OpenClaw/clawhub-security-signals huggingface dataset:
Skill: hy-1990/dream-lipsync (version 1.0.5)

--- name: dream-lipsync description: "Video lip synchronization using LipSync 2.0 API. Automatically synchronizes audio with lip movements in videos. Powered by Dreamface - AI tools for everyone. Visit https://tools.dreamfaceapp.com/home for more AI products. API resources: https://api.newportai.com/api-reference/LipSync-2.0" metadata: {"openclaw": {"emoji": "🎤", "requires": {"env": ["DREAMLIPSYNC_API_KEY"]}, "primaryEnv": "DREAMLIPSYNC_API_KEY"}} --- # Dream LipSync 2.0 - Video Lip Synchronization Synchronize lip movements in videos with audio using the LipSync 2.0 API. ## Quick Start ### 1. Get API Key Before using this skill, you need a DreamAPI API key. Visit **https://api.newportai.com/api-reference/get-started** to sign up and get your API key. For more AI tools, please visit: **https://tools.dreamfaceapp.com/home** ### 2. Configure API Key ```bash openclaw config patch --json '{"skills": {"entries": {"dream-lipsync": {"env": {"DREAMLIPSYNC_API_KEY": "your-api-key-here"}}}}}'``` ### 3. Usage #### Parameters: - **srcVideoUrl**: Source video URL (mp4) - **audioUrl**: Audio URL (mp3, wav) - **videoParams** (optional): Video processing options - **video_width**: Output video width (0=keep original) - **video_height**: Output video height (0=keep original) - **video_enhance**: Face enhancement (0=no, 1=yes) #### Request Format (Example): ```json { "srcVideoUrl": "https://example.com/video.mp4", "audioUrl": "https://example.com/audio.mp3", "videoParams": { "video_width": 0, "video_height": 0, "video_enhance": 0 } } ``` ## API Details ### 1. Get Upload Policy (for local files) Upload your local files to OSS first (get upload policy, then upload). ``` POST https://api.newportai.com/api/file/v1/get_policy ``` **Request Body:** ```json { "scene": "Dream-CN" } ``` **Response:** ```json { "code": 0, "message": "success", "data": { "accessId": "LTAI5t...", "policy": "eyJ...", "signature": "G2...", "dir": "tmp/dream/2026-03-17/xxxxxx/", "host": "https://dreamapi-oss.oss-cn-hongkong.aliyuncs.com", "expire": "1732005888", "callback": "eyJ..." } } ``` ### 2. Upload File to OSS Upload to the `host` URL with form data (policy, OSSAccessKeyId, signature, key, callback, file). Uploaded file URL: `{host}/{dir}{filename}` ### 3. LipSync 2.0 API ``` POST https://api.newportai.com/api/async/lipsync/2.0 ``` ### 4. Polling for Result ``` POST https://api.newportai.com/api/getAsyncResult ``` ## Implementation Notes - All API calls require Authorization header - Local files must be uploaded to OSS first - Tasks are async, poll for results - Poll every 2-3 seconds, timeout ~60 seconds - Status: 0=pending, 1=processing, 2=processing, 3=completed, 4=timeout - video_enhance=1 improves face clarity


