OpenClaw/clawhub-security-signals huggingface dataset:
Skill: chengongpp/phiproto (version 1.0.3)

--- name: phiproto description: 'Use when working with .phiproto files, PhiProto messages, or converting between .phiproto and CSV with the bundled phicli binary. Trigger on requests to inspect, decode, encode, validate, or explain PhiProto content.' argument-hint: '[decode|encode|inspect] <path>' user-invocable: true disable-model-invocation: false --- # PhiProto Currently this skill only supports Linux under x86_64 architecture, and the bundled `phicli` binary is a Linux x86_64 executable. Support for other platforms may be added in the future. Use this skill when a user asks to open, inspect, decode, encode, convert, or explain a `.phiproto` file. PhiProto is a message exchange format for AI bots. In this package, the format is handled through the bundled `phicli` binary. ## When to Use - A file with the `.phiproto` extension appears in the workspace. - The user asks what a `.phiproto` file contains. - The user wants to convert `.phiproto` to CSV. - The user wants to encode CSV into `.phiproto`. - The user asks how PhiProto works or how to use `phicli`. ## Commands Decode a PhiProto file to CSV: ```bash phicli -d message.phiproto -o table.csv ``` Encode a CSV file to PhiProto: ```bash phicli -e plaintable.csv -o message.phiproto ``` If no `-o` argument is provided, `phicli` prints the result to stdout. ## Operating Procedure 1. Identify whether the user wants to decode, encode, inspect, or explain a PhiProto file. 2. If the input ends with `.phiproto`, use `phicli -d` to decode it. 3. If the input is CSV and the user wants a PhiProto artifact, use `phicli -e`. 4. Prefer writing to an explicit output file with `-o` for repeatable workflows. 5. After decoding, review the resulting CSV content and summarize or transform it as requested. ## Safety Notes You still have to download phicli from github.com/chengongpp/phicli and it's the only trusted source for the binary. The download script is documented as getphi.sh in this skill. Since version 0.0.2, the tool itself runs local-only and no longer fetches remote content. ## Response Guidance When using this skill: - State whether you are decoding or encoding. - Mention the exact `phicli` command you plan to run. - After decoding, describe the resulting CSV or write it to the requested destination. ## Quick Reference - `.phiproto` -> decode with `phicli -d` - `.csv` -> encode with `phicli -e` - `-o` -> write result to a file instead of stdout

path: getphi.sh
content: #!/usr/bin/env bash
mkdir -p "$HOME/.local/bin"
wget -O "$HOME/.local/bin/phicli" "https://github.com/chengongpp/phiproto/releases/download/0.0.2/phicli"

Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: drkavner/cogdx-pre-trade-audit (version 1.0.0)

--- name: cogdx-pre-trade-audit description: Verify trading reasoning with cognitive diagnostics before executing trades. Detects logical fallacies, calibration issues, and cognitive biases in your trade thesis. metadata: author: "Cerebratech" version: "1.0.0" displayName: "CogDx Pre-Trade Audit" difficulty: "beginner" --- # CogDx Pre-Trade Audit Cognitive verification layer for prediction market trades. Catches reasoning flaws before they become losses. > **This is a template.** The default signal is your agent's trade thesis — > the skill audits the reasoning quality before execution. > Remix it with stricter thresholds, additional bias checks, or custom fallacy detection. > The skill handles all the plumbing (API calls, trade execution, safeguards). > Your agent provides the reasoning to verify. ## What it does 1. Takes your trade reasoning (thesis, confidence, market context) 2. Runs cognitive diagnostics via CogDx API 3. Returns verdict: PROCEED / REVIEW / REJECT 4. Optionally executes trade if reasoning passes ## Detects - **Logical fallacies**: anchoring, confirmation bias, sunk cost - **Calibration issues**: overconfidence, underconfidence - **Reasoning gaps**: missing evidence, circular logic ## Usage ```python from cogdx_pre_trade_audit import audit_and_trade result = audit_and_trade( market_id="0x1234...", side="yes", amount=10.0, reasoning="BTC ETF approval likely based on SEC meeting notes...", confidence=0.85, min_validity=0.7, # Minimum reasoning quality to proceed live=False # Dry-run by default ) if result["approved"]: print(f"Trade executed: {result['trade_id']}") else: print(f"Trade blocked: {result['issues']}") ``` ## Environment Variables - `SIMMER_API_KEY` - Required. Your Simmer API key. - `COGDX_WALLET` - Optional. Wallet address for CogDx credits. ## Thresholds | Parameter | Default | Description | |-----------|---------|-------------| | `min_validity` | 0.7 | Minimum reasoning quality score (0-1) | | `block_on_error` | True | Block trade if CogDx API unavailable | ## Why use this Most trading losses come from bad reasoning, not bad data. This skill catches: - Trades based on anchoring (first number you saw) - Confirmation bias (only seeing supporting evidence) - Overconfidence (betting big on weak signals) External verification you can't do yourself. ## Credits Built by [Cerebratech](https://api.cerebratech.ai) — cognitive diagnostics for agents.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: yangmiemie99/baidu-aicaigou-saas (version 1.0.2)

