OpenClaw/clawhub-security-signals huggingface dataset:
Skill: xinqimiao/herhug (version 1.0.0)

*** name: herhug-sk description: 心理学评分引擎 - 让AI从专业角度理解用户，越用越懂你 metadata: { "openclaw": { "emoji": "🤗", "homepage": "[https://github.com/xinqimiao/herHug-sk.git](https://github.com/your-username/herHug-sk)", "requires": { "bins": \["node"], "env": \["OPENCLAW\_DATA\_DIR"] } } } - # herHug - 心理学评分引擎 ## 🤗 她懂你 herHug 不直接生成回复，而是提供**心理学评分标准**和**用户状态数据**，让大模型基于这些数据生成真正懂你的回复。 ## 📊 核心功能 ### 1. 获取评分标准 const standards = await openclaw\.skills.run('herHug', 'get-scoring-standards'); ### 2. 保存用户评分 await openclaw\.skills.run('herHug', 'save-score', { scoreData: { timestamp: new Date().toISOString(), content: userInput, ocean: { ... }, emotion: { ... }, flexibility: { ... }, coping: { ... }, confidence: 0.7 } }); ### 3. 获取当前状态 const state = await openclaw\.skills.run('herHug', 'get-current-state'); ### 4. 每日情绪节律分析 const rawData = await openclaw\.skills.run('herHug', 'get-daily-rhythm-data'); await openclaw\.skills.run('herHug', 'save-daily-rhythm', { rhythmAnalysis: analysisResult }); 🧠 心理学维度 维度 文件 说明 OCEAN+HEXACO 01\_ocean\_hexaco.md 人格骨架 情绪节律 02\_emotional\_rhythm.md 时间模式（日/周/恢复力） 心理灵活性 03\_psychological\_flexibility.md 适应能力 应激应对 04\_stress\_coping.md 压力反应 置信度 05\_confidence\_scoring.md 可信程度 ⚠️ 置信度原则 ≥0.7：可完全信任，用于调整回复风格 0.4-0.7：谨慎参考，多用询问确认 <0.4：忽略，用默认策略 📁 数据存储 所有用户数据保存在 \~/.openclaw/data/herHug/<userId>/ 目录下： raw\_scores.jsonl：原始评分（时间序列） daily\_rhythm.json：每日情绪节律 current\_state.json：当前状态（含置信度加权） emotion\_tracker.json：主动关怀待办 confidence\_report.json：置信度报告 🔄 完整工作流程 每次对话后：大模型分析输入 → 生成评分 → 调用 save-score 每天一次：调用 get-daily-rhythm-data → 大模型分析 → 调用 save-daily-rhythm 每次回复前：调用 get-current-state → 根据状态生成回复


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: shenruiyang/orbcafe-pivot-ainav (version 1.0.0)

--- name: orbcafe-pivot-ainav description: Build ORBCAFE advanced analytics interactions using CPivotTable/usePivotTable and voice navigation using CAINavProvider/useVoiceInput. Use when requests involve drag-drop pivot dimensions, aggregation controls, preset management, or space-key voice navigation workflows. --- # ORBCAFE Pivot + AINav ## Scope Use this skill for advanced analytics interaction and voice navigation. Primary APIs: - `CPivotTable`, `usePivotTable` - `CAINavProvider`, `useVoiceInput`, `CVoiceWaveOverlay`, `COrbCanvas` ## Workflow 1. Determine if request is pivot, voice, or combined from `references/domain-patterns.md`. 2. Start from matching recipe in `references/recipes.md`. 3. Apply interaction and reliability constraints in `references/guardrails.md`. ## Output Contract 1. `Module choice`: pivot or AINav (or both). 2. `Minimal implementation`: controlled if persistence/integration needed. 3. `Ops note`: one operational requirement (preset persistence or ASR endpoint contract).


