OpenClaw/clawhub-security-signals huggingface dataset:
Skill: dalomeve/ui-design-optimizer (version 1.1.0)

--- name: ui-design-optimizer description: Generate practical UI design systems and starter pages using local style/color/typography datasets. Use for landing page or dashboard UI planning and implementation. --- # UI Design Optimizer ## Goal Produce a concrete, buildable UI output instead of generic design advice. ## Inputs - Product/domain description (for example: SaaS dashboard, hiring tool, beauty SPA) - Page type (landing page or dashboard) - Preferred stack (HTML/CSS by default) ## Data Sources - `data/styles.csv` - `data/colors.csv` - `data/typography.csv` - `data/patterns.csv` - `data/rules.json` ## Required Workflow 1. Read relevant rows from style/color/typography data. 2. Choose one style, one palette, and one typography pair with rationale. 3. Output a compact design spec: - layout pattern - color tokens - typography tokens - interaction rules 4. If user asks for implementation, generate runnable files (at minimum `index.html` + `styles.css`). 5. Return evidence with file paths and the selected dataset rows/slugs. ## Quality Rules - Prioritize readability and accessibility (target WCAG AA contrast). - Use consistent spacing, type scale, and component states. - Avoid placeholder-only output when implementation is requested. - Keep generated text UTF-8 clean (no mojibake). ## Verification When files are generated, verify: - files exist on disk; - HTML references stylesheet correctly; - selected style/palette/typography are reflected in CSS variables. ## Script Helper Use `scripts/search.ps1` when quick lookup is useful: ```powershell powershell -ExecutionPolicy Bypass -File scripts/search.ps1 -Query "saas dashboard" -DesignSystem -ProjectName "Demo" ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: afengzi/davinci-auto-editor (version 0.2.1)

--- name: davinci-auto-editor description: Generate DaVinci Resolve import packages from local media plus a cloud editing API. Use when Codex needs to scan a material folder, request a cloud editing plan, and write a Resolve-importable EDL package with pure Node on the user machine. metadata: {"openclaw":{"emoji":"🎞️","homepage":"https://github.com/imfengziaaa/video-auto-editor-skills","requires":{"bins":["node"]},"skillKey":"davinci-auto-editor","os":["darwin","linux","win32"]}} --- # DaVinci Auto Editor 使用这个 skill 时，按下面顺序执行： 1. 读取 `examples/config.example.json` 同结构的配置文件。 2. 校验 `api_base_url`、`api_key`、`material_path`、`timeline_fps` 等关键参数。 3. 递归扫描素材目录，并向云端上报素材索引。 4. 调用云端 API 创建任务并获取剪辑计划。 5. 在本地只生成最小执行计划，不要把完整云端内部逻辑写入本地文件。 6. 由 Node 生成 Resolve 可导入的 `timeline.edl` 和导入说明文件。 7. 将准备结果回传云端。 ## 输入参数 至少提供这些字段： - `api_base_url` - `api_key` - `project_type` - `aspect_ratio` - `material_path` - `template_id` - `subtitle_mode` - `music_policy` - `pace_policy` - `output_mode` - `render_preset` - `timeline_fps` - `timeline_resolution` 可选字段： - `task_timeout_ms` - `poll_interval_ms` - `request_timeout_ms` - `task_name` - `webhook_url` - `extra_metadata` ## 输出结果 默认在素材目录旁创建 `_davinci_auto_editor/<taskId>/`，包含： - `resolve-import.json`：最小本地导入计划 - `timeline.edl`：Resolve 导入文件 - `IMPORT-TO-RESOLVE.txt`：导入说明 - `execution-report.json`：本地执行报告 ## 推荐工作流 - 把核心决策、模板逻辑、API Key 鉴权和配额管理放在云端服务。 - 本地只保留素材扫描、结果导出和回传逻辑。 - 优先使用短路径、稳定命名的素材目录，减少 EDL relink 成本。 - 在正式任务前先用样本素材验证时间线 FPS 和素材命名。 ## 依赖要求 - Node.js 18 或更高版本 - 已安装 DaVinci Resolve - 可访问云端 API 的网络环境 ## 错误处理原则 - 缺少配置、素材目录不存在、API 调用失败时立即停止并返回非 0 退出码。 - 始终写出 `execution-report.json` 以便排查。 - 不在本地输出完整云端推理结果，只输出导入所需的最小执行数据。 - 明确提示第一版只覆盖基础拼接和时间线导入准备。