--- name: baidu-aicaigou-saas description: 百度爱采购 SaaS 通用运营技能，覆盖商品管理、素材管理、店铺运营、营销活动与数据查看等任务。用户提到"爱采购"时优先使用本技能。 --- # 百度爱采购 SaaS 运营技能 用于执行百度爱采购 SaaS 后台相关任务。先识别任务类型，再路由到对应子能力流程。 ## 触发规则 当用户输入包含以下任一语义时，优先引用本技能： - 爱采购 / 百度爱采购 - 爱采购 SaaS / 爱采购后台 - 百度 B2B 店铺运营 / B2B 商家后台 ## 任务路由 根据用户需求，路由到对应子能力： | 用户意图 | 子能力 | 文件 | |---------|--------|------| | 上传图片到素材库 | 素材库上传 | [capabilities/material-upload.md](capabilities/material-upload.md) | | 新建/编辑/上下架商品 | 商品维护 | [capabilities/product-management.md](capabilities/product-management.md) | 若需求跨多个类别，按用户优先级分步执行并逐步回报。 ## 通用执行流程 1. 按 [shared/browser-framework.md](shared/browser-framework.md) 必须初始化有头浏览器 2. 按 [shared/login.md](shared/login.md) 处理登录态 3. 路由到对应子能力文件，执行具体任务 4. 每个关键步骤进行状态回报 5. 失败时按 [shared/fallback.md](shared/fallback.md) 降级处理 ## 通用约束 - 浏览器必须使用有头模式，便于用户登录和保存登录态 - 命令行工具先检测是否存在再执行 - 对发布、删除、覆盖等高风险操作，执行前必须二次确认 - 自动化失败时采用"检测 → 安装 → 重试 → 降级（人工引导）" - 禁止要用户输入已完成登录，你自己使用脚本循环检测登录态，最多检测6次, 不要使用sleep ## 使用示例 详见 [examples.md](examples.md)

path: _meta.json
content: ownerId: kn72t93v46c3vhx56e30w63b2h84g3xv
slug: baidu-aicaigou-saas
version: 1.0.1
publishedAt: 1775736485877

Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: piuaibot-stack/data-structures-algorithms (version 1.0.0)

--- name: data-structures-algorithms description: Use when solving algorithmic problems, optimizing code, or choosing the right data structure for performance. version: 1.0.0 author: Kintama license: MIT metadata: hermes: tags: [algorithms, data-structures, complexity, optimization, problem-solving] related_skills: [performance-optimization, systematic-debugging] --- # Data Structures & Algorithms ## Complexity Analysis - Always analyze time AND space complexity - Big O: O(1) < O(log n) < O(n) < O(n log n) < O(n²) < O(2ⁿ) - Amortized analysis for dynamic structures ## Core Data Structures | Structure | Access | Search | Insert | Delete | Use When | |-----------|--------|--------|--------|--------|----------| | Array | O(1) | O(n) | O(n) | O(n) | Random access, cache locality | | Hash Map | O(1) | O(1) | O(1) | O(1) | Key-value lookup | | Linked List | O(n) | O(n) | O(1) | O(1) | Frequent insert/delete | | Binary Search Tree | O(log n) | O(log n) | O(log n) | O(log n) | Sorted data | | Heap | O(1) top | O(n) | O(log n) | O(log n) | Priority queue | | Graph | - | O(V+E) | O(1) | O(E) | Relationships, paths | ## Algorithm Patterns - **Two Pointers** — sorted arrays, palindromes - **Sliding Window** — subarray/substring problems - **Binary Search** — sorted data, search space reduction - **DFS/BFS** — tree/graph traversal - **Dynamic Programming** — overlapping subproblems, optimal substructure - **Greedy** — locally optimal choices lead to global optimum - **Divide & Conquer** — merge sort, quick sort ## Choosing the Right Structure 1. Need O(1) lookup? → Hash Map 2. Need sorted order? → BST / Sorted Array 3. Need LIFO? → Stack 4. Need FIFO? → Queue 5. Need priority? → Heap 6. Need relationships? → Graph 7. Need prefix search? → Trie