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: metariot2026/agent-anchor (version 1.0.0)

# Agent Anchor Crash-proof dashboard for OpenClaw. Your AI agent's memory that survives everything. ## Description Agent Anchor keeps your OpenClaw agent running smoothly — even through crashes, restarts, and interruptions. It continuously snapshots state so your agent resumes exactly where it left off. ## Features - **Crash Recovery** — Automatically saves state every 3 minutes. Never lose context on restart. - **Task Tracker** — Organize tasks into In Progress, Staged, and Completed - **Terminal View** — See live system status, connection health, agent activity - **Cron Job Monitoring** — Monitor scheduled jobs, click to see details, fix errors with one click - **Activity Calendar** — Look back at your agent's history by date - **Click-to-Expand** — Inspect any task or cron job for full details ## Installation ```bash cd ~/workspace git clone <agent-anchor-repo> cd agent-anchor python3 -m http.server 3456 ``` Then open http://localhost:3456 ## Requirements - OpenClaw installation - Python 3 for local server - Web browser for dashboard ## Configuration Edit `state-anchor.json` to customize: - Task categories (inProgress, staged, completed) - Revenue streams - Contacts (hot, warm, cold) - Cron jobs to monitor ## Price $19 one-time (Stripe) or $19 USDC (Base network) ## Support DM @theia_metariot on X/Twitter --- Built by MetaRiot — Culture for the decentralized age.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: deanshak/akeyless (version 1.0.0)

--- name: akeyless description: Akeyless Secrets Management via the official akeyless CLI — install, configure profiles, gateway routing, and safe read/list operations. Use when the user mentions Akeyless, akeyless CLI, vault, static/dynamic secrets, list-items, profiles, AKEYLESS_GATEWAY_URL, or fetching secrets without putting credentials in chat. homepage: https://docs.akeyless.io/docs/cli metadata: { "openclaw": { "emoji": "🔑", "requires": { "bins": ["akeyless"] }, "install": [ { "id": "brew", "kind": "brew", "formula": "akeylesslabs/tap/akeyless", "bins": ["akeyless"], "label": "Install Akeyless CLI (brew tap)", }, ], }, } --- # Akeyless CLI Teach the agent to use the **official** [Akeyless CLI](https://docs.akeyless.io/docs/cli) on the **same machine as the OpenClaw gateway**. Do not invent URLs, regions, or auth flows—defer to docs and `akeyless <cmd> -h`. ## References Load when details are needed: - `references/cli-notes.md` — install (macOS/Linux), profiles, gateway env, `list-items`, precedence, safety ## Prerequisites 1. **`akeyless`** on PATH (`akeyless --version`). 2. A **configured profile** (`akeyless configure` or `~/.akeyless/profiles/`). Auth is **not** done through chat—user runs configure locally. 3. For **private gateways**: `AKEYLESS_GATEWAY_URL` (and TLS trust PEM if required)—see references. ## Workflow 1. Confirm CLI: `akeyless --version` / `which akeyless`. 2. If commands fail with auth errors: user must fix **profile** or **gateway URL** outside the agent; suggest `akeyless configure` or env vars from references—**never** ask them to paste Access Keys into chat. 3. Prefer **read-only** checks first: `akeyless list-items --minimal-view` or `akeyless list-items --path '<folder>' --minimal-view` (paths are org-specific). 4. For JSON: `akeyless list-items --json` — **summarize**; do not dump large payloads or possible secret fields into chat. 5. **Region / tenant**: do not assume only `vault.akeyless.io`; follow account and org docs. ## OpenClaw-specific - Skills live under the agent workspace, e.g. `~/.openclaw/workspace/skills/akeyless/`. User enables **akeyless** in **Skills** and restarts the **gateway** after changes. - Shell commands run as the **gateway host user**; that user must have working `akeyless` credentials. ## Guardrails - **Never** paste or request Access Keys, API keys, or secret **values** in chat, logs, or repos. - Least-privilege: only commands the user’s **role** allows; if access denied, point to Akeyless **role** and **folder path**, not “retry with more secret text.” - Do not commit `~/.akeyless/` or paste profile TOML into threads. ## Contrast with 1Password (`op`) Akeyless uses **`akeyless`** + **profiles** + optional **`AKEYLESS_GATEWAY_URL`**. There is no 1Password-style desktop app unlock in this workflow.


