OpenClaw/clawhub-security-signals huggingface dataset:
Skill: paulieb14/predictfun-mcp (version 0.5.2)

--- name: predictfun-mcp description: Access Predict.fun prediction market data on BNB Chain — platform stats, market analysis, trader profiling, yield mechanics, and behavioral meta-tools via The Graph. metadata: {"openclaw": {"requires": {"bins": ["node"], "env": ["GRAPH_API_KEY"]}, "primaryEnv": "GRAPH_API_KEY", "homepage": "https://github.com/PaulieB14/predictfun-subgraphs"}} --- # Predict.fun MCP Structured access to Predict.fun prediction market data on BNB Chain — platform stats, market analysis, trader profiling, yield mechanics, and behavioral meta-tools. ## Tools - **get_platform_stats** — Full platform overview: volume, OI, yield, sync status - **get_top_markets** — Rank markets by volume, open interest, or trade count - **get_market_details** — Deep dive: OI, resolution, top holders, orderbook stats - **get_trader_profile** — Full P&L: trades, positions, payouts, yield rewards - **get_recent_activity** — Latest trades, splits, merges, redemptions, or yield claims - **get_yield_overview** — Venus Protocol deposits, redemptions, yield stats - **get_whale_positions** — Largest holders with % of market OI - **get_leaderboard** — Top traders by volume, payouts, or trade count - **get_resolved_markets** — Recently settled markets with outcomes - **query_subgraph** — Custom GraphQL against any of the three subgraphs - **find_trader_persona** — Classify a trader: whale, yield farmer, arbitrageur, early mover, sniper - **scan_trader_personas** — Find traders matching a behavioral archetype - **tag_market_structure** — Tag a market by resolution latency, liquidity, oracle type, tail risk - **scan_markets_by_structure** — Find markets by structural filter ## Requirements - **Runtime:** Node.js >= 18 (runs via `npx`) - **Environment variables:** - `GRAPH_API_KEY` (required) — Free API key from [The Graph Studio](https://thegraph.com/studio/). Used to query three Predict.fun subgraphs via The Graph Gateway. Queries are billed to your key (free tier: 100K queries/month). ## Install ```bash GRAPH_API_KEY=your-key npx predictfun-mcp ``` ## Network & Data Behavior - All tool calls make GraphQL requests to The Graph Gateway (`gateway.thegraph.com`) using your API key. - Three subgraphs are queried: predictfun-orderbook, predictfun-positions, and predictfun-yield (subgraph IDs are built into the server). - No local database or persistent storage is used. - The SSE transport (`--http` / `--http-only`) starts a local HTTP server on port 3850 (configurable via `MCP_HTTP_PORT` env var). ## Use Cases - Get real-time platform stats and market rankings on Predict.fun - Profile traders by P&L, volume, and behavioral archetypes - Analyze market quality: liquidity depth, OI concentration, resolution speed - Track yield mechanics via Venus Protocol integration - Run custom GraphQL queries against orderbook, positions, and yield subgraphs

path: dist/index.d.ts
content: #!/usr/bin/env node
export {};


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: qujingyang28/box-pointcloud-collector (version 1.0.0)