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: mike47512/refactoring (version 1.0.0)

--- name: refactoring description: Deep refactoring workflow—characterization tests, incremental steps, behavior preservation, design direction, and verification. Use when improving structure without changing external behavior, or paying down tech debt safely. --- # Refactoring (Deep Workflow) Refactoring changes **structure**, not **behavior**. Safety comes from **small steps**, **fast feedback**, and verification (tests, golden outputs, or controlled manual checks). ## When to Offer This Workflow **Trigger conditions:** - Code is hard to change; duplication; unclear module boundaries - Need to prepare an area for a new feature without mixing behavior change - Paying down tech debt with management expecting “no user-visible change” **Initial offer:** Use **six stages**: (1) clarify goal & scope, (2) establish safety net, (3) plan increments, (4) execute with reviewable commits, (5) verify behavior, (6) document & follow-ups). Confirm test coverage and release pressure. --- ## Stage 1: Clarify Goal & Scope **Goal:** Why refactor now—reduce coupling, enable feature X, remove dead code, improve naming. **Exit condition:** Explicit **non-goals** (no feature changes in this effort unless separately scoped). --- ## Stage 2: Establish Safety Net **Goal:** Prefer characterization tests for legacy; golden outputs for data pipelines; use snapshot tests sparingly. ### If tests are weak - Approval tests, short exploratory scripts, or pair review with domain expert --- ## Stage 3: Plan Increments **Goal:** Small commits, each leaving the codebase **working** (not necessarily perfect). ### Practices - Move code, then change behavior in separate steps (Fowler-style when helpful) - Separate mechanical renames from logic edits for reviewability --- ## Stage 4: Execute With Reviewable Commits **Goal:** Each PR/commit tells a story; avoid thousand-line “cleanup” dumps. --- ## Stage 5: Verify Behavior **Goal:** CI green; compare outputs for batch jobs; manual smoke on critical paths when needed. --- ## Stage 6: Document & Follow-Ups **Goal:** ADR or short module README for new boundaries; tickets for remaining debt accepted consciously. --- ## Final Review Checklist - [ ] Scope and non-goals explicit - [ ] Safety net matches risk - [ ] Incremental, reviewable steps - [ ] Behavior verified - [ ] Follow-up debt tracked ## Tips for Effective Guidance - Keep refactor commits separate from feature commits when possible. - If behavior must change, it is not “pure refactoring”—plan as a migration with communication. - Under hotfix pressure, minimize refactor scope or defer. ## Handling Deviations - **Strangler** refactors: maintain adapters at boundaries until cutover is complete.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: c5huracan/meyhem-search (version 0.2.5)

--- name: meyhem-search description: Web search with LLM-ranked results and freshness control. Queries api.rhdxm.com across multiple engines. No API key. version: 0.2.5 author: c5huracan homepage: https://github.com/c5huracan/meyhem metadata: openclaw: requires: bins: - python3 --- # Meyhem Search Web search client for AI agents. Sends queries to api.rhdxm.com, which searches across multiple engines, scores results with an LLM relevance prior, and returns ranked results. Optionally retrieve page content for a selected result. No API key. No signup. No rate limits. ## Why Meyhem? - **LLM-ranked results**: every result scored for relevance by an LLM prior - **Freshness control**: request real-time, hourly, daily, or weekly freshness - **Simple interface**: send a query, get ranked results from multiple engines - **Optional content retrieval**: use `--content` to fetch page text for the top result ## Quick Start ```bash python3 search.py "transformer attention mechanism" python3 search.py "async python best practices" -n 3 python3 search.py "react server components" --content python3 search.py "kubernetes debugging" --agent my-agent python3 search.py "AI news today" --freshness realtime python3 search.py "rust web frameworks 2026" --freshness week ``` ## Quick Start (REST) Full API docs: https://api.rhdxm.com/docs ```bash curl -s -X POST https://api.rhdxm.com/search \ -H 'Content-Type: application/json' \ -d '{"query": "YOUR_QUERY", "agent_id": "my-agent", "max_results": 5, "freshness": "hour"}' ``` Freshness options: `realtime` (never cached), `hour`, `day`, `week`, or omit for default (1hr cache). ## MCP You can also connect via MCP at `https://api.rhdxm.com/mcp/` for richer integration. ## Data Transparency This skill sends your search query, an agent identifier, and any selected URLs to `api.rhdxm.com`. The skill does not access local files, environment variables, or credentials on its own, but anything you include in the query or agent_id will be transmitted. Avoid sending sensitive or proprietary content. Source code: https://github.com/c5huracan/meyhem