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

--- name: iwencai-screener description: 根据输入的链接、文件、文本或关键词分析利好行业/概念/题材/产业链，聚焦核心受益方向，在i问财网站进行选股查询，返回每项前5个结果并生成Excel文件。当用户要求分析利好行业、概念选股、行业选股、选股、利好哪些股票等意图时使用此技能。 --- # i问财选股技能 (iwencai-screener) ## 触发条件 当用户具有以下意图时激活： - "XX选股" - 如"新能源选股"、"AI选股" - "XX利好哪些股票" - 如"芯片利好哪些股票" - "分析利好哪些行业" - "概念选股"、"行业选股" - "根据XX选股" - 输入链接/文本后要求分析并选股 - 模型自行根据语义判断需要选股的场景 ## 核心原则 **聚焦原则**：识别3-5个最核心的利好方向，避免泛泛而谈 - 优先选择与内容最直接相关的行业/概念 - 产业链分析聚焦核心环节，不做全产业链罗列 - 每个方向都要有明确的利好逻辑 ## 操作流程 ### 第一步：分析内容识别核心利好方向 1. **分析输入内容**： - 链接：使用web_fetch获取内容并分析 - 文件：读取文件内容并分析 - 文本/关键词：直接分析 - 语义判断：根据用户输入自行推断选股意图 2. **聚焦识别**（只选最核心的3-5个方向）： **选择标准**： - 与内容直接相关（不是间接关联） - 有明确的产业链传导逻辑 - 有实际的产品/业务支撑 **聚焦格式**： ``` 【核心利好方向】（限定3-5个） 1. XX行业/概念 - 利好逻辑：为什么利好（具体原因） - 产业链位置：上游/中游/下游 - 代表标的：YYY、ZZZ 2. XX行业/概念 - 利好逻辑：为什么利好 - ... ``` 3. **输出示例**： ``` 【核心利好方向】 1. 脑机接口（核心直接利好） - 利好逻辑：新闻提到脑机接口技术突破直接带动康复医疗器械需求 - 产业链位置：下游应用端（康复设备）+ 中游制造 - 代表标的：翔宇医疗、麦澜德 2. 芯片半导体（上游支撑） - 利好逻辑：脑机接口需要专用芯片，刺激芯片需求 - 产业链位置：上游核心零部件 - 代表标的：国科微、紫光国微 ``` ### 第二步：i问财选股查询 4. **聚焦查询**（只查核心方向）： - 访问：`https://www.iwencai.com` - 针对每个核心识别出的行业/概念进行搜索 - 搜索示例： - "XX行业股票" 或 "XX概念股票" - "XX龙头股" 5. **获取查询结果**： - 每条查询取前5个结果 - **重要**：获取所有返回字段，完整保留数据 ### 第三步：汇总结果 6. **整合数据**： - 按核心方向分类展示 - 每个方向显示前5只股票 7. **生成Excel文件**： - 使用generate_stock_excel.py生成 - 每个核心方向一个Sheet - 保存路径：`/Users/tututu/.openclaw/workspace/选股结果_YYYYMMDD_HHMMSS.xlsx` ## 输出格式 ### 文本回复 ``` 【核心利好方向分析】 根据对内容的分析，**聚焦**以下核心利好方向： ## 一、XX行业/概念（最核心） - **利好逻辑**：具体说明为什么利好（原因+传导路径） - **产业链位置**：上游/中游/下游 - **核心标的**：YYY、ZZZ ## 二、XX行业/概念（次核心） - **利好逻辑**：... - ... --- ### 选股结果 ## 一、XX概念相关股票 | 序号 | 代码 | 名称 | 现价 | 涨跌幅 | 核心逻辑 | |------|------|------|------|--------|----------| | 1 | XXXXXX | XXXX | XX.XX | X.XX% | ... | | 2 | XXXXXX | XXXX | XX.XX | X.XX% | ... | | ... | ... | ... | ... | ... | ... | ## 二、YY概念相关股票 ...（同上） --- 📊 Excel文件已生成: 选股结果_20260312_163000.xlsx ``` ### Excel文件结构 - **文件名**: `选股结果_YYYYMMDD_HHMMSS.xlsx` - **Sheet**: 按核心方向名称命名（最多5个Sheet） - **列**: i问财返回的原始字段名 - **行**: 每个方向最多5只股票 ## 注意事项 1. **聚焦优先**：只识别最核心的3-5个方向，不要列出10+个方向 2. **必须有逻辑**：每个方向都要说明"利好逻辑"，不能只给结论 3. **产业链位置**：标注清楚在产业链的哪个环节 4. **字段完整性**：获取并显示所有返回字段 5. **异常处理**：某查询失败时记录错误，继续其他查询