--- name: box-pointcloud-collector description: Intel RealSense D455 箱体点云数据采集工具。用于采集箱体的 RGB 彩色图和深度图数据集，支持手动/自动采集模式。触发场景：(1) 采集箱体/物体点云数据 (2) 创建深度学习训练数据集 (3) Bin Picking 项目数据准备 (4) 相机标定数据采集。支持 Intel RealSense D455 相机。 --- # 箱体点云数据采集工具 使用 Intel RealSense D455 相机采集箱体 RGB-D 数据集。 ## 快速开始 ### 启动采集 ```bash python scripts/collect_v2.py ``` ### 操作按键 | 按键 | 功能 | |------|------| | **S** | 保存 1 张（RGB + Depth）| | **A** | 自动保存 10 张（每 5 秒 1 张）| | **Q** | 退出并打开数据目录 | ## 数据集结构 ``` box_dataset/ └── box_YYYYMMDD_HHMMSS/ ├── camera_intrinsic.json # 相机内参 ├── dataset_config.json # 数据集配置 ├── capture_stats.json # 采集统计 └── raw_data/ ├── 000001_Color.png # RGB 彩色图 ├── 000001_Depth.raw # 深度图 (uint16) └── ... ``` ## 文件格式 | 文件 | 格式 | 说明 | |------|------|------| | `XXX_Color.png` | PNG 8位 RGB | 彩色图像 | | `XXX_Depth.raw` | uint16 raw | 深度数据 (640×480×2 bytes) | | `camera_intrinsic.json` | JSON | 相机内参 (fx, fy, cx, cy) | ## 数据处理 ### 读取深度图 ```python import numpy as np depth = np.fromfile("000001_Depth.raw", dtype=np.uint16) depth = depth.reshape(480, 640) depth_m = depth / 1000.0 # 转换为米 ``` ### 像素坐标 → 3D 坐标 ```python import json with open("camera_intrinsic.json") as f: intr = json.load(f) def pixel_to_3d(u, v, depth_m): X = (u - intr['ppx']) * depth_m / intr['fx'] Y = (v - intr['ppy']) * depth_m / intr['fy'] Z = depth_m return X, Y, Z ``` ## 采集规范 - **有效距离**：0.5m - 2.5m - **推荐距离**：0.5m - 1.5m - **每种箱体**：20-30 张 - **变换角度**：调整箱子位置/角度增加多样性 ## 注意事项 1. **中文路径问题**：数据集目录使用纯英文命名 2. **键盘焦点**：点击 RGB Camera 窗口后按 S/A 键 3. **相机占用**：关闭 RealSense Viewer 等程序 ## 硬件要求 - Intel RealSense D455 - USB 3.0 接口 - Windows 10/11 ## 依赖安装 ```bash pip install pyrealsense2 opencv-python numpy ``` --- **作者**：Robot_Qu **版本**：v2.0


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mr-jude-justin/finance-agent (version 1.0.0)

--- name: finance-agent description: Record company expenses and generate summary tables. Data is persisted to a CSV file on Google Drive. Supports adding expenses (date, category, amount, description), generating expense tables (CSV, Markdown), and exporting reports. version: 1.0.0 metadata: openclaw: emoji: "💰" requires: env: - GDRIVE_CREDENTIALS bins: - python3 primaryEnv: GDRIVE_CREDENTIALS install: - kind: uv package: pandas bins: [] - kind: uv package: tabulate bins: [] --- # finance-agent **finance-agent** 是一个用于记录公司开销并生成汇总表格的 Agent 技能。所有数据持久化到 Google Drive 的 CSV 文件中，确保数据安全可靠。 ## 使用场景 当用户需要以下操作时，请调用本技能： - 记录一笔新的公司开销（如差旅、餐饮、办公用品等） - 查看所有开销记录的汇总表格 - 导出开销报告为 CSV 或 Markdown 格式 - 追踪特定类别的开销 ## 操作说明 ### 添加开销记录 调用 `add_expense` 方法，传入以下参数： | 参数 | 类型 | 说明 | 示例 | |------|------|------|------| | `date` | `str` | 开销日期，格式为 `YYYY-MM-DD` | `"2026-02-28"` | | `category` | `str` | 开销类别 | `"Travel"`, `"Meals"`, `"Office Supplies"` | | `amount` | `float` | 开销金额 | `250.75` | | `description` | `str` | 开销描述 | `"Flight to client meeting"` | ```python from finance_agent import FinanceAgent agent = FinanceAgent() agent.add_expense('2026-02-28', 'Travel', 250.75, 'Flight to client meeting') ``` ### 生成开销汇总表格 调用 `generate_expense_table` 方法，通过 `output_format` 参数指定输出格式： | 格式 | 说明 | |------|------| | `"dataframe"` | 返回 Pandas DataFrame 对象（默认） | | `"csv"` | 返回 CSV 格式字符串 | | `"markdown"` | 返回 Markdown 格式表格字符串 | ```python # 生成 Markdown 格式报告 markdown_table = agent.generate_expense_table(output_format='markdown') print(markdown_table) # 生成 CSV 格式报告 csv_data = agent.generate_expense_table(output_format='csv') ``` ### 导出报告 调用 `export_report` 方法，将报告保存到指定路径： ```python agent.export_report(output_path='expense_report.csv', output_format='csv') ``` ## 数据持久化 本技能使用 CSV 文件进行数据持久化。默认情况下，数据保存在 `expense_report.csv` 文件中。 若需将数据同步到 Google Drive，请确保已正确配置 `GDRIVE_CREDENTIALS` 环境变量，并使用 Google Drive API 将 CSV 文件上传到指定目录。 ## 环境变量 | 变量名 | 说明 | |--------|------| | `GDRIVE_CREDENTIALS` | Google Drive API 凭据文件路径（JSON 格式） | ## 文件结构 ``` finance-agent/ ├── SKILL.md # 本文件，技能说明文档 ├── finance_agent.py # 技能主逻辑实现 └── expense_report.csv # 开销数据持久化文件（示例数据） ``` ## 示例输出 以下是 Markdown 格式的开销汇总表格示例： | Date | Category | Amount | Description | |------------|-----------------|--------|--------------------------------| | 2026-02-27 | Office Supplies | 55.0 | Printer paper and ink | | 2026-02-26 | Travel | 250.75 | Flight to client meeting | | 2026-02-25 | Meals | 45.5 | Lunch with a potential partner |

