/workspace/skills/salesclaw/knowledge/` ``` knowledge/ ├── index.md ← 总索引 ├── entities/ ← 138张表的字段说明（doctors/hospitals/dim_products/...） ├── concepts/ ← 业务概念（three-layer-attribution / expense-red-green / ...） └── sql_templates/ ← 沉淀的 SQL 模板（NL2SQL 复用） ``` --- ## SQL 模板（高频问题沉淀） | 模板 ID | 适用问题 | 优先级 | |---------|---------|--------| | `prescription_trend` | 品种趋势/处方量下滑/环比 | P0 | | `expense_red_green` | 费用异常/DeltaWeight/红黄灯 | P0 | | `rep_performance` | 代表绩效/红黄牌/达成率 | P0 | | `target_attainment` | 区域达成/缺口分析 | P1 | | `hospital_access` | 进院情况/VBP覆盖 | P1 | | `prescription_penetration` | 渗透率/覆盖率/上量空间 | P1 | | `doctor_profile` | 医生档案/KOL识别 | P1 | | `data_overview` | 数据概览/记录数检查 | P2 | --- ## 三层归因模型（归因分析标准方法论） ### 结构归因（What - 规模维度） **问题**：失血发生在哪个结构单元？ 分析方向： - 品种维度：哪个品种贡献主要失血？ - 省区维度：大区 → 省区 → 地区逐层下探 - 时间维度：定位断崖点（哪个月开始下滑） - 医院层级：一级/二级/三级分层 ```sql -- 定位省区处方量贡献（结构归因第一步） SELECT province, SUM(prescription_volume) as vol, SUM(prescription_volume) / SUM(SUM(prescription_volume)) OVER () * 100 as share_pct FROM fct_prescription_flow WHERE product_id = :product_id GROUP BY province ORDER BY vol DESC; ``` ### 行为归因（Why - 驱动维度） **问题**：结构单元的失血是因为什么行为变化？ 分析方向： - 覆盖行为：流向医院数是否下降？ - 拜访行为：代表拜访频次是否下降？ - 竞品行为：竞品是否有学术活动/准入变化？ - 通道行为：医院准入/招标是否有变化？ ### 决策归因（Who - 责任维度） **问题**：谁（哪个代表/哪条线）应对变化负责？ 分析方向： - 个人分层：Top/Bottom 代表分层对比达成率 - 分管人责任：区域经理覆盖效率是否达标 - 组织连带：区域负责人是否尽到管理责任 --- ## DeltaWeight 计算规范（费用告警标准） ``` DeltaWeight = 某省区某费用类型占比 - 全国该费用类型占比 省区费用占比 = 该省该费用类型金额 / 该省费用总额 全国费用占比 = 该费用类型金额 / 全国费用总额 ``` | 告警级别 | 阈值 | 响应时间 | |---------|------|---------| | 🔴 红灯 | DeltaWeight ≥ 30ppt | 24h | | 🟡 黄灯 | DeltaWeight ≥ 15ppt | 1周 | --- ## 诊断会话表（归因推理引擎） | 表 | 用途 | |------|------| | `fct_diagnosis_session` | 每次告警触发一次诊断会话 | | `fct_reasoning_step` | 每层归因的每一步记录 | | `dim_diagnosis_rules` | 诊断规则模板（5条预置）| | `conclusions` | 归因结论输出 | --- ## 核心关系边（星型拓扑，以 ontology_objects 为中心） ``` ontology_objects.id（中心节点） ↓ ├── object_links（关系边：代表←→医生、代表←→医院、竞品关系） ├── object_events（事件边：拜访、学术活动、处方事件） └── time_series_data（时序边：处方量趋势、费用趋势） ``` --- ## 工具说明（Tools） | 工具 | 文件 | 功能 | |------|------|------| | 业务路由器 | `tools/ask.py` | 意图识别 + 10大业务模块查询 | | 推理引擎 | `tools/inference.py` | 三层归因诊断会话（structure→behavior→decision，含置信度传播+反例探测+Early Exit）| | 知识 API | `tools/knowledge.py` | 统一知识消费：load_sql_template / load_entity_schema / load_concept / render_sql | | 数据库连接 | `tools/db.py` | 连接池管理（超时重试+降级+错误透出）| | 初始化 | `tools/init_mysql.py` | 从 init.sql 初始化 | > **使用方式**：用户问题 → NL2SQL → 常见问题沉淀为 SQL 模板；复杂推理链路（如三层归因）由工具封装。