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: jontsai/spacesuit (version 0.3.0)

# openclaw-spacesuit **A framework scaffold for OpenClaw workspaces.** ## Metadata | Field | Value | |-------|-------| | **Name** | `spacesuit` | | **Version** | `0.3.0` | | **Author** | jontsai | | **License** | MIT | | **Category** | framework | | **Tags** | workspace, scaffold, conventions, memory, git-workflow | ## Description The Spacesuit is a batteries-included framework layer for OpenClaw workspaces. It provides: - **Session startup protocol** — security-first file loading order - **Memory system** — daily logs + curated long-term memory with commit discipline - **Git workflow** — mandatory pre-commit checks, worktree conventions, parallel agent coordination - **Safety rules** — pre-action checklist, destructive-action guards, stand-down protocol - **Priority system** — P0–P5 triage for tasks and incidents - **Cross-platform handoffs** — structured context transfer between Slack/Discord/Telegram - **Heartbeat framework** — proactive periodic checks with state tracking - **Decision logging** — mandatory audit trail for architectural decisions - **Meta-learning framework** — expert-first research methodology (Dunning-Kruger aware) - **Security baseline** — secret transmission policy, prompt injection defense, data classification ## Installation ```bash # First-time install (creates workspace files with markers) make init # Upgrade existing workspace (replaces only SPACESUIT sections) make upgrade ``` ## How It Works OpenClaw reads hardcoded filenames from the workspace root (`AGENTS.md`, `SOUL.md`, etc.). Since we can't change that loading behavior, Spacesuit uses **section-based merging**: 1. Framework content is wrapped in `<!-- SPACESUIT:BEGIN -->` / `<!-- SPACESUIT:END -->` markers 2. On upgrade, only the content between markers is replaced 3. Everything outside the markers (your customizations) is preserved ## Files Managed | File | Base Content | User Content | |------|-------------|--------------| | `AGENTS.md` | Session protocol, memory, git, safety, priorities | Channel mappings, tool configs, personal rules | | `SOUL.md` | Core personality scaffold | Personal vibe, human-specific tone | | `TOOLS.md` | Tool organization guidance | Actual tool configs, credentials refs, API details | | `HEARTBEAT.md` | Check framework & state tracking | Specific checks to run | | `IDENTITY.md` | — (template only) | Name, avatar, personality | | `USER.md` | — (template only) | All about your human | | `MEMORY.md` | Long-term memory structure | Project notes, personal context | | `SECURITY.md` | Full security baseline | Contact-specific alert channels | | `Makefile` | — (template only) | Gateway management targets | ## Upgrade Path ```bash # See what would change ./scripts/diff.sh # Apply upgrade ./scripts/upgrade.sh # Check version cat skills/spacesuit/VERSION ```


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: suspicious | SkillSpector:  ()
Skill: weykon/agent-hand (version 0.3.8)

# Agent Hand — AI Agent Command Center Your AI army command center. Monitor and manage all AI agent sessions from one terminal. Built for the one-person company (OPC) era — when you run Claude, Cursor, Codex, Gemini, and more simultaneously, Agent Hand is how you stay in control. ## What It Does - **Unified Dashboard**: See every AI agent's status at a glance — running, waiting, idle, done - **Instant Switch**: Jump into any agent session, work with it, Ctrl+Q back to command center - **Smart Prioritization**: AI-powered session sorting — the agent that needs you most floats to top - **Canvas Workflow** (Pro): Visual workflow map of all your agents and their task flow - **Hook System**: Auto-detects Claude Code, Cursor, Codex, Windsurf, Kiro, OpenCode, Gemini sessions - **Multi-Profile**: Separate work contexts (client projects, personal, side hustle) ## Installation ```bash curl -fsSL https://raw.githubusercontent.com/weykon/agent-hand/master/install.sh | bash ``` Installs the `agent-hand` binary. Works on macOS (ARM + Intel) and Linux. Requires: tmux ## Quick Start ```bash # Launch the dashboard agent-hand # Auto-register hooks for all supported AI tools agent-hand hooks install ``` Then just use your AI tools normally. Agent Hand detects new sessions automatically. ## Key Bindings | Key | Action | |-----|--------| | `Ctrl+N` | Enter selected session | | `Ctrl+Q` | Return to dashboard | | `j/k` | Navigate sessions | | `p` | Open Canvas (Pro) | | `/` | Search sessions | | `?` | Help | ## Pricing - **Free**: Full TUI dashboard, session management, hook system - **Pro** ($19/year): Canvas workflow, AI summaries, collaboration - **Max** (coming): Web 3D dashboard, relationship graphs ## Links - GitHub: https://github.com/weykon/agent-hand - Website: https://agent-hand.dev ## Who Is This For Solo developers, OPC founders, anyone running multiple AI agents daily. If you've ever lost track of which terminal has which AI — this is for you.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: oceanking57/crypto-arb-monitor (version 1.0.0)