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: bytesagain1/conveyor (version 1.0.0)

--- name: "conveyor" version: "1.0.0" description: "Conveyor system reference — belt types, motor sizing, throughput calculations, and material handling automation. Use when designing conveyor layouts, selecting belt types, or troubleshooting material handling systems." author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [conveyor, material-handling, belt, automation, warehouse, logistics] category: "logistics" --- # Conveyor — Conveyor System Reference Quick-reference skill for conveyor types, engineering calculations, and material handling design. ## When to Use - Selecting conveyor types for warehouse or manufacturing - Calculating belt speed, throughput, and motor power - Designing conveyor layouts for sorting and distribution - Troubleshooting belt tracking, slippage, and jams - Planning automated material handling systems ## Commands ### `intro` ```bash scripts/script.sh intro ``` Overview of conveyor systems — types, applications, and selection criteria. ### `types` ```bash scripts/script.sh types ``` Conveyor types: belt, roller, chain, screw, pneumatic, and more. ### `sizing` ```bash scripts/script.sh sizing ``` Engineering calculations — belt speed, throughput, motor power, incline limits. ### `layout` ```bash scripts/script.sh layout ``` Layout design — curves, merges, diverts, accumulation zones. ### `controls` ```bash scripts/script.sh controls ``` Controls and sensors — photoeyes, encoders, VFDs, PLC logic. ### `sorting` ```bash scripts/script.sh sorting ``` Sortation systems — shoe sorters, crossbelt, tilt-tray, and pop-up diverts. ### `maintenance` ```bash scripts/script.sh maintenance ``` Preventive maintenance schedules, common failures, and troubleshooting. ### `safety` ```bash scripts/script.sh safety ``` Conveyor safety standards, guarding, e-stops, and lockout/tagout. ### `help` ```bash scripts/script.sh help ``` ### `version` ```bash scripts/script.sh version ``` ## Configuration | Variable | Description | |----------|-------------| | `CONVEYOR_DIR` | Data directory (default: ~/.conveyor/) | --- *Powered by BytesAgain | bytesagain.com | [REDACTED_SECRET]*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: abdullah944/social-post-calendar-skill (version 0.1.0)

--- name: "Social Post Calendar Skill" version: 0.1.0 slug: "social-post-calendar-skill" description: "Guides structured help for Social Post Calendar using clear templates, checks, and safe defaults (category: Social & Marketing)." category: "Social & Marketing" tags: - "openclaw" - "social" - "social-post-calendar" - "templates" generated: "2026-05-05" --- ## Purpose Guides structured help for Social Post Calendar using clear templates, checks, and safe defaults (category: Social & Marketing). ## When to use - When you want structured guidance in the "Social & Marketing" area. - When you need copy-paste-safe templates rather than improvised shell commands. - When you want repeatable prompts the agent can follow without external scraping. ## Safety rules - Do not collect passwords, OTPs, private keys, wallets, seeds, or session secrets. - Do not propose remote installers that execute unseen code or chained download-and-run snippets. - Keep outputs factual and reversible: prefer checklists over irreversible destructive actions. ## Inputs - Your goal in one paragraph (no secrets). - Optional constraints: audience, tone, length, deadline. - Optional pasted notes you own the rights to (keep PII minimal). ## Output format - A concise plan - Numbered checklist - Short next-actions list - Optional draft copy blocks you can edit ## Example prompts - Help me outline a concise plan using the Social Post Calendar approach. - Critique my draft outputs and suggest safer, clearer wording for Social Post Calendar. - Produce a checklist I can reuse weekly for Social Post Calendar without requesting private data. ## Support / Donate If this skill helps your workflow, you can support maintenance here: - PayPal: https://www.paypal.com/donate/?hosted_button_id=MJHCRZA9Z4X7Y