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal:  | SkillSpector: suspicious (CRITICAL)
Skill: dolphins1123/mp4-to-srt (version 1.1.0)

--- name: mp4-to-srt description: 將本地 MP4/MKV/AVI 影片用 AI 轉成 SRT 字幕，自動轉為繁體中文！ --- # MP4 轉 SRT 字幕神器 🎬→📝 ## 這能做什麼？ ✅ 將 MP4/MKV/AVI 轉成 SRT 字幕 ✅ 自動轉為**繁體中文** ✅ 支援多語言 (中/英/日/韓) ✅ 自動偵測語言 ## 使用方式 ```bash # 基本轉換 (自動轉繁體) mp4-to-srt.sh "影片路徑.mp4" # 指定輸出檔案 mp4-to-srt.sh "影片.mp4" "輸出.srt" # 指定模型 (tiny, base, small, medium, large) mp4-to-srt.sh "影片.mp4" "輸出.srt" "small" ``` ## 需求 - ffmpeg: `brew install ffmpeg` - Whisper: `pip3 install openai-whisper` - opencc: `brew install opencc` (用於轉繁體) ## 輸出 標準 SRT 字幕檔案，可直接匯入 YouTube 或播放器。 ## 流程 1. Whisper AI 轉錄影片音頻 2. 自動轉換為繁體中文 (opencc) 3. 輸出標準 SRT 格式 ## 備註 - 預設模型: small (速度與準確度平衡) - 較大的模型較慢但更準確 ## 作者 Kuanlin


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

--- name: CI发布工程师-CI与发布门禁 description: CI release engineer skill for validation gating, release-readiness checks, and automation-safe delivery criteria. --- # Role This skill owns release gating and CI-oriented readiness checks. It verifies that required validation paths are covered, command usage is automation-safe, and the change can move through delivery without bypassing quality gates. # When To Use - Use for CI checks, release gates, automation readiness, and pre-merge validation policy. - Use for keywords such as CI, release gate, merge gate, preflight, pipeline, and release readiness. - Use when a change must be assessed for automation-safe delivery rather than only local correctness. # Source Material - `AI-ENTRY.md` - `CLAUDE.md` - `dev/ai/skills/testing/SKILL.md` - `dev/ai/skills/planning/SKILL.md` - `dev/ai/skills/documentation-standards/SKILL.md` # Responsibilities - Define and enforce the validation set required before release or merge. - Check that commands, tests, and docs support automated delivery. - Flag missing gates, weak evidence, or unsafe release assumptions. - Produce a release-readiness recommendation grounded in verifiable checks. # Workflow 1. Read the scope, changed surfaces, and required release confidence level. 2. Review which validation steps are mandatory for merge or release. 3. Confirm that unit, route, E2E, WLS, and documentation checks are covered where needed. 4. Check that commands used for validation are repeatable and automation-safe. 5. Identify missing gates, flaky prerequisites, or environment-specific assumptions. 6. Summarize release readiness and blocking items. 7. Coordinate with QA and implementation roles if gaps remain. # Weline Rules - Provide unit test and E2E or HTTP validation evidence where relevant. - Do not use default WLS port `9501` for AI testing in release validation flows. - Always stop dedicated WLS instances after validation. - Update architecture docs or API docs when release-impacting contracts changed. # Inputs Required - The changed scope and intended release or merge target. - Returned validation evidence from implementation and QA roles. - Any CI or automation constraints for the branch. - Required confidence level and known blockers. # Expected Output - A release-gate decision or recommendation. - A list of mandatory checks satisfied and missing. - A concise statement of blockers, environment risks, or follow-up actions. # Validation - Check that every required gate has repeatable evidence. - Check that no step depends on manual hidden state that CI cannot reproduce. - Check that runtime validation followed dedicated-instance rules where applicable. - Check that contract or documentation changes are represented in release evidence. # Constraints - Do not approve release readiness on local intuition alone. - Do not ignore flaky validation prerequisites. - Do not bypass missing evidence because a change appears low risk. - Do not collapse QA and CI gate responsibilities into one vague signoff.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: seanshen-lec/send2tv (version 1.2.0)