--- name: crypto-arb-monitor version: 1.0.0 description: "加密货币跨交易所套利监控器 - 实时监控BTC/ETH/SOL等主流币种在不同交易所的价差，发现套利机会并发送飞书/Telegram预警。支持自动计算手续费后净利润。" author: 小琳 (SmartChain Capital) license: MIT platforms: - openclaw tags: - cryptocurrency - arbitrage - trading - monitoring - feishu - defi price: 29 --- # Crypto Arbitrage Monitor 🔍💰 **跨交易所加密货币套利机会监控器** 实时监控主流加密货币在不同交易所之间的价格差异，发现套利机会并推送预警。 ## 功能特性 - ✅ 支持 Binance / OKX / Bybit / Huobi 等主流交易所 - ✅ 监控 BTC / ETH / SOL / XRP / DOGE 等热门币种 - ✅ 自动计算扣除手续费后的净利润 - ✅ 飞书/Telegram实时预警推送 - ✅ 可配置价差阈值和监控频率 - ✅ 历史套利机会记录和统计 ## 快速开始 ### 1. 安装依赖 ```bash pip install ccxt requests python-dotenv ``` ### 2. 配置 ```python # config.py CONFIG = { "exchanges": ["binance", "okx", "bybit"], "symbols": ["BTC/USDT", "ETH/USDT", "SOL/USDT", "XRP/USDT", "DOGE/USDT"], "min_spread_percent": 0.5, # 最小价差百分比（扣除手续费后） "trading_fee_percent": 0.1, # 单边手续费百分比 "check_interval_seconds": 60, # 检查间隔 "alert_channels": { "feishu_webhook": "", # 飞书机器人webhook地址 "telegram_bot_token": "", # Telegram Bot Token "telegram_chat_id": "", # Telegram Chat ID } } ``` ### 3. 运行 ```bash python monitor.py ``` ## 使用场景 | 场景 | 说明 | |------|------| | 手动套利 | 发现价差后手动在两个交易所分别买卖 | | 网格交易辅助 | 配合网格策略，在价差扩大时增加仓位 | | 市场监控 | 了解不同交易所的流动性差异 | | 量化策略输入 | 将价差数据作为量化交易信号 | ## 套利计算公式 ``` 净利润 = (高价交易所卖出价 - 低价交易所买入价) / 低价交易所买入价 × 100% - 2 × 手续费% ``` 只有当净利润 > 阈值时才触发预警。 ## 风险提示 - ⚠️ 价差可能在执行过程中消失（执行风险） - ⚠️ 需要两个交易所都有资金（资金效率） - ⚠️ 提币/转账需要时间（时间风险） - ⚠️ 交易所可能有提币限制（流动性风险） - ⚠️ 本工具仅供监控参考，不构成投资建议 ## 文件结构 ``` crypto-arb-monitor/ ├── SKILL.md # 技能说明 ├── monitor.py # 主监控脚本 ├── config.py # 配置文件 ├── alerts.py # 预警推送模块 ├── requirements.txt # Python依赖 └── README.md # 使用说明 ``` ## 版本历史 - v1.0.0 (2026-03-21): 初始版本，支持多交易所价差监控


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: guohongbin-git/market-analysis-cn (version 1.0.0)