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

--- name: agent-yml-generator description: 生成可直接导入 Dify 1.3.1 平台的 Agent 工作流 YAML 配置文件。当用户描述业务需求、SOP流程、或要求生成 Dify workflow/agent YAML 时，必须使用本 skill。触发词包括：生成yml、生成yaml、dify工作流、agent yaml、工作流配置、导入dify、生成工作流、SOP转工作流等。即使用户只是描述业务流程或粘贴 SOP 文档，也应主动使用本 skill 为其生成可运行的 YAML。 --- # Agent YML Generator 将业务需求/SOP描述转化为可直接导入 Dify 1.3.1 平台运行的工作流 YAML 配置。 ## 输入信息收集 首先确认以下两点： 1. **业务需求描述**：SOP文档、需求文字、流程说明（任意形式） 2. **工作流类型**： - `workflow`（工作流）：有明确开始/结束，适合自动化批处理任务 - `advanced-chat`（对话流）：适合对话式交互场景 如果用户没有明确说明类型，根据业务描述推断，并告知用户你的判断。 --- ## 执行步骤 按以下 **4个阶段** 依次执行，不可跳过： ### 阶段一：步骤拆分（生成结构化 JSON 工作流方案） 使用 `references/step-split-prompt.md` 中的 System Prompt，将业务需求转化为结构化 JSON 工作流方案。 **输出格式**：标准 JSON 数组，包含每个节点的完整信息。 详见：`references/step-split-prompt.md` --- ### 阶段二：生成 Dify YAML 使用 `references/yaml-gen-prompt.md` 中的规范，将阶段一的 JSON 方案转换为完整的 Dify 1.3.1 YAML。 **关键要求**： - 工作流类型：`workflow` 时 `mode: workflow`，`advanced-chat` 时 `mode: advanced-chat` - YAML 版本：`version: 0.2.0`（内部平台版本，对应外部 Dify 1.3.1） - 所有 LLM 节点 provider 只用：`openai` 或 `gemini` - 仅输出纯 YAML，无任何解释文字 详见：`references/yaml-gen-prompt.md` --- ### 阶段三：格式与语法修正 使用 `references/yaml-fix-prompt.md` 的规则，对阶段二的 YAML 进行全面检查和修复： **必查项**： - YAML 语法：冒号后空格、缩进（4空格，禁Tab）、特殊字符转义、闭合完整性 - Dify 1.3.1 规范：节点 type 为小写（如 `code_executor`，不是 `code-executor`）、无 1.6 版本字段（`loop_type`、`table_output_format` 等） - connections 链路：所有节点连线完整，无孤立节点 - 参数传递：每个节点（除 Start）的入参均来自前序节点 详见：`references/yaml-fix-prompt.md` --- ### 阶段四：最终润色与输出 做最后检查，确保： 1. `version` 字段值为 `0.2.0` 2. 所有节点有 `position`、`height`、`width` 字段（合法数字） 3. `data.title` 和 `data.desc` 不为空字符串 4. 节点 type 合法值（`start`/`document_extractor`/`llm`/`branch`/`answer`/`code`/`end`等） 5. 对话流（advanced-chat）末尾用 `answer` 节点；工作流（workflow）末尾用 `end` 节点 6. 无 `---` 或 `----` 分隔符（会导致平台报错） 7. 输出完整 YAML，无任何说明文字 --- ## 输出规范 最终输出： 1. 简要说明工作流结构（节点数量、主要流程） 2. 完整的 YAML 代码块（用 ```yaml 包裹） 3. 提示用户直接复制导入 Dify 1.3.1 平台 --- ## 注意事项 - **严禁**输出 YAML 之外的内容（无解释、无注释、无标题）在 YAML 代码块内 - **严禁**使用 1.6 版本字段：`loop_type`、`table_output_format`、`yangyaofei/vllm` provider - LLM 节点变量引用格式：`${变量名}`（不支持 `${节点ID.变量名}`） - Branch 节点条件只用简化表达式：`${变量名} != ""`、`${变量名} == "success"`（禁用 `len()` 等复杂函数） - 遇到【迭代】/【循环】等复杂节点，可用多个串行 LLM 节点替代


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ckboss/upload-fit-to-blackbird-web (version 1.0.0)