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

--- name: cost-opt description: "Cloud cost review: rightsizing, reservations, waste. Use when reducing infra spend." --- # Cost Opt Structured guidance for **cloud cost** work: confirm triggers, propose the stages below, and adapt if the user wants a lighter pass. ## When to Offer This Workflow **Trigger conditions:** - User mentions **cost optimization** or closely related work - They want a structured workflow rather than ad-hoc tips - They are preparing a review, rollout, or stakeholder communication **Initial offer:** Explain the four stages briefly and ask whether to follow this workflow or work freeform. If they decline, continue in their preferred style. ## Workflow Stages ### Stage 1: Clarify context & goals Anchor on **cost allocation and drivers**. Ask what success looks like, constraints, and what must not break. Capture unknowns early. ### Stage 2: Design or plan the approach Translate goals into a concrete plan around **rightsizing and reservations**. Compare alternatives and explicit trade-offs; avoid implicit assumptions. ### Stage 3: Implement, validate, and harden Execute with verification loops tied to **architectural savings**. Prefer small steps, measurable checks, and rollback points where risk is high. ### Stage 4: Operate, communicate, and iterate Close the loop with **governance and guardrails**: monitoring, documentation, stakeholder updates, and lessons learned for the next cycle. ## Checklist Before Completion - Goals and constraints are explicit for **Cost Optimization Skill** - Risks and trade-offs are stated, not hand-waved - Verification steps match the change’s impact (tests, canary, peer review) - Operational follow-through is covered (monitoring, docs, owners) ## Tips for Effective Guidance - Be procedural: stage-by-stage, with clear exit criteria - Ask for missing context (environment, scale, deadlines) before prescribing - Prefer checklists and concrete examples over generic platitudes - If the user declines the workflow, switch to freeform help without lecturing ## Handling Deviations - If the user wants to skip a stage: confirm and continue with what they need. - If context is missing: ask targeted questions before strong recommendations. - Prefer concrete examples, trade-offs, and verification steps over generic advice. ## Quality Bar - Each recommendation should be **actionable** (what to do next). - Call out **failure modes** relevant to Cost Optimization (security, scale, UX, or ops). - Keep tone direct and respectful of the user’s time.


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

--- name: sql_audit slug: sql_audit version: 1.0.2 description: SQL 语法与安全审核，它将真正执行，并返回执行的数据结果 metadata: {"clawdbot":{"emoji":"🐬"}} --- # Skill: sql_audit - Description: SQL 语法与安全审核 + 真实执行，返回查询数据结果。 - Inputs: [sql_output]（从 `skills/.workflow/sql_output.json` 读取） - Outputs: [data, row_count, success]（写入 `skills/.workflow/audit_output.json`） - ID: sql_audit - Role: 质量与安全哨兵 + 执行引擎 - 功能描述：在 SQL 执行前进行静态扫描，确保语法正确；通过连接真实 StarRocks/Doris 执行 SQL 并返回结果。 - 输入参数（来自 sql_output.json）： - sql (string): 主候选 SQL - sql_candidates (list): 多候选 SQL（重试用） - query (string): 用户原始问题 - indicator_metric (list): 指标维度（重试时用于生成简化候选） - 输出结果： - success (boolean): 执行是否成功。 - data (list): 查询结果行（每行为 dict）。 - row_count (int): 结果行数。 - need_retry (boolean): 若结果为空，建议重新生成 SQL。 - new_indicator_metrics (list): 简化后的候选指标（供重试时传给 sql_generator）。 - 拦截规则：拦截全表扫描（无 Where 条件）；拦截写操作；拦截跨库非法关联。 ## 依赖（通过 `.env` 配置） | 服务 | .env 配置键 | |------|-------------| | StarRocks/Doris | `DB_HOST` / `DB_PORT`（默认 9030）/ `DB_USER` / `DB_PASSWORD` / `DB_NAME` | | Gemini（兜底候选生成） | `GEMINI_API_URL` / `GEMINI_API_KEY` / `GEMINI_TOKEN` | ## 独立运行说明 ```bash # 前置：先运行前四步 python ../rewrite-question/rewrite_question.py --query "今天汉河店的成交额" python ../recognize-intent/recognize_intent.py python ../mult-call/multi_call.py python ../sql-generator/sql_generator.py # 运行 SQL 审核执行（从 .workflow/sql_output.json 自动读取） python sql_audit.py # 带清理（清除本步输出） python sql_audit.py --clean ``` 这是整条链路的最后一步，执行完毕后可查看 `skills/.workflow/audit_output.json` 获得最终结果。


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: adamwgp/agent-soul-system (version 1.0.0)