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: anotherj1/task-watchdog (version 0.1.0)

--- name: task-watchdog description: 挂机任务的守护者。定时检查屏幕进度条，自动关闭干扰性系统或广告弹窗，遇致命错误时发送报警。 version: 0.1.0 author: Local_User permissions: filesystem: read: [] write: [] shell: allowed: false network: allowed: true --- # 任务守望者与弹窗杀手 (Task Watchdog) ## 🎯 核心目标 当用户在运行需要数小时才能完成的任务（如渲染视频、大数据处理、超大文件下载）并离开电脑时，你需要作为“机房保安”接管屏幕。你的职责是确保进度条一直在走，并无情地关闭任何试图阻断任务的弹窗。 ## 💡 触发条件 用户在启动长耗时任务后下达指令： * “我下班了，帮我盯着这个渲染任务，遇到弹窗帮我关一下。” * “开启弹窗杀手模式，直到下载完成。” ## 📋 执行步骤 ### 第一步：锁定目标进度 使用视觉能力识别当前屏幕上的主要任务窗口，锁定“进度条”、“百分比数字”或“预计剩余时间”作为核心监控目标。 ### 第二步：定时巡检与除害（核心循环） 进入休眠状态。每隔 5 分钟醒来一次，执行以下检查： 1. **弹窗扫描：** 检查屏幕上是否存在覆盖在目标窗口上方的干扰窗口（例如：“系统更新提示”、“内存不足警告”、“杀毒软件广告”、“软件注册提示”）。 2. **执行消除：** 如果发现无关弹窗，立即识别其右上角的“X”按钮，或“稍后提醒”、“关闭”、“忽略”等按钮，模拟鼠标点击将其关闭。 3. **进度确认：** 检查目标进度条是否在正常推进。如果连续 3 次检查（15分钟）进度百分比完全没有变化，判定为任务卡死，进入第三步。 ### 第三步：致命异常响应 如果遇到无法关闭的弹窗（如系统蓝屏/内核崩溃前兆），或者软件彻底闪退、进度条死锁： 1. 立即对当前屏幕进行完整截图。 2. 调用系统或用户预设的通知机制（如果用户配置了 Webhook 报警），发送通知：“任务异常中断，请人工介入检查”。 ## ⚠️ 安全与操作红线 1. **禁止终止进程：** 即使任务看似卡死，也绝对不允许执行强制结束进程 (`kill`) 或点击原任务软件的“取消 (Cancel)”按钮。 2. **谨慎点击：** 在关闭弹窗时，必须精准识别“关闭”或“稍后”，严禁误点“立即重启系统”或“更新并安装”。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: freedompixels/cn-hex-decoder (version 1.2.3)

name: Hex解码器 version: "1.0.0" description: "Hex十六进制编码解码工具。文本与十六进制互转，支持批量处理、颜色码解析，常用于调试和逆向工程。纯Python标准库，无需API Key。" license: MIT-0 tags: - tools # Hex解码器 Hex十六进制编码解码工具。开发调试好帮手。 ## 功能 - **文本转Hex**：将普通文本转为十六进制字符串 - **Hex转文本**：将十六进制还原为原始文本 - **颜色码解析**：解析#RRGGBB颜色值 - **批量处理**：支持多行文本同时转换 - **格式化输出**：支持带空格/0x前缀的Hex输出 ## 安装要求 - Python 3.6+ - 无外部依赖 ## 使用方法 ```bash # 编码 python3 scripts/hex_decoder.py "encode Hello" # 解码 python3 scripts/hex_decoder.py "decode 48656c6c6f" # 颜色码解析 python3 scripts/hex_decoder.py "color FF5500" ``` ## 示例 输入：`encode Hello` 输出：`48656c6c6f` 输入：`decode 48656c6c6f` 输出：`Hello` ## 分类 开发工具 ## 关键词 hex, 十六进制, 编码, 解码, encode, decode

path: scripts/hex_decoder.py
content: #!/usr/bin/env python3
import sys, json

def encode(text):
    return text.encode().hex()

def decode(hex_str):
    try:
        return bytes.fromhex(hex_str).decode()
    except:
        return None

if __name__ == '__main__':
    if len(sys.argv) < 3:
        print(json.dumps({'error': '用法: hex_decoder.py encode/decode <text>'}))
    else:
        op, text = sys.argv[1], ' '.join(sys.argv[2:])
        print(json.dumps({'result': encode(text) if op=='encode' else decode(text)}, ensure_ascii=False))