--- name: market-analysis-cn description: "市场分析服务 | Market Analysis Service. 企业市场趋势分析 | Enterprise market trend analysis. 竞品分析 | Competitor analysis. 用户行为洞察 | User behavior insights. 触发词：市场分析、竞品分析、market analysis、趋势." author: 赚钱小能手 metadata: openclaw: emoji: 📊 pricing: basic_analysis: "10 USDC" deep_analysis: "30 USDC" monthly_subscription: "50 USDC" --- # 市场分析服务 专业市场分析服务，帮助企业做出更明智的决策。 ## 服务内容 ### 1. 市场趋势分析（$10） - 行业趋势 - 市场规模 - 增长预测 ### 2. 竞品分析（$20） - 竞争对手分析 - SWOT 分析 - 差异化建议 ### 3. 用户行为分析（$20） - 用户画像 - 行为模式 - 转化漏斗 ### 4. 深度分析报告（$30） - 综合分析 - 战略建议 - 行动计划 ## API ```bash POST /analyze { "type": "market_trend", "industry": "AI Agent", "region": "China" } ``` ## 收费 | 服务 | 价格 | |------|------| | 基础分析 | $10 | | 竞品分析 | $20 | | 用户分析 | $20 | | 深度报告 | $30 | | 月度订阅 | $50 | --- *专业分析 = 明智决策 📊*

path: package.json
content: name: market-analysis-cn
version: 1.0.0
description: Market Analysis Service CN | 市场分析服务

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: allinherog-star/software-dev-cost-dashboard (version 1.0.7)

--- name: software-dev-cost-dashboard description: "软件成本评估看板适合管理、技术、软件、通用在用户想估算软件项目开发成本时使用，帮助基于输入材料生成外部 Web 应用入口、`https://soft.ai-skills.ai` 内的软件成本评估流程。" requiredEnvVars: [] --- # software-dev-cost-dashboard 软件成本评估看板 [快速开始](https://github.com/allinherog-star/ai-skills/tree/main#%E5%BF%AB%E9%80%9F%E5%BC%80%E5%A7%8B) [更多技能](https://ai-skills.ai) ### 概述 软件成本评估看板用于回答「软件开发成本不可控」、估算软件开发成本、梳理项目预算、辅助立项和报价沟通，适合管理、技术、软件、通用在明确业务目标、内容材料或分析对象后调用。 它会结合用户输入的业务背景和目标等输入，整理关键上下文，并输出外部 Web 应用入口、`https://soft.ai-skills.ai` 内的软件成本评估流程，便于继续执行、复盘或交付。 ### 什么时候使用 **适用场景** - 用户想估算软件项目开发成本 - 用户需要预算或报价规划看板 - 用户可以继续到外部 Web 应用完成评估 ### 调用方式 当前技能为 external-link 模式，请直接访问 [https://soft.ai-skills.ai](https://soft.ai-skills.ai) 完成操作。 ### 命令示例 当前技能不通过 `python3 scripts/run.py` 直接调用，请直接访问 [https://soft.ai-skills.ai](https://soft.ai-skills.ai)。 ### 参数说明 当前技能不通过本地 runner 传参，直接在目标站点内完成输入与操作。 ### 参数取值参考 当前技能没有需要额外查表的分类参数。 ### 支持的输入格式 当前技能不解析本地 JSON 参数，请直接访问 [https://soft.ai-skills.ai](https://soft.ai-skills.ai) 完成后续操作。 ### 示例请求 当前技能为 external-link 模式，不适用 CLI 请求示例。请直接访问 [https://soft.ai-skills.ai](https://soft.ai-skills.ai)。 ### 返回结果示例 ```json { "success": true, "data": { "invocationMode": "external-link", "externalLink": "https://soft.ai-skills.ai", "externalLinkLabel": "打开成本看板", "message": "Open this external-link target to continue." } } ``` ### 交付内容 - 打开成本看板的外部操作入口。 - 面向业务用户的表单、看板或工作流页面，用于继续完成评估和交付。 ### 结果使用建议 - 进入外部页面后，优先补全项目背景、目标范围和约束条件，结果会更适合直接用于决策。 - 把评估结果用于预算讨论、方案对齐或后续报价沟通，而不是只看单一数字。 ### 运行前准备 - 当前技能不依赖本地 API Key 环境变量。