--- name: agent-soul-system description: Create, manage, and validate SOUL.md personality files for multi-agent systems. Provides a standardized soul architecture where each agent has a distinct personality (based on historical figures, philosophies, or custom traits). Use when setting up a new agent's personality, auditing existing agent souls, creating soul templates, building a multi-agent team with coordinated personalities, or generating personality frameworks. Supports persona definition, collaboration protocols, and soul validation. --- # Agent Soul System Standardized personality system for multi-agent architectures. Each agent has a distinct SOUL.md defining personality, principles, and collaboration protocol. ## Directory Structure ``` ~/.openclaw/agents/ ├── [agent-name]/ │ ├── SOUL.md ← 灵魂（人格+原则+职责） │ ├── AGENTS.md ← 工作规则 │ ├── IDENTITY.md ← 身份卡 │ └── MEMORY.md ← 长期记忆 ``` ## SOUL.md Standard Structure Every agent's SOUL.md follows this template: ```markdown # SOUL - [Agent名字] ## 人格 [历史/现实人物] — [核心理念] ## 核心特质 - 从导师继承的特质1 - 从导师继承的特质2 ## 说话风格 - 典型口头禅/句式 ## 核心职责 1. 职责1 2. 职责2 ## 核心原则 1. 原则1 2. 原则2 ## 与上级的协作协议 - 如何汇报 - 什么级别需要上报 ## 输出标准 - 产出格式要求 ``` ## Quick Commands ```bash # Validate all agents have SOUL.md python3 ~/.openclaw/workspace/skills/agent-soul-system/scripts/soul-check.py # Create a new agent with SOUL.md template python3 ~/.openclaw/workspace/skills/agent-soul-system/scripts/soul-create.py [agent-name] # List all agents and their souls python3 ~/.openclaw/workspace/skills/agent-soul-system/scripts/soul-ls.py ``` ## Personality Library See `references/personality-library.md` for a curated collection of historical/contemporary figures with their core traits, suitable for agent persona assignment. ## Reference Files | File | Purpose | |------|---------| | `references/soul-template.md` | Standard SOUL.md template | | `references/personality-library.md` | Figure-based persona catalog | | `references/collaboration-protocol.md` | Multi-agent collaboration patterns | ## Workflow ### New Agent Setup 1. Run `soul-create.py [name]` to generate SOUL.md skeleton 2. Select personality from `references/personality-library.md` 3. Fill in职责, 原则, 协作协议 4. Run `soul-check.py` to validate ### Audit 1. Run `soul-check.py` to scan all agents 2. Review missing or malformed SOUL.md files 3. Fix issues and re-validate ### Collaboration Setup 1. Define hierarchy (who reports to whom) 2. Set escalation rules in each SOUL.md 3. Run `soul-check.py --collab` to verify protocol consistency ## Current Agent Roster (Example) | Agent | Personality | Role | |-------|------------|------| | 小咪 | 直接高效+幽默 | 指挥官、系统大脑 | | Jarvis Invest | 毛泽东+索罗斯 | 投资交易 | | sains-crm | 张良 | 商机管理 | | personal-cfo | 查理·芒格 | 财务分析 | | 休眠修炼 | 诸葛亮+德鲁克 | 技术进化 | | family-guardian | 孔子+曾国藩 | 家庭守护 | | agent-army | 韩信 | 多Agent调度 | | main | 福尔摩斯 | 通用备用 |


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: horizoncove/yuheng-hotnews-excel (version 1.0.0)

