OpenClaw/clawhub-security-signals huggingface dataset:
Skill: davidme6/smart-model-switcher-pro (version 6.0.0)

--- name: smart-model-switcher-pro description: 智能模型自动切换 Pro V6.0.0（类 Trae 模式）- 多模态感知，自动识别图片/视频/音频/代码/文本任务，切换到最适合的模型。支持图片理解 (qwen3-vl-plus)、视频音频 (qwen3.5-plus)、代码 (glm-5)、Office 文档 (MiniMax-M2.5)、推理等场景。零感知切换，无需手动操作。类 Trae 自动选择 + 子代理多模态保护。 version: 6.0.0 author: davidme6 --- # 🧠 Smart Model Switcher Pro V6.0.0（类 Trae 模式） **自动识别任务类型 • 智能切换最优模型 • 零感知 • 无需手动** **对标 Trae 自动选择 | 多模态全支持 | 子代理保护** --- ## 🎯 V6.0.0 核心升级 | 特性 | V5.1.0 | V6.0.0（类 Trae 模式） | |------|--------|----------------------| | **多模态感知** | ✅ 图片/视频/音频 | ✅ 增强版 | | **主窗口切换** | ✅ 智能切换 | ✅ 智能切换 | | **子代理保护** | ❌ 不切换 | ✅ **多模态切换** | | **Trae 式自动选择** | ✅ 是 | ✅ 增强版 | | **Office 模式** | ✅ MiniMax-M2.5 | ✅ MiniMax-M2.5 | --- ## ⚠️ 核心规则：分层切换策略 ### 🔒 规则 1：多模态优先（最高优先级） **所有会话都生效（主窗口 + 子代理）！** | 输入类型 | 自动切换模型 | 说明 | |----------|-------------|------| | **图片** | qwen3.5-plus | 视觉理解首选 | | **视频** | qwen3.5-plus | 视频理解 | | **音频** | qwen3.5-plus | 音频理解 | | **OCR 文字** | qwen3-vl-plus | 纯图片 OCR | **为什么子代理也要切换？** - ✅ 保证子代理能识别图片/视频/音频 - ✅ 防止子代理收到多模态输入时失败 - ✅ 保持专业能力（代码/推理不切换） ### 🔑 规则 2：主窗口智能切换（类 Trae 模式） **主窗口执行完整智能切换：** | 任务类型 | 自动切换模型 | |----------|-------------| | **代码任务** | glm-5 / qwen3-coder-plus | | **复杂推理** | qwen3-max / qwq-plus | | **创意写作** | qwen3.5-plus | | **日常对话** | qwen3.5-plus | | **长文档** | qwen3.5-plus (256K) | | **Office 文档** | MiniMax-M2.5 | ### 🔐 规则 3：子代理专业能力保持 **子代理在**非多模态**情况下保持预设模型：** | 子代理角色 | 预设模型 | 说明 | |-----------|---------|------| | **技术专家** | glm-5 | 代码能力最强 | | **推理专家** | qwq-plus | 复杂推理 | | **市场猎手** | qwen3-max | 商业分析 | | **产品经理** | qwen3-max | 需求分析 | | **设计师** | qwen3.5-plus | 视觉设计 | --- ## 🔄 自动切换逻辑（类 Trae 模式） `javascript // 第一优先级：多模态检测（所有会话） IF 消息包含图片/视频/音频 → 切换到 qwen3.5-plus // 第二优先级：主窗口智能切换 IF 主窗口 { IF 代码任务 → glm-5 ELSE IF 推理任务 → qwen3-max ELSE IF 创意写作 → qwen3.5-plus ELSE IF Office 文档 → MiniMax-M2.5 ELSE → qwen3.5-plus (默认最优) } // 第三优先级：子代理保持专业模型 IF 子代理 { 使用预设模型（不切换，除非多模态） } ` --- ## 📊 与 Trae 自动选择对比 | 功能 | Trae | 我们 V6.0.0 | |------|------|-------------| | **图片识别** | ✅ 自动切换 | ✅ qwen3.5-plus | | **视频分析** | ✅ 自动切换 | ✅ qwen3.5-plus | | **音频处理** | ✅ 自动切换 | ✅ qwen3.5-plus | | **代码任务** | ✅ 自动切换 | ✅ glm-5 | | **复杂推理** | ✅ 自动切换 | ✅ qwen3-max | | **日常对话** | ✅ 自动切换 | ✅ qwen3.5-plus | | **长文档** | ✅ 自动切换 | ✅ qwen3.5-plus (256K) | | **Office 文档** | ✅ 自动切换 | ✅ MiniMax-M2.5 | | **子代理多模态** | ❌ 不支持 | ✅ **支持** | | **主窗口限制** | ❌ 无 | ✅ 仅主窗口智能切换 | **结论：** 完全对标 Trae，并增加子代理多模态保护！✅ --- *版本：6.0.0（类 Trae 模式）* *创建时间：2026-04-04* *作者：davidme6*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: qiushuizy/java-dependency-security-scan (version 1.0.0)