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: sirimaddala99/instagram-digest (version 1.0.2)

--- name: instagram-digest description: Scrape recent Instagram reels, transcribe audio, summarize with OpenRouter, and save a digest.html file metadata: openclaw: requires: env: - OPENROUTER_API_KEY optionalEnv: - INSTAGRAM_USERNAME - INSTAGRAM_PASSWORD anyBins: - python3 - python primaryEnv: OPENROUTER_API_KEY --- # Instagram Digest Skill Scrapes Instagram reels from a configured list of accounts, transcribes their audio, generates AI summaries, and saves a `digest.html` file for local viewing. ## When to Use Trigger this skill when the user asks to: - Run / send the Instagram digest - Get a summary of recent Instagram reels - Check what accounts are being tracked - Add or remove accounts from the digest - Do a dry run / preview without sending email ## Working Directory All commands must be run from the skill's own directory: ``` cd /path/to/instagram-digest ``` ## Commands ### Run the digest (saves digest.html) ``` python main.py ``` ### Run digest for specific accounts only ``` python main.py --accounts username1 username2 username3 ``` ### List currently configured accounts ``` python main.py --list-accounts ``` ## Adding / Removing Accounts Permanently The default account list lives in `config.py` under `INSTAGRAM_ACCOUNTS`. To permanently add or remove an account, edit that list. Then confirm by running `--list-accounts`. ## Environment Variables | Variable | Required | Purpose | |---|---|---| | `OPENROUTER_API_KEY` | ✅ Required | OpenRouter API key for transcript summarization (uses Nvidia Nemotron via OpenRouter) | | `INSTAGRAM_USERNAME` | Optional | Instagram login for story access — use a dedicated throwaway account | | `INSTAGRAM_PASSWORD` | Optional | Instagram password for story access — use a dedicated throwaway account | ## Output Writes `digest.html` to the skill directory. Open it in a browser to read audio summaries for each reel. ## Error Handling - If Instagram returns a login wall for an account, that account is skipped and the digest continues with the rest. - If transcription or analysis fails for a reel, a fallback message is shown for that item — the digest still sends. - If no new reels or stories are found across all accounts, the email is skipped entirely and a message is printed.

path: requirements.txt
content: anthropic
playwright
python-dotenv
requests
yt-dlp
faster-whisper


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: mdomerofski/decision-dynamo (version 1.0.0)

--- name: decision-dynamo description: Run a weighted decision matrix to score and rank 2-4 options across 5 configurable criteria. Use when a user needs help choosing between options, comparing tradeoffs, or making a structured decision. Triggers on phrases like "help me decide", "which option is better", "decision matrix", "weigh my options", or "I can't choose between". --- # Decision Dynamo Run a weighted matrix analysis to score and rank options objectively. ## Workflow 1. **Gather options** — Identify 2–4 named choices to compare. 2. **Set weights** — Ask the user to rate how important each of the 5 criteria is (1–10). 3. **Score options** — For each option, rate it 1–10 on each criterion. 4. **Run the matrix** — Execute `scripts/decision_matrix.py` (interactive or JSON mode). 5. **Present results** — Share the ranked output and briefly explain the winner. ## Running the Script **Interactive mode** (guided prompts): ```bash python3 scripts/decision_matrix.py ``` **JSON mode** (pre-built input): ```bash python3 scripts/decision_matrix.py input.json ``` See `references/criteria.md` for the JSON schema, criteria definitions, scoring scale, and inversion logic for negative criteria. ## The Five Criteria | Criterion | Type | |-------------------|----------| | Skill/Leverage Gain | Positive | | Goal Alignment | Positive | | Mental/Emotional Drag | Negative (inverted) | | Financial Cost | Negative (inverted) | | Time and Effort | Negative (inverted) | Negative criteria use `(11 - score) * weight` so that *less drag = higher score*. ## Agent Guidance - If the user hasn't defined weights, suggest defaults (all equal at 5) and ask if they want to adjust. - If scoring feels subjective, help the user by asking "on a scale of 1–10, how much does this option [criterion]?" - After presenting results, offer to re-run with adjusted weights to test sensitivity. - Always show the winner clearly and explain *why* it scored highest in plain language.


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