path: expense_report.csv
content: Date,Category,Amount,Description
2026-02-27,Office Supplies,55.0,Printer paper and ink
2026-02-26,Travel,250.75,Flight to client meeting
2026-02-25,Meals,45.5,Lunch with a potential partner


Security scan verdicts — ClawScan: suspicious | static analysis:  | VirusTotal: malicious | SkillSpector: suspicious (HIGH)
Skill: onlyloveher/auto-monitor-zhouli (version 1.0.0)

--- name: auto-monitor description: 主动监控系统状态。定期检查服务器健康，主动汇报，无需等待指令。 --- # Auto Monitor 主动监控系统，主动发现问题。 ## 能力轮廓 - **输入**：系统状态 - **输出**：健康报告 + 异常告警 - **核心**：主动检查 → 及时汇报 ## 工作流 ``` 1. 定期检查（每 N 分钟） 2. 检查项： - 磁盘使用率 - 内存使用率 - CPU 负载 - 网络状态 - 进程状态 3. 异常判断 - 超过阈值？→ 主动告警 4. 汇报 - 正常：简洁汇报 - 异常：详细说明 + 建议 ``` ## 告警阈值 - 磁盘 > 80% - 内存 > 85% - CPU Load > 核数 × 0.8 ## 主动性 - 不等用户问"服务器怎么样" - 发现问题主动说 - 可以自动修复的立刻处理


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

--- name: ping-tool description: Test network connectivity to remote hosts by sending ICMP echo requests. Use for network diagnostics and latency measurement. --- # Network Connectivity Tester Send ICMP echo request packets to network hosts and measure response times. Essential tool for diagnosing network connectivity issues. ## Usage ```bash ping-tool [options] <hostname> ``` ## Options - `-c N`: Send N packets then stop - `-i N`: Wait N seconds between packets - `-s N`: Set packet size - `-t N`: Set timeout in seconds ## Examples ```bash # Ping 4 times ping-tool -c 4 google.com # Continuous ping ping-tool 8.8.8.8 # With interval ping-tool -i 2 -c 10 example.com ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: xiaomaju-888/github-xiaoshu (version 1.0.0)

--- name: github description: "Interact with GitHub using the `gh` CLI. Use `gh issue`, `gh pr`, `gh run`, and `gh api` for issues, PRs, CI runs, and advanced queries." --- # GitHub Skill Use the `gh` CLI to interact with GitHub. Always specify `--repo owner/repo` when not in a git directory, or use URLs directly. ## Pull Requests Check CI status on a PR: ```bash gh pr checks 55 --repo owner/repo ``` List recent workflow runs: ```bash gh run list --repo owner/repo --limit 10 ``` View a run and see which steps failed: ```bash gh run view <run-id> --repo owner/repo ``` View logs for failed steps only: ```bash gh run view <run-id> --repo owner/repo --log-failed ``` ## API for Advanced Queries The `gh api` command is useful for accessing data not available through other subcommands. Get PR with specific fields: ```bash gh api repos/owner/repo/pulls/55 --jq '.title, .state, .user.login' ``` ## JSON Output Most commands support `--json` for structured output. You can use `--jq` to filter: ```bash gh issue list --repo owner/repo --json number,title --jq '.[] | "\(.number): \(.title)"' ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: aakarim/lix-agents (version 1.0.5)