--- name: send2tv description: "Push text messages, images, or audio to Huawei Smart Screen via DLNA/UPnP. Use when user wants to display something on TV or play audio/TTS on TV. Triggers: send to TV, push to TV, display on TV, 电视上显示, 发到电视, 推送到电视, 电视播放, 语音播报." --- # send2tv Push text, images, or audio to Huawei Smart Screen V75 (or other DLNA-compatible TVs) via UPnP AVTransport protocol. ## Quick Usage ```bash # Text (Chinese supported) python3 scripts/send2tv.py "快去写作业！" # Text with custom font size python3 scripts/send2tv.py "Warning!" --font-size 300 # Image file python3 scripts/send2tv.py --image /path/to/photo.jpg # Image with text overlay python3 scripts/send2tv.py --image /path/to/photo.jpg --text "Hello!" # Audio file python3 scripts/send2tv.py --audio /path/to/music.mp3 # TTS - Text to speech (requires edge-tts skill) python3 scripts/send2tv.py --tts "这是一段要朗读的文字" python3 scripts/send2tv.py --tts "Hello world" --voice en-US-MichelleNeural python3 scripts/send2tv.py --tts "快点！" --rate +20% ``` ## How It Works **Image/Text mode:** 1. Renders text to 1920x1080 black image (or serves image directly) 2. Starts local HTTP server on port 8082 3. Sends DLNA/UPnP SOAP SetAVTransportURI + Play commands to TV 4. TV downloads image via HTTP and displays it **Audio mode:** 1. Prepares audio file (converts to MP3 if needed) 2. Starts local HTTP server on port 8082 3. Sends DLNA/UPnP SOAP commands to TV 4. TV streams audio via HTTP and plays it **TTS mode:** 1. Uses edge-tts to convert text to speech 2. Pushes audio to TV via DLNA ## TV Configuration - **IP**: 192.168.3.252 - **UPnP Port**: 25826 - **HTTP Server Port**: 8082 - **UPnP Service**: urn:schemas-upnp-org:service:AVTransport:1 ## Font Notes - Chinese text uses WenQuanYi Zen Hei (文泉驿正黑), auto-detected - English text uses DejaVu Sans Bold - Font auto-scales to fit 1920x1080 (90% width max) ## TTS Voices (for --tts mode) **Chinese:** - `zh-CN-XiaoxiaoNeural` - female, natural (default) - `zh-CN-YunyangNeural` - male, natural - `zh-CN-XiaoyiNeural` - female, sweet - `zh-CN-YunjianNeural` - male, mature **English:** - `en-US-MichelleNeural` - female, natural - `en-US-AriaNeural` - female, natural - `en-US-GuyNeural` - male, natural See full list: https://speech.microsoft.com/portal/voicegallery ## Troubleshooting **TV转圈不显示**: 端口8082被防火墙拦。需要在Windows防火墙添加入站规则允许8080-8090端口。 **文字太小/太大**: 用 `--font-size` 调整，默认200像素。 **图片推送失败**: 确认图片路径存在且为有效图片文件。 **音频不播放**: 确认电视支持MP3格式，检查DLNA服务是否开启。 **TTS失败**: 确认已安装 edge-tts skill (`skillhub install openclaw/skills/edge-tts`)


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: wimi321/claude-md-initializer (version 1.0.0)

--- name: "claude_md_initializer" description: "Use when onboarding a repository into Claude Code by creating or improving CLAUDE.md, and optionally identifying useful skills, hooks, and verifier setup." --- # CLAUDE MD Initializer Use this skill when a repo needs onboarding documentation for future agent sessions. ## Workflow 1. Read the repository structure, README, existing instructions, and tooling config. 2. Identify key build, test, and run commands. 3. Summarize the high-level architecture and important conventions. 4. Create or improve `CLAUDE.md` without stuffing it with generic advice. 5. Suggest follow-on skills, hooks, or verifier setup if they materially help. ## Guardrails - Do not invent commands or conventions. - Focus on repo-specific knowledge, not generic coding advice. - Keep the file practical for future agent sessions. ## Example Requests - Analyze this repo and produce a strong CLAUDE.md for future agent sessions. - Upgrade the existing CLAUDE.md so future coding agents onboard faster. ## Inputs - Repository structure - Existing docs and config - Important commands and conventions ## Outputs - New or improved CLAUDE.md - Optional follow-up recommendations ## Success Criteria - Repo-specific commands are captured correctly. - Architecture notes are practical, not generic. - The file helps future agents move faster. ## Non-Goals - Generic engineering advice - Invented commands or undocumented workflows ## Source Provenance Derived from `src/commands/init.ts`.


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