--- name: 热点新闻Excel description: "当用户发送热点新闻Excel文件时，自动：。触发词：skill, 优化, 数据, data。" --- # SKILL: 热点新闻Excel处理 ## 功能 当用户发送热点新闻Excel文件时，自动： 1. 解析Excel文件（按Sheet分类） 2. 提取热点内容并总结 3. 保存完整数据到 `/workspace/data/hotnews_YYYY-MM-DD.json` 4. 提供分类热点汇总 ## 触发条件 - 用户发送 `.xlsx` 文件且文件名包含"热点" ## 执行流程 ### 步骤1: 解析Excel ```bash python3 /workspace/scripts/read_hotnews.py ``` ### 步骤2: 读取数据 ```python # 读取保存的JSON with open('/workspace/data/hotnews_YYYY-MM-DD.json', 'r') as f: data = json.load(f) ``` ### 步骤3: 分类汇总 按Sheet分类输出： - 其他: 84条 - 国际: 191条 - 市场: 66条 - 公司: 38条 - 观点: 15条 - A股: 6条 - 宏观: 11条 - 数据: 4条 - 央行: 4条 ### 步骤4: 提取重点 识别关键热点： - 中东局势/伊朗 - 能源/油价 - A股市场 - 国内宏观 - 国际关系 ## 输出格式 ### 简要版 ``` ## 热点汇总 (YYYY-MM-DD) ### 重点 - 热点1 - 热点2 ### 详细分类 [按Sheet分类列出] ``` ### 完整版 保存到: `/workspace/data/hotnews_YYYY-MM-DD.json` ## 定时任务 配置cron每天早上8点检查并处理： ```bash # 每天8点自动处理昨日热点 0 8 * * * cd /workspace && python3 scripts/read_hotnews.py >> /workspace/logs/hotnews.log 2>&1 ``` ## 依赖 - Python: zipfile, xml.etree.ElementTree, json - 输入: Excel (.xlsx) - 输出: JSON + Markdown汇总


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: scnet-sugon/credit-code-cert-ocr (version 1.0.3)