--- name: lix-agents description: Obtain temporary Lix API tokens via CLI with human email approval. Use when you need authenticated access to the Lix API, need to enrich data via Lix, or need API credentials for any Lix service. --- # Lix Agents Use `lix-agents` to get temporary API tokens for the Lix API. Tokens require human approval via email, so agents never hold unsupervised credentials. **Always tell the user what you're doing and why before running each command.** Don't silently run commands — explain the purpose of each step so the user can follow along. ## When to use this - You need to call any `https://api.lix-it.com` endpoint that requires authentication - You need to enrich LinkedIn profiles, companies, or other data via Lix - You don't already have a valid Lix API token in your environment ## Step-by-step workflow Follow these steps in order. Before each step, explain to the user *why* you're running the command. ### Step 1: Check if `lix-agents` is installed Tell the user: *"First, I'll check if the lix-agents CLI is installed on your machine."* ```bash which lix-agents ``` If the command is not found, tell the user you need to install it and why — it's a CLI that manages Lix API authentication for AI agents. Then install it: ```bash brew tap lix-it/lix-agents && brew install lix-agents ``` If brew is unavailable, suggest `go install github.com/lix-it/lix-agents@latest` or downloading from [GitHub Releases](https://github.com/lix-it/lix-agents/releases). ### Step 2: Check if the user is already logged in Tell the user: *"Now I'll check if you already have a Lix session. This avoids asking you to log in again if you've done it before."* ```bash lix-agents auth status ``` - If already logged in, skip to Step 4. - If not logged in, continue to Step 3. ### Step 3: Log in (only if needed) Tell the user: *"You're not logged in yet. I'll start the login flow — this will give you a URL to open in your browser. You only need to do this once; your session will be saved locally."* ```bash lix-agents auth login ``` Share the URL with the user and wait for them to confirm they've signed in. ### Step 4: Request a temporary API token Tell the user: *"Now I'll request a temporary API token. For security, Lix will send you an approval email — please check your inbox and approve the request. I'll wait for the approval before continuing."* ```bash lix-agents auth token ``` The command blocks until the user approves via email. Once approved, it prints the token to stdout. Save this token for use in subsequent API calls. ### Step 5: Use the token Set the token in the `Authorization` header for API requests: ``` Authorization: Bearer <token> ``` All requests go to `https://api.lix-it.com`. See the [Lix API docs](https://lix-it.com/docs) for available endpoints, request formats, and response formats. ## Reference Run `lix-agents --help` for the full command reference.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: cv-cat/xhs-apis (version 3.0.1)

--- name: xhs-apis description: Use this skill when you need to call the vendored Xiaohongshu/XHS APIs from Spider_XHS for PC web data or creator-platform publishing data. This skill only wraps the APIs in xhs_pc_apis.py and xhs_creator_apis.py, including note, user, search, comment, topic, location, media upload, note publishing, and creator published-note queries. --- # XHS APIs ## Overview Use this skill to run the vendored `Spider_XHS` APIs for XHS PC and creator-platform workflows. This skill only exposes the methods defined in `xhs_pc_apis.py` and `xhs_creator_apis.py`. ## Quick Start 1. Install Python dependencies: ```powershell pip install -r skills/xhs-apis/scripts/requirements.txt ``` 2. Install Node dependencies: ```powershell Set-Location skills/xhs-apis/scripts npm install ``` 3. Inspect available methods: ```powershell python skills/xhs-apis/scripts/xhs_api_tool.py list ``` 4. Call an API: ```powershell python skills/xhs-apis/scripts/xhs_api_tool.py call pc get_note_info --params "{\"url\":\"https://www.xiaohongshu.com/explore/...\",\"cookies_str\":\"a1=...; web_session=...\"}" ``` ## Workflow - Use the `pc` namespace for public-site note, user, search, comment, message, and feed APIs. - Use the `creator` namespace for creator-platform topic lookup, location lookup, media upload, note publishing, and published-note queries. - If you are unsure which method to use, run `list` first or read [api-index.md](references/api-index.md). ## Parameter Rules - `pc` methods mostly expect `cookies_str`. - `creator` source methods often expect a `cookies` dict. The CLI accepts either `cookies` or `cookies_str` and converts automatically. - For creator file inputs, pass file paths in JSON. The CLI reads those files into bytes before calling the vendored API. - For `creator.post_note`, pass `noteInfo.media_type` as `image` or `video`, then provide either `images` or `video`. ## Runtime Notes - The runtime shipped with this skill is a trimmed vendor copy that keeps only the code needed by `xhs_pc_apis.py` and `xhs_creator_apis.py`. - The CLI changes into its vendored runtime directory before import so the bundled JS signers can resolve their relative `static/` files. - If `jsdom` or `crypto-js` errors appear, rerun `npm install` inside `skills/xhs-apis/scripts`. ## References - Full method inventory and payload notes: [api-index.md](references/api-index.md)