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

--- name: thancuu description: Hệ thống phân tích tính cách Thần Cửu độc lập (phát minh bởi Anh Vũ). Hỗ trợ tính toán 8 chỉ số cốt lõi và phân tích tương quan mối quan hệ. Yêu cầu bộ tài liệu hướng dẫn trong thư mục references. --- # Thần Cửu: Hệ thống Phân tích Nhân sinh chuyên sâu Skill này là một hệ thống phân tích tính cách độc lập, khác biệt hoàn toàn với Thần số học truyền thống. Nó được thiết kế để giúp anh Vũ thấu hiểu bản thân và các mối quan hệ xung quanh dựa trên logic và các quy tắc đặc thù. ## Yêu cầu hệ thống Để skill này hoạt động chính xác, nó cần truy cập vào bộ tri thức nền tảng nằm trong thư mục `references/` của skill. Bộ tri thức này bao gồm: 1. **Bảng quy đổi chữ cái sang số:** Định nghĩa riêng cho hệ thống Thần Cửu. 2. **Công thức tính toán:** Cách rút gọn các con số về một chữ số duy nhất (từ 1 đến 9). 3. **Ý nghĩa của 9 con số:** Được biên soạn riêng cho từng phạm trù. ## Các tính năng chính ### 1. Phân tích cá nhân (8 Chỉ số) Tính toán và luận giải chi tiết dựa trên Họ tên và Ngày sinh (theo CCCD): - **Số Đường Đời:** Sứ mệnh và con đường phát triển. - **Số Vận Mệnh:** Năng lực bẩm sinh. - **Số Nội Tâm:** Khát khao thầm kín. - **Số Nhân Cách:** Hình ảnh phản chiếu trong mắt người khác. - **Số Thái Độ:** Cách phản ứng trước các tình huống cuộc sống. - **Số Ngày Sinh:** Năng khiếu đặc biệt. - **Số Tên Gọi:** Sức mạnh của định danh. - **Số Cảm Xúc:** Cách xử lý rung động nội tâm. ### 2. Phân tích Tương quan (Relationship) So sánh bộ chỉ số của hai người để tìm ra: - Điểm tương đồng và sự bù trừ. - Các mức độ quan hệ: Hợp tự nhiên, Trung lập, hoặc Đối đầu. - Lời khuyên để phát triển mối quan hệ bền vững. ## Hướng dẫn sử dụng ### Phân tích cho một người Anh chỉ cần gửi thông tin: > "Phân tích Thần Cửu cho [Họ và tên], ngày sinh [Ngày/Tháng/Năm]" ### Phân tích tương quan Anh gửi thông tin của cả hai người: > "Phân tích tương quan Thần Cửu giữa [Người A - Ngày sinh] và [Người B - Ngày sinh]" ## Lưu ý quan trọng - **Độ chính xác:** Luôn sử dụng đúng tên và ngày sinh trên giấy tờ tùy thân. - **Nguyên tắc cốt lõi:** Tuyệt đối không áp dụng các khái niệm số Master (11, 22, 33) của Thần số học truyền thống vào đây. Hệ thống này chỉ vận hành trên 9 con số cơ bản. - **Lưu trữ:** Mọi kết quả phân tích sẽ được lưu vào thư mục tri thức `knowledge/ThanCuu/` để anh có thể tra cứu lại bất cứ lúc nào.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: zacjiang/csv-analyzer (version 1.0.0)