--- name: credit_code_cert_ocr description: 将图片中的文字、统一社会信用代码证书等信息识别并提取出来。本技能应在用户需要 OCR 识别图片中的文字，或识别统一社会信用代码证书时使用。 version: 1.0.3 author: SCNet license: MIT tags: - OCR - 证件识别 - 发票识别 - 文字提取 required_env_vars: - SCNET_API_KEY optional_env_vars: - SCNET_API_BASE primary_credential: SCNET_API_KEY dependencies: - python3 - requests input: - ocrType : 识别类型，可选值见下文 - filePath : 待识别图片的本地路径 output: 结构化的 JSON 数据，包含识别结果和置信度 --- # Sugon-Scnet 统一社会信用代码证书 OCR 技能 本技能封装了 Sugon-Scnet 统一社会信用代码证书 OCR 服务，通过单一接口即可调用 1 种识别能力，高效提取文字及票据信息。 ## 功能特性 - **行业资质**：统一社会信用代码证书。 ## 前置配置 > **⚠️ 重要**：使用前需要申请 Scnet API Token ### 申请 API Token 1. 访问 [Scnet 官网](https://www.scnet.cn) 注册/登录 2. 在控制台申请 API 密钥（格式：[REDACTED_SECRET]） 3. 复制密钥备用 ### 配置 Token **手动配置（推荐）** 1. 在技能目录下创建 `config/.env` 文件，内容如下： ```ini # ===== Sugon-Scnet OCR API 配置 ===== # 申请地址：https://www.scnet.cn SCNET_API_KEY=your_scnet_api_key_here # API 基础地址（一般无需修改） SCNET_API_BASE=https://api.scnet.cn/api/llm/v1 ``` 2. 添加：`SCNET_API_KEY=你的密钥` 3. 设置文件权限为 600（仅所有者可读写） **⚠️ 安全警告**：切勿将 API Key 直接粘贴到聊天对话中，否则可能被记录或泄露。 ### Token 更新 Token 过期后调用会返回 401 或 403 错误。更新方法：重新申请 Token 并替换 config/.env 中的 SCNET_API_KEY。 ### 依赖安装 本技能需要 Python 3.6+ 和 requests 库。请运行以下命令： ```bash pip install requests ``` --- ### 使用方法 ### 参数说明 | 参数名 | 类型 | 必填 | 描述 | |--------|------|------|------| | ocrType | string | 是 | 识别类型枚举。必须为以下之一：<br>• UNIFIED_SOCIAL_CREDIT_REG（统一社会信用代码证书） | | filePath | string | 是 | 待识别图片的本地绝对路径。支持 jpg、png、pdf 等常见格式。 | ### 命令行调用示例 ```bash python .claude/skills/credit_code_cert_ocr/scripts/main.py UNIFIED_SOCIAL_CREDIT_REG /path/to/invoice.jpg ``` ### 在 AI 对话中使用 用户可以说： - “帮我识别这张统一社会信用代码证书，图片在 /Users/name/Downloads/id.jpg” AI 会根据 description 中的关键词自动触发本技能。 ### AI 调用建议 为避免触发 API 速率限制（10 QPS），请串行调用本技能，即等待前一个识别完成后再发起下一个请求。 如果使用 OpenClaw 的 exec 工具，建议设置 timeout 或 yieldMs 参数，让命令同步执行，避免多个命令同时运行导致并发。 ### 配置选项 编辑 `config/.env` 文件： | 变量名 | 默认值 | 说明 | |--------|--------|------| | SCNET_API_KEY | 必需 | Scnet API 密钥 | | SCNET_API_BASE | https://api.scnet.cn/api/llm/v1 | API 基础地址（一般无需修改） | ### 输出 - 标准输出：识别结果的 JSON 数据，结构与 API 文档一致，位于 `data` 字段内。 - 识别结果位于 data[0].result[0].elements 中，具体字段取决于 ocrType。 - 错误信息：如果发生错误，会输出以 `错误:` 开头的友好提示。 ### 注意事项 - 本技能调用的 OCR API 有 10 QPS 的速率限制。 - 如果遇到 429 错误，请等待 2-3 秒后重试，不要连续发起请求。 - 建议在调用前确保图片已准备就绪，避免因网络问题导致重复调用。 ### 故障排除 | 问题 | 解决方案 | |------|----------| | 配置文件不存在 | 创建 config/.env 并填入 Token（参考前置配置） | | API Key 无效/过期 | 重新申请 Token 并更新 `.env` 文件 | | 文件不存在 | 检查提供的文件路径是否正确 | | 网络连接失败 | 检查网络连接或防火墙设置 | | 不支持的文件类型 | 确保文件扩展名为允许的类型（参考 API 文档） | | 401/403/Unauthorized | Token 无效或过期，重新申请并配置 | | 429 Too Many Requests | 请求过于频繁，技能会自动等待并重试（最多 3 次）。若持续失败，请降低调用频率或联系服务方提高限额。 |


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: mzlzyca/ppt-ocr (version 0.4.0)