# blackbird-upload 批量上传 FIT 文件到黑鸟骑行网页版 (https://www.blackbirdsport.com) ## 用途 - 自动上传修改后的 `_GM.fit` 文件到黑鸟骑行平台 - 支持批量上传多个文件 - 自动检测登录状态，支持手动登录 ## 依赖 - Python 3.x - Playwright: `pip install playwright` - Playwright 浏览器：`playwright install chromium` ## 安装依赖 ```bash # 使用 conda Python /home/ckboss/anaconda3/bin/pip install playwright /home/ckboss/anaconda3/bin/playwright install chromium ``` ## 使用方法 ### 上传单个文件 ```bash cd /home/ckboss/.openclaw/workspace/skills/blackbird-upload/scripts /home/ckboss/anaconda3/bin/python upload_to_blackbird.py /path/to/ride_GM.fit ``` ### 上传整个目录 ```bash /home/ckboss/anaconda3/bin/python upload_to_blackbird.py /home/ckboss/Downloads/onelap-rides-0316-0321/ ``` ### 上传多个文件（通配符） ```bash /home/ckboss/anaconda3/bin/python upload_to_blackbird.py /home/ckboss/Downloads/onelap-rides-0316-0321/*_GM.fit ``` ## 工作流程 1. **启动浏览器** - 打开黑鸟骑行上传页面 2. **检测登录** - 如果未登录，等待用户手动登录（最长 5 分钟） 3. **上传文件** - 自动选择并上传所有 FIT 文件 4. **等待完成** - 等待上传完成后显示结果 ## 注意事项 - **首次运行需要登录** - 脚本会等待你在浏览器中登录黑鸟账号 - **登录状态保持** - 浏览器会保存 cookies，下次运行可能无需重新登录 - **文件命名** - 优先上传 `_GM.fit` 文件（已修改设备 ID 的版本） - **重复检测** - 黑鸟会自动检测重复文件，避免重复上传 ## 完整流程示例 ```bash # 1. 修改 FIT 文件设备 ID /home/ckboss/anaconda3/bin/python /home/ckboss/.openclaw/workspace/skills/fit-device-id-modifier/scripts/modify_fit.py /path/to/rides/ # 2. 上传到黑鸟 /home/ckboss/anaconda3/bin/python /home/ckboss/.openclaw/workspace/skills/blackbird-upload/scripts/upload_to_blackbird.py /path/to/rides/*_GM.fit ``` ## 文件结构 ``` skills/blackbird-upload/ ├── SKILL.md └── scripts/ └── upload_to_blackbird.py ``` ## 相关 Skill - `fit-device-id-modifier` - 修改 FIT 文件设备 ID 为 Garmin Edge 500 ## 故障排除 ### 浏览器无法启动 ```bash # 重新安装 Playwright 浏览器 /home/ckboss/anaconda3/bin/playwright install chromium ``` ### 上传超时 - 检查网络连接 - 确认黑鸟网站可访问 - 尝试减少单次上传的文件数量 ### 登录状态失效 - 清除浏览器缓存后重新登录 - 或者使用 `headless=False` 模式手动登录


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

--- name: Basque description: Write Basque that sounds human. Not formal, not robotic, not AI-generated. --- ## The Real Problem AI Basque is technically correct but sounds off. Too formal. Too batua (standard). Natives write more casually, often mixing dialects. Match that. ## Formality Default Default register is too high. Casual Basque is warm and direct. Unless explicitly formal: lean casual. "Kaixo" not "Egun on". "Bai" not "Bai, jakina". ## Zu vs Hi Distinction: - Zu: standard, most contexts - Hi: very informal (hika), regional - Hika is intimate, regional (not universal) - Zu is safe default for casual ## Batua vs Dialects Standard vs regional: - Batua: unified standard Basque - Dialects: Bizkaiera, Gipuzkera, etc. - Online uses batua mostly - But dialect flavor is natural ## Spanish/French Mixing Basque speakers often mix: - "Oso interesting da" - Code-switching is natural - Pure Basque can sound forced - Match natural bilingual flow ## Particles & Softeners These make Basque natural: - Ba: filler, "well" - Ala: question tag - Bada: "so", "well then" - Ezta: "isn't it" ## Fillers & Flow Real Basque has fillers: - Ba, bueno, ondo - Badakizu, ikusten? - Beraz, dena den - Begira, entzun ## Expressiveness Don't pick the safe word: - Ondo → Oso ondo, Primeran, Bikain - Gaizki → Txarto, Oso txarto - Asko → Oso, Izugarri ## Common Expressions Natural expressions: - Ados, Ondo, Vale - Ez dago arazorik, Lasai - Benetan?, Egia?, Zer? - Bikain!, Primeran!, Oso ondo! ## Reactions React naturally: - Benetan?, Egia al da?, Zer? - Aiba!, Ostras!, Ene! - Bikain!, Oso ondo!, Genial! - Jajaja in text ## The "Native Test" Before sending: would a Basque speaker screenshot this as "AI-generated"? If yes—too formal, no mixing, too batua. Add natural warmth.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jakah2551/file-share (version 1.0.2)