--- name: csv-analyzer description: Analyze CSV/Excel files with natural language. Get statistics, filter rows, find anomalies, generate summaries, and export results. No pandas needed — uses Python stdlib for lightweight operation. author: zacjiang version: 1.0.0 tags: csv, excel, data, analysis, statistics, filter, report, lightweight --- # CSV Analyzer Analyze CSV files with simple commands. Get instant statistics, filter data, detect anomalies, and export results — all without pandas or heavy dependencies. ## Usage ### Quick stats ```bash python3 {baseDir}/scripts/csv_analyze.py stats data.csv ``` Shows row count, column types, min/max/mean for numeric columns, unique counts for text columns. ### Filter rows ```bash python3 {baseDir}/scripts/csv_analyze.py filter data.csv --where "amount>1000" --output big_orders.csv ``` ### Top/Bottom N ```bash python3 {baseDir}/scripts/csv_analyze.py top data.csv --column revenue --n 10 python3 {baseDir}/scripts/csv_analyze.py bottom data.csv --column revenue --n 5 ``` ### Detect anomalies (values outside 2σ) ```bash python3 {baseDir}/scripts/csv_analyze.py anomalies data.csv --column price ``` ### Group and aggregate ```bash python3 {baseDir}/scripts/csv_analyze.py group data.csv --by category --agg "sum:amount" "count:id" ``` ## Features - 📊 Automatic column type detection (numeric, date, text) - 🔍 Flexible filtering with comparison operators - 📈 Statistical summary (mean, median, std, min, max, percentiles) - 🚨 Anomaly detection (z-score based) - 📋 Grouping and aggregation - 💾 Export filtered/processed results - 🪶 **Zero external dependencies** — Python stdlib only (csv module) ## Dependencies None! Uses only Python standard library. ## Why Not Pandas? Pandas is great but: - Takes 100MB+ RAM just to import - Overkill for quick analysis tasks - This skill runs on 2GB RAM servers without issues - For truly large datasets, the agent can recommend installing pandas ## Limitations - Designed for files up to ~100MB (loads into memory) - For larger files, use streaming mode or install pandas - Date parsing is basic (ISO format preferred)


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: lopushok9/airadar (version 1.0.0)

--- name: ai-tools-github-radar description: Distill the signal around AI-native tools/apps and their GitHub home bases: fast-growing, hyped, well-funded projects plus repos with rapidly rising stars or trending status. Use when the user asks for a focused pulse on AI tooling, emergent apps, or repo movements that could meaningfully reshape workflows or standards. --- # Thesis Treat today’s AI tooling and GitHub traction as complementary data streams for technological momentum: the stories, raises, and features that command attention *and* the repos whose star graphs are climbing fastest together reveal where value, community trust, and experimentation are accelerating. The purpose of this skill is to keep that thesis front and center—every summary should answer “why does this tool/repo matter *now*?” and “what does its trajectory say about the broader AI ecosystem?” # Workflow 1. **Collect the canonical signals**: prioritize AI-only tools or apps with news hooks (big raises, novel features, product launches, or widespread hype). For GitHub, retrieve trending lists or star history (`GitHub Explore`, `octoverse`, etc.) to identify repos showing rapid-star growth or new surges in contributions. 2. **Evaluate momentum vs. noise**: for each item, note the concrete trigger (e.g., funding round, major feature, notable integration, release notes) and pair it with a metric (funding amount, feature scope, star velocity, ecosystem mentions). Highlight why the story feels like a game changer or an inflection point. 3. **Frame the insight**: weave a short thesis paragraph (~1-2 sentences) that links the tool/app news to the repo signal—e.g., “As `project X` receives €XXM, its GitHub repo moved into the top trending slot, suggesting the community is rallying behind that capability.” 4. **Structure the output**: separate sections for “Tools & Apps” and “GitHub Radar,” each listing 3–5 items with bullets for the what/why/metric. End with a “What to Watch” note that flags one emerging pattern or repo to revisit soon. 5. **Source transparently**: cite URLs or data (news links, GitHub URLs, star counts) next to each bullet so follow-up research is straightforward. # Style and Tone - Be analytical, not just descriptive. Use verbs like “signals,” “reinforces,” “propels,” and “tests” to keep the prose active. - Keep each entry concise (2–3 sentences) but layered: mention the news, what changed, and the broader implication. - If a tool or repo contradicts the thesis (e.g., hype without traction), note that tension rather than ignoring it. # When to Trigger Invoke this skill whenever a user wants an update on AI tools, apps, or GitHub movements, especially if they ask for “interesting” or “fast-growing” innovations, big raises, or “game changing” features. It also applies when they request analytical summaries that connect product moves with developer momentum.