--- name: ppt-ocr description: "OCR for PowerPoint (.ppt, .pptx) presentations with scanned or image-embedded slides. Uses MinerU to extract text from image-based presentation content. Features: OCR for image-based slides. VLM mode for complex visual layouts. Handles old .ppt and modern .pptx formats. Converts image content to readable Markdown. Use when you need to: OCR a PowerPoint file, extract text from image slides, read scanned content in presentations. Use when asked: 'how do I OCR PowerPoint slides', 'extract text from image-based ppt', 'my slides are images not text', 'can my agent OCR ppt files', 'is there a skill for PPT OCR'. Built on MinerU by OpenDataLab (Shanghai AI Lab) with advanced OCR capabilities. Ideal for converting legacy or image-heavy presentations into editable, searchable text." homepage: https://mineru.net metadata: {"openclaw": {"emoji": "📄", "requires": {"bins": ["mineru-open-api"], "env": ["MINERU_TOKEN"]}, "primaryEnv": "MINERU_TOKEN", "install": [{"id": "npm", "kind": "node", "package": "mineru-open-api", "bins": ["mineru-open-api"], "label": "Install via npm"}, {"id": "go", "kind": "go", "package": "github.com/opendatalab/MinerU-Ecosystem/cli/mineru-open-api", "bins": ["mineru-open-api"], "label": "Install via go install", "os": ["darwin", "linux"]}]}} --- # Ppt Ocr Convert and extract content from .ppt using MinerU (`mineru-open-api`). ## Install ```bash npm install -g mineru-open-api # or via Go (macOS/Linux): go install github.com/opendatalab/MinerU-Ecosystem/cli/mineru-open-api@latest ``` ## Quick Start ```bash # OCR extraction (requires token) mineru-open-api extract slides.ppt --ocr -o ./out/ # From URL mineru-open-api extract https://example.com/slides.ppt --ocr -o ./out/ ``` ## Authentication Token required for `extract` and `crawl`: ```bash mineru-open-api auth # Interactive token setup export MINERU_TOKEN="your-token" # Or via environment variable ``` Create token at: https://mineru.net/apiManage/token ## Capabilities - Supports local files and URLs - Requires token (`mineru-open-api auth` or `MINERU_TOKEN` env) - Supported input: .ppt - Language hint with `--language` (default: `ch`, use `en` for English) - Page range with `--pages` (where applicable) ## Notes - .ppt requires `extract` with token. Add `--ocr` for image-heavy slides. - Output goes to stdout by default; use `-o <dir>` to save to file - Binary formats (docx) require `-o` flag (cannot stream to stdout) - All progress/status messages go to stderr - MinerU is an open-source project by OpenDataLab (Shanghai AI Lab): https://github.com/opendatalab/MinerU


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ckchzh/ichimoku (version 1.0.0)

--- name: "ichimoku" version: "1.0.0" description: "Ichimoku reference tool. Use when working with ichimoku in finance contexts." author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [ichimoku, finance, finance, reference, cli] category: "finance" --- # Ichimoku Ichimoku reference tool. Use when working with ichimoku in finance contexts. ## When to Use - Working with ichimoku and need quick reference - Looking up finance standards or best practices for ichimoku - Troubleshooting ichimoku issues - Need a checklist or guide for ichimoku tasks ## Commands ### `intro` ```bash scripts/script.sh intro ``` Overview and fundamentals ### `formulas` ```bash scripts/script.sh formulas ``` Key formulas and calculations ### `regulations` ```bash scripts/script.sh regulations ``` Regulatory framework and compliance ### `risks` ```bash scripts/script.sh risks ``` Risk factors and mitigation ### `instruments` ```bash scripts/script.sh instruments ``` Instruments and tools overview ### `strategies` ```bash scripts/script.sh strategies ``` Common strategies and approaches ### `glossary` ```bash scripts/script.sh glossary ``` Key terms and definitions ### `checklist` ```bash scripts/script.sh checklist ``` Due diligence checklist ### `help` ```bash scripts/script.sh help ``` ### `version` ```bash scripts/script.sh version ``` --- *Powered by BytesAgain | bytesagain.com | [REDACTED_SECRET]*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: sawyer-shi/mind-map-skill (version 1.0.2)