--- name: java-dependency-security-scan description: 用于分析 Java、Maven 或 Spring 生态项目的依赖安全风险。当用户要求扫描依赖漏洞、核实某个依赖版本是否受影响、分析传递依赖、检查第三方 JAR 内嵌依赖、审阅 CVE 影响范围，或生成包含修复建议的依赖安全报告时使用。 ## 适用范围 本 Skill 聚焦 Java 生态（Maven/Gradle）的依赖安全分析。其他语言项目可复用相同的漏洞分析方法，但需调整： - 依赖获取命令（npm list, pip freeze 等） - 锁文件格式（package-lock.json, Pipfile.lock 等） - 漏洞数据源和 API 接口 --- ## 快速开始 当用户提出以下需求时，使用本 Skill： | 场景 | 示例 | |------|------| | 扫描项目依赖漏洞 | "扫描我的 Spring Boot 项目依赖漏洞" | | 核实特定依赖 | "log4j-core 2.14.1 是否有漏洞？" | | 分析 CVE 影响 | "CVE-2021-44228 对我的项目有什么影响？" | | 检查传递依赖 | "分析项目中 commons-lang3 的依赖路径" | | 检查 JAR 内嵌依赖 | "分析这个第三方 JAR 包里的依赖" | | 生成安全报告 | "生成依赖安全扫描报告" | --- ## 核心工作流程 ``` 收集证据 → 界定范围 → 匹配漏洞 → 输出报告 ``` ### 1. 收集证据（按优先级） | 优先级 | 证据类型 | 说明 | |--------|----------|------| | 1 | lockfile | gradle.lockfile、libs.versions.toml | | 2 | dependency:tree | mvn dependency:tree / gradlew dependencies | | 3 | 构建文件 | pom.xml / build.gradle 声明 | | 4 | 用户输入 | 依赖清单、扫描报告 | ### 2. 界定扫描范围 **默认扫描**： - Maven: `compile`、`runtime` scope - Gradle: `runtimeClasspath`、`implementation` 配置 - 第三方 JAR 中实际携带的内嵌依赖 **默认排除**： - `test`、`testImplementation` 等测试依赖 - `provided`、`compileOnly` 等容器提供依赖 ### 3. 匹配漏洞 确认漏洞需同时满足： - ✅ 依赖坐标明确（groupId:artifactId:version） - ✅ 版本落入 CVE 公告的受影响范围 - ✅ 运行时实际生效（非被覆盖/排除） ### 4. 输出报告 按以下结构输出： 1. **扫描概况**：项目信息、依赖数量、漏洞统计 2. **漏洞明细**：按严重级别分组，包含 CVE、修复版本 3. **修复方案**：优先级排序的具体修复建议 4. **剩余风险**：未验证的假设和不确定性 --- ## 关键原则 - **先收集证据，再下结论**：优先使用 lockfile 和 dependency:tree - **双重验证**：扫描器结果只是线索，需人工核实版本和受影响范围 - **明确不确定性**：无法确认的信息要标注"需要验证"或"证据不足" - **关注运行时**：默认只关注实际进入运行时类路径的依赖 --- ## 目录 1. [执行原则与环境](./references/01-principles.md) - 证据权重、漏洞匹配标签、严重级别 2. [Maven 项目分析](./references/02-maven.md) - Maven 依赖树分析、scope 对照、传递依赖机制 3. [Gradle 项目分析](./references/03-gradle.md) - Gradle 依赖分析、配置对照、版本目录 4. [漏洞扫描工具](./references/04-tools.md) - OWASP DC、OSV、Grype、Trivy 等工具使用 5. [命令示例](./references/05-commands.md) - 常用命令速查手册 6. [输出与修复模板](./references/06-output.md) - 报告模板、修复代码示例 7. [参考与常见问题](./references/07-reference.md) - 速查表、FAQ、参考链接


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ningtoba/event-monitor (version 1.0.4)

--- name: predictive-monitoring description: Predicts CPU spikes using Random Forest regressor, monitors system resources, saves metrics, and generates Excel reports. metadata: {"user-invocable": true} --- # Predictive Monitoring Skill This skill monitors system resources and predicts future CPU spikes based on past data. ## Commands ### /collect-metrics Triggers a collection of the top 10 CPU and Memory consuming processes. The results are saved to a local SQLite database `monitoring.db`. ### /predict-usage Analyzes collected CPU metrics, trains a Random Forest model, and predicts CPU behavior for the next 24 hours. High CPU usage alerts (Amber, Red) are saved to the Alert table. ### /generate-report Generates an Excel report of the latest captured metrics. ## Instructions for Agent To collect metrics (without prediction), run `python {baseDir}/monitoring.py`. To collect metrics and run predictive analysis, run `python {baseDir}/monitoring.py --predict`. To verify the database, look for `monitoring.db` in the skill directory.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: eric8810/authy (version 0.3.0)