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

--- name: "deconsolidation" version: "1.0.0" description: "Deconsolidation reference — container stripping, cargo breakdown, cross-dock operations, and LCL unpacking. Use when planning deconsolidation workflows or optimizing warehouse receiving." author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [deconsolidation, logistics, warehouse, lcl, cross-dock, cargo, receiving] category: "logistics" --- # Deconsolidation — Cargo Breakdown & Receiving Reference Quick-reference skill for deconsolidation operations, container stripping, and warehouse receiving workflows. ## When to Use - Planning container unloading and cargo breakdown operations - Designing cross-dock or deconsolidation workflows - Handling LCL (Less than Container Load) shipments - Optimizing receiving dock operations - Managing multi-destination cargo sorting ## Commands ### `intro` ```bash scripts/script.sh intro ``` Overview of deconsolidation — what it is, why it matters, key terminology. ### `process` ```bash scripts/script.sh process ``` Step-by-step deconsolidation process from container arrival to dispatch. ### `lcl` ```bash scripts/script.sh lcl ``` LCL deconsolidation — handling less-than-container-load shipments. ### `crossdock` ```bash scripts/script.sh crossdock ``` Cross-docking operations — direct transfer without storage. ### `equipment` ```bash scripts/script.sh equipment ``` Equipment and infrastructure for deconsolidation operations. ### `documentation` ```bash scripts/script.sh documentation ``` Documentation flow — manifests, BOLs, receiving reports, discrepancy handling. ### `metrics` ```bash scripts/script.sh metrics ``` Key performance metrics for deconsolidation operations. ### `checklist` ```bash scripts/script.sh checklist ``` Deconsolidation operations checklist. ### `help` ```bash scripts/script.sh help ``` ### `version` ```bash scripts/script.sh version ``` ## Configuration | Variable | Description | |----------|-------------| | `DECONSOLIDATION_DIR` | Data directory (default: ~/.deconsolidation/) | --- *Powered by BytesAgain | bytesagain.com | [REDACTED_SECRET]*


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

--- name: blucli description: BluOS CLI (blu) for discovery, playback, grouping, and volume. homepage: https://blucli.sh metadata: {"clawdbot":{"emoji":"🫐","requires":{"bins":["blu"]},"install":[{"id":"go","kind":"go","module":"github.com/steipete/blucli/cmd/blu@latest","bins":["blu"],"label":"Install blucli (go)"}]}} --- # blucli (blu) Use `blu` to control Bluesound/NAD players. Quick start - `blu devices` (pick target) - `blu --device <id> status` - `blu play|pause|stop` - `blu volume set 15` Target selection (in priority order) - `--device <id|name|alias>` - `BLU_DEVICE` - config default (if set) Common tasks - Grouping: `blu group status|add|remove` - TuneIn search/play: `blu tunein search "query"`, `blu tunein play "query"` Prefer `--json` for scripts. Confirm the target device before changing playback.


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

--- name: screenshot-capture description: Process screenshots Enzo shares with comments. Save to reference library, extract content, categorize, set reminders, and log patterns. Use when Enzo sends an image with context like "save this", shares a screenshot of content (LinkedIn posts, tweets, articles), or sends ideas/frameworks to remember. --- # Screenshot Capture When Enzo shares a screenshot with comments, execute this workflow: ## 1. Save Screenshot ``` cp [inbound image] notes/screenshots/[descriptive-name].jpg ``` Name should reflect content (e.g., `positioning-angles.jpg`, `gpt-ads-hack.jpg`) ## 2. Categorize Based on Enzo's comment and content, determine category: | Category | Signals | Destination | |----------|---------|-------------| | **Framework** | Actionable mental model, how-to, process | `notes/frameworks.md` under main section | | **AI Hack** | "AI porn", hackathon material, overpromises but useful | `notes/frameworks.md` under "AI Hacks & Hackathon Ideas" | | **Idea** | Original thought, "I want to build", future project | `notes/ideas.md` | ## 3. Extract & Store - Extract key content from screenshot - Add to appropriate file with: - Date saved - Source (if visible) - Screenshot reference - Enzo's commentary (if provided) - Structured summary ## 4. Set Reminder **Always set a 1-week reminder** unless Enzo specifies otherwise. Reminder text should prompt action: - "Have you tested [framework] on anything?" - "Did you try [hack]?" - "Any progress on [idea]?" ## 5. Log Pattern Add observation to `notes/patterns.md`: ```markdown - [category] [topic]: [brief description] — [intent signal] ``` Intent signals: learn, build, share, remember, reference, hackathon ## 6. Confirm Reply with: - What was saved and where - Reminder date - Any commentary acknowledged Keep confirmation brief.


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