--- name: mind-map-skill description: Generate a PNG mind map from Markdown using free (auto), center, or horizontal layout. Use when user asks to produce a brain map/思维导图 from notes. argument-hint: "[free|center|horizontal] [markdown_file_or_inline_markdown]" disable-model-invocation: true allowed-tools: Bash(python mind_map/scripts/run_mind_map.py *), Read, Write --- # Mind Map Skill Generate mind map images locally from Markdown content. ## Invocation Use this skill directly: - `/mind-map-skill free docs/plan.md` - `/mind-map-skill center "# 项目\n## 模块\n- A\n- B"` - `/mind-map-skill horizontal notes.md` Arguments are mapped as: - `$0`: layout type (`free`, `center`, `horizontal`) - `$1`: markdown file path or inline markdown text If layout argument is missing, use `free`. ## Execution steps 1. Parse `$ARGUMENTS`. 2. Resolve markdown input: - If `$1` is a file path, read file content. - Otherwise treat `$1` as inline markdown. 3. Run from repository root (`D:\Work\skills\mind_map`): ```bash python mind-map-skill/scripts/run_mind_map.py --layout <layout> --input "<file-or-markdown>" --output-dir mind_map_output ``` By default, output files are grouped by date under `mind_map_output/YYYY-MM-DD/`. Use `--flat-output` to write directly into `mind_map_output/`. 4. Report: - selected layout - output PNG path - input markdown saved path (same dated folder) - node count and depth ## Layout behavior - `center`: radial structure (equivalent to mind_map_center capability) - `horizontal`: left-to-right structure (equivalent to mind_map_horizontal capability) - `free`: auto select based on complexity (equivalent to mind_map_free capability) Auto rule in free mode: - choose `center` when `depth <= 4` and `total_nodes <= 100` - otherwise choose `horizontal` ## Supporting files - Runtime engine: `mind-map-skill/scripts/mind_map_generator.py` - Runner script: `mind-map-skill/scripts/run_mind_map.py` - Example markdown: `mind-map-skill/examples/sample.md` - Parameter schema (optional compatibility): `mind-map-skill/skill.yaml` - Detailed reference: `mind-map-skill/reference.md` - Usage examples: `mind-map-skill/examples.md` ## Font behavior - On first run, the skill attempts to download a CJK font to local cache: `~/.cache/mind-map-skill/chinese_font.ttc`. - Download source in repository: `resources/chinese_font.ttc` (not part of skill upload package). - If download is unavailable, it falls back to prioritized system CJK fonts (Linux/macOS/Windows specific order). ## Project metadata - **Author**: [@sawyer-shi](https://github.com/sawyer-shi) - **Email**: [REDACTED_SECRET]


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: aipoch-ai/facs-gating-viz-style (version 0.1.0)

--- name: facs-gating-viz-style description: Beautify FACS gating plots for publication version: 1.0.0 category: Visual tags: [] author: AIPOCH license: MIT status: Draft risk_level: Medium skill_type: Tool/Script owner: AIPOCH reviewer: '' last_updated: '2026-02-06' --- # FACS Gating Viz Style Beautify flow cytometry gating plots for publication. ## Parameters | Parameter | Type | Default | Required | Description | |-----------|------|---------|----------|-------------| | `--data`, `-d` | string | - | Yes | FCS file path | | `--style`, `-s` | string | contour | No | Plot style (contour, density, dot) | ## Usage ```bash python scripts/main.py --data fcs_file.fcs --style contour ``` ## Features - Contour plots - Density visualization - Publication-ready styling ## Risk Assessment | Risk Indicator | Assessment | Level | |----------------|------------|-------| | Code Execution | Python/R scripts executed locally | Medium | | Network Access | No external API calls | Low | | File System Access | Read input files, write output files | Medium | | Instruction Tampering | Standard prompt guidelines | Low | | Data Exposure | Output files saved to workspace | Low | ## Security Checklist - [ ] No hardcoded credentials or API keys - [ ] No unauthorized file system access (../) - [ ] Output does not expose sensitive information - [ ] Prompt injection protections in place - [ ] Input file paths validated (no ../ traversal) - [ ] Output directory restricted to workspace - [ ] Script execution in sandboxed environment - [ ] Error messages sanitized (no stack traces exposed) - [ ] Dependencies audited ## Prerequisites No additional Python packages required. ## Evaluation Criteria ### Success Metrics - [ ] Successfully executes main functionality - [ ] Output meets quality standards - [ ] Handles edge cases gracefully - [ ] Performance is acceptable ### Test Cases 1. **Basic Functionality**: Standard input → Expected output 2. **Edge Case**: Invalid input → Graceful error handling 3. **Performance**: Large dataset → Acceptable processing time ## Lifecycle Status - **Current Stage**: Draft - **Next Review Date**: 2026-03-06 - **Known Issues**: None - **Planned Improvements**: - Performance optimization - Additional feature support


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