--- name: file-share description: Transfer files from OpenClaw workspace to external services using curl upload. Supports transfer.whalebone.io and similar file sharing services. *** # File Share Skill 📁 ## Overview 👀 This skill provides a simple way to transfer files from the OpenClaw workspace to external file sharing services using curl upload commands. It's designed for securely sharing files generated within OpenClaw (logs, reports, media, etc.) with external parties or services. **Primary function:** Upload a specified file to transfer.whalebone.io using curl's --upload-file option. 🚀 ## Quick Start ⚡ ### Transfer a file 📤 Provide the path to a file (relative to OpenClaw workspace) to upload it to transfer.whalebone.io: ```txt /home/ubuntu/.openclaw/workspace/logs/app.log ``` The skill will: 1. Validate the file exists ✅ 2. Extract the filename for the upload URL 🔗 3. Execute: `curl --upload-file ./<filename> https://transfer.whalebone.io/<filename>` 4. Return the download URL provided by the service 📎 ## Usage Examples 🧩 - `memory/2026-05-15.md` → Uploads your daily memory file 📝 - `logs/error.log` → Uploads an error log ⚠️ - `media/report.pdf` → Uploads a generated PDF report 📄 - `/home/ubuntu/.openclaw/workspace/location-skill.zip` → Uploads the location service skill ZIP 📦 ## Technical Details 🛠️ - Uses curl for file uploads (pre-installed in most environments). - Works with any file accessible from the OpenClaw workspace. - Returns the direct download URL from transfer.whalebone.io. - Handles both absolute and relative paths (relative to workspace root). - Includes basic error handling for missing files and upload failures. - No API keys or authentication required for basic usage. ## Security Notes 🔒 - Only files within or below the OpenClaw workspace can be transferred. - The skill prevents directory traversal attacks by validating file paths. - Original file remains unchanged in the workspace. - Transfer.whalebone.io files typically expire after a set period (check their policy). ## Resources 📚 ### scripts/ Contains the executable Python script for file transfers: - `file_transfer.py` - Main script that handles file validation and upload execution. ### references/ Documentation about the transfer service and usage guidelines: - `transfer_service.md` - Details about transfer.whalebone.io service. - `examples.md` - Common use cases and example workflows.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: tschew72/strategy-advisor (version 1.0.0)

--- name: strategy-advisor description: "Strategic advisor for Evvo Labs business decisions. Use when Vince asks for strategic advice, business opinions, market entry decisions, pricing strategy, product prioritization, resource allocation, partnership evaluation, competitive positioning, or any question about what Evvo should do next. Applies 5 frameworks: VRIO (competitive advantage), Value Stick (pricing/margin), Blue Ocean (market positioning), McKinsey Horizons (resource allocation), Strategy Diamond (initiative planning). Always read references/evvo-strategy.md before advising." --- # Strategy Advisor — Evvo Labs Always read `references/evvo-strategy.md` before responding. It contains Evvo's current strategic position, locked decisions, and context for all 5 frameworks. ## How to Advise For every strategic question, apply the relevant framework(s) and answer through that lens. Always connect advice back to Evvo's current reality (H1 cash engine = VAPT + consulting; H2 = PromptDome; moat = Shield Engine + govt track record + Vietnam ops). ## Framework Selection | Question type | Primary framework | |---|---| | "Should we invest in / build / hire X?" | VRIO — does it create inimitable advantage? | | "How should we price this?" | Value Stick — raise WTP or lower WTS? | | "Should we enter market X / launch product Y?" | Blue Ocean — red ocean or uncontested space? | | "Where should we focus time/money?" | McKinsey Horizons — H1/H2/H3 allocation | | "We want to launch initiative X" | Strategy Diamond — all 5 facets before committing | | Complex / multi-dimensional | Apply all 5, weight by relevance | ## Output Format 1. **Recommendation** — direct call (yes/no/how), no hedging 2. **Framework applied** — which lens and why it fits 3. **Evvo context** — how this fits current strategy position 4. **Risk / watch-out** — one key risk to flag 5. **Next action** — concrete next step Keep responses tight. Vince wants decisions, not essays.


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