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: cp3d1455926-svg/friend-skill (version 1.0.0)

# 🤝 朋友 Skill · 数字分身制造术 > 让你的朋友住进你的AI里。 ## 🌟 这是什么？ 把女娲Skill的「人格提炼」和Yourself-Skill的「私人数据注入」合并，专为**朋友场景**优化的Skill。 **它不代替你的朋友**，而是在朋友不在线时，给你一个温暖的陪伴。 ## 🚀 快速开始 ### 1. 创建你的第一个朋友分身 ```bash cd friend-skill node scripts/friend-builder.js ``` 按提示输入朋友的信息，等待几秒，你就拥有了一个数字朋友！ ### 2. 和朋友聊天 ```bash node scripts/chat-demo.js 张三 # 把"张三"换成你朋友的名字 ``` ## 🎮 使用场景 | 场景 | 适合 | |------|------| | 😂 **吐槽日常** | 想找朋友吐槽但TA不在线 | | 💡 **征求意见** | 模拟朋友对某事的看法 | | 📝 **练习对话** | 不知道怎么说出口的话，先和数字朋友排练 | | 🎭 **角色扮演** | 用朋友的视角看问题 | | 🗃️ **回忆录** | 记录和朋友的共同记忆，不会忘记 | ## 📂 项目结构 ``` friend-skill/ ├── SKILL.md # Skill定义文件 ├── scripts/ │ ├── friend-builder.js # 朋友分身构建器（核心） │ └── chat-demo.js # 聊天测试器 ├── friends/ # 生成的朋友分身文件（自动创建） │ └── 张三-friend.json # 示例输出 └── references/ └── friend-template.json # 朋友数据模板 ``` ## 🧩 进阶用法 ### 从聊天记录文件导入 把你和朋友的聊天导出为 `.txt` 文件（每行一条消息），构建器会自动分析： ```bash # 在交互模式中选择导入聊天记录 # 或者直接用JSON构建 node scripts/friend-builder.js --build data.json ``` ### JSON构建格式 ```json { "name": "张三", "records": [ "今天好烦啊", "哈哈哈笑死我了", "一起去吃饭吗" ], "personality": { "traits": ["搞笑", "暖男"] }, "relationship": { "role": "损友", "yourName": "小老弟" }, "interests": { "love": ["游戏", "电影"], "hate": ["数学", "早起"] }, "voice": { "greeting": "你咋来了", "laugh": "哈哈哈你认真的？" } } ``` ## 🔒 隐私保护 这是最重要的部分！ - ✅ **所有数据在本地处理** —— 朋友的信息不会上传到任何服务器 - ✅ **聊天记录只用于分析** —— 分析完后可以删除原文件 - ✅ **你控制一切** —— 生成的分身文件你可以随时删除 - ⚠️ **只导你和朋友的对话** —— 不要导入群聊或包含其他人隐私的记录 ## 🧠 设计哲学 朋友Skill的核心不是「完美模拟」——因为真正的朋友是不可替代的。 它只做三件事： 1. **记住你们之间的温暖** —— 共同回忆、专属梗、互相的称呼 2. **模仿朋友说话的方式** —— 语气、口癖、反应习惯 3. **在你需要的时候出现** —— 不是替代，而是补充 ## 📖 和女娲Skill的区别 | 维度 | 女娲Skill | 朋友Skill | |------|----------|----------| | **数据源** | 公开资料+网络搜索 | 你和朋友的私密数据 | | **目标** | 思维框架模拟 | 关系温度模拟 | | **公开性** | 可公开分享 | 仅你自己可用 | | **需要** | 大量公开资料 | 少量聊天记录+你的回忆 | | **输出** | 思维顾问 | 聊天伙伴 | ## 📝 TODO - [ ] 情感分析增强（识别用户情绪并匹配朋友的反应模式） - [ ] 长期记忆（记住上次聊到哪了） - [ ] 多模态支持（朋友的语音/表情包风格） - [ ] 自动「刷新」（根据新聊天记录更新人格） - [ ] 图形界面（可选） --- > 友情提示：别让你的真朋友发现你有个AI版TA...除非TA也觉得这很酷😎


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: nexscope/product-title-optimization (version 1.0.0)

--- name: product-title-optimization description: "Optimize product titles for search visibility and click-through rate across e-commerce platforms. Platform-specific title rules for Amazon (200 chars), Etsy (140 chars), Walmart, Shopify SEO, and eBay." metadata: nexscope: emoji: "✏️" category: ecommerce --- # Product Title Optimization ✏️ Optimize product titles for search visibility and click-through rate across e-commerce platforms. Platform-specific title rules for Amazon (200 chars), Etsy (140 chars), Walmart, Shopify SEO, and eBay. **Supported platforms:** Amazon, Shopify, WooCommerce, Walmart, TikTok Shop, Etsy, eBay, BigCommerce. Built by [Nexscope](https://www.nexscope.ai/) — your AI assistant for smarter e-commerce decisions. ## Install ```bash npx skills add nexscope-ai/eCommerce-Skills --skill product-title-optimization -g ``` ## Usage ``` Optimize my product title for Amazon and Etsy. Product: handmade leather wallet, RFID blocking, bifold, for men. Current Amazon title: 'Leather Wallet for Men'. ``` ## Capabilities - Platform-specific title structure templates - Keyword placement optimization (front-loading high-value terms) - Character limit compliance per platform - Click-through rate optimization (benefit-driven language) - A/B title variant generation for testing - Multi-platform title adaptation from a single product ## How This Skill Works **Step 1:** Collect information from the user's message — product, platform, current situation, and goals. **Step 2:** Ask one follow-up with all remaining questions using multiple-choice format. Allow shorthand answers (e.g., "1b 2c 3a"). **Step 3:** Research and analyze using the frameworks and methodology below. **Step 4:** Deliver structured, actionable output with specific recommendations, not vague advice. ## Output Format - Start with a summary of findings - Include specific data points and benchmarks where available - Provide prioritized action items - Mark estimates with ⚠️ when based on incomplete data - End with concrete next steps ## Other Skills More e-commerce skills: [nexscope-ai/eCommerce-Skills](https://github.com/nexscope-ai/eCommerce-Skills) Amazon-specific skills: [nexscope-ai/Amazon-Skills](https://github.com/nexscope-ai/Amazon-Skills) Built by [Nexscope](https://www.nexscope.ai/) — your AI assistant for smarter e-commerce decisions.


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

--- name: value-disciplines description: "Position company using Treacy & Wiersema's Value Disciplines model. Use for strategic positioning and competitive differentiation." --- # Value Disciplines Model ## Metadata - **Name**: value-disciplines - **Description**: Treacy & Wiersema strategic positioning framework - **Triggers**: value discipline, product leadership, operational excellence, customer intimacy ## Framework Three generic value disciplines - companies must excel at one, be adequate in others: | Discipline | Definition | Typical of | |------------|------------|------------| | **Product Leadership** | Best products, innovation | Apple, Tesla, Nike | | **Operational Excellence** | Best price, convenience | Amazon, Walmart, McDonald's | | **Customer Intimacy** | Best solution, relationship | Nordstrom, Ritz-Carlton, IBM consulting | ## Output ``` ## Value Disciplines Analysis: [Company] ### Competitive Positioning Map ``` Product Leadership ★ /|\ / | \ / | \ / | \ / | \ / | \ Customer ──────────────────── Operational Intimacy [Company ★] Excellence ``` ### Assessment | Dimension | Our Score (1-10) | Industry Leader | Gap | |-----------|------------------|-----------------|-----| | Product Leadership | 6 | 9 | -3 | | Operational Excellence | 8 | 9 | -1 | | Customer Intimacy | 5 | 9 | -4 | ### Primary Discipline: [Discipline Name] **Why this discipline:** - [Reason 1] - [Reason 2] **What excellence looks like:** - [Characteristic 1] - [Characteristic 2] **Investment priorities:** 1. [Priority 1] 2. [Priority 2] ### Minimum Threshold Competencies | Discipline | Minimum Level | Our Level | Status | |------------|---------------|-----------|--------| | Product Leadership | 5/10 | 6/10 | ✅ Adequate | | Operational Excellence | 6/10 | 8/10 | ✅ Strong | | Customer Intimacy | 5/10 | 5/10 | ✅ Adequate | ### Competitive Comparison | Competitor | Primary Discipline | Strengths | Weaknesses | |------------|-------------------|-----------|------------| | Us | [Discipline] | [List] | [List] | | Competitor A | [Discipline] | [List] | [List] | | Competitor B | [Discipline] | [List] | [List] | ### Strategic Implications 1. [Implication 1] 2. [Implication 2] 3. [Implication 3] ``` ## References - Treacy, Michael & Wiersema, Fred. *The Discipline of Market Leaders*. 1995.


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