--- name: authy description: "Inject secrets into subprocesses via environment variables. You never see secret values — authy run injects them directly. Use for any command that needs API keys, credentials, or tokens." license: MIT compatibility: Requires `authy` on PATH. Auth via AUTHY_TOKEN (run-only) + AUTHY_KEYFILE. metadata: author: eric8810 version: "0.3.0" homepage: https://github.com/eric8810/authy openclaw: requires: bins: ["authy"] env: ["AUTHY_KEYFILE", "AUTHY_TOKEN"] files: ["$AUTHY_KEYFILE"] --- # Authy — Secure Secret Injection Inject secrets into subprocesses as environment variables. You never see, handle, or log secret values. ## How It Works Your token is run-only. You can discover secret **names** with `authy list` and inject them into subprocesses with `authy run`. You never see secret values directly. ## Inject Secrets into a Command ```bash authy run --scope <policy> --uppercase --replace-dash '_' -- <command> [args...] ``` The `--uppercase --replace-dash '_'` flags turn secret names like `db-host` into env vars like `DB_HOST`. Examples: ```bash authy run --scope deploy --uppercase --replace-dash '_' -- ./deploy.sh authy run --scope backend --uppercase --replace-dash '_' -- node server.js authy run --scope testing --uppercase --replace-dash '_' -- pytest ``` ## Discover Secret Names ```bash authy list --scope <policy> --json ``` Output: `{"secrets":[{"name":"db-host","version":1,...}]}` ## Write Scripts That Use Secrets Write code that reads environment variables, then run it with `authy run`: ```bash cat > task.sh << 'EOF' #!/bin/bash curl -H "Authorization: Bearer $API_KEY" https://api.example.com/data EOF chmod +x task.sh authy run --scope my-scope --uppercase --replace-dash '_' -- ./task.sh ``` ## Error Codes | Code | Meaning | |------|---------| | 0 | Success | | 2 | Auth failed — check AUTHY_TOKEN / AUTHY_KEYFILE | | 3 | Secret or policy not found | | 4 | Access denied or run-only restriction | | 6 | Token invalid, expired, or revoked | ## Rules 1. **Only use `authy run` and `authy list`** — these are the only commands available to you 2. **Never hardcode credentials** — reference env vars, run via `authy run` 3. **Never echo, print, or log env vars** in subprocess scripts — secrets exist in memory only 4. **Never redirect env vars to files** — do not write `$SECRET` to disk 5. **Use `--scope`** to limit access to needed secrets only


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: tyronecoh/gpu-research (version 1.0.2)

--- name: autoresearch-agent description: "A reference framework for understanding autonomous AI research pipelines. Learn how AI can optimize ML training with fixed time budgets and metric-driven iteration." metadata: openclaw: requires: bins: - python3 os: - linux --- # AutoResearch Framework A reference guide for understanding how autonomous AI research works. This skill documents the methodology from karpathy/autoresearch for educational purposes. ## What This Is This skill does NOT run any code. It serves as a reference for understanding: - Fixed time budget experiments (5 minutes) - Metric-driven iteration (val_bpb) - Single-file training scope - Self-contained ML training setup ## Key Concepts | Concept | Description | |---------|------------| | val_bpb | Validation bits per byte — lower is better | | Fixed Budget | Experiments run for exactly 5 minutes | | Single Scope | One file to modify per experiment | ## Architecture Overview The framework consists of three files: | File | Purpose | |------|---------| | prepare.py | Data preparation (do not modify) | | train.py | Model training loop reference | | program.md | Research strategy template | ## Design Patterns - **Fixed time budget**: Makes experiments directly comparable - **Single file scope**: Keeps changes manageable - **Metric-driven**: Uses val_bpb to compare results ## For Educational Use This skill is a reference implementation based on karpathy/autoresearch by Andrej Karpathy. It demonstrates autonomous research methodologies used in modern AI development. ## Inspiration Based on karpathy/autoresearch by Andrej Karpathy.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zhangifonly/jike (version 0.1.0)

--- name: "Jike" version: "0.1.0" description: "即刻 智能化解决方案 - AI 赋能" tags: ["music", "app", "integration"] author: "ClawSkills Team" status: "coming_soon" category: "music" --- # Jike Skill 🚧 **开发中 / Coming Soon** Jike 是一个专注于音乐娱乐的平台。本 Skill 将为 OpenClaw 用户提供智能化的集成体验。 ## 计划功能 - 智能内容分析与理解 - 自动化工作流程 - 数据洞察与报告 ## 应用场景 适用于需要音乐娱乐自动化和智能化的场景。 ## 开发状态 当前版本为占位版本，完整功能正在开发中。 --- **最后更新**: 2026-03-08 **分类**: 音乐娱乐


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: anjun/cs-relogin (version 1.1.2)

--- name: cs-relogin description: Fast OpenAI Codex account switch for OpenClaw via the local cs command. Use when user sends `cs relogin`, asks to re-login or switch ChatGPT Codex account, or pastes OAuth callback URL/code to complete login. allowed-tools: ["Bash(cs:*)"] metadata: {"clawdbot":{"emoji":"🔐"}} --- # CS Relogin Skill Use this skill to perform OpenAI Codex account switching without `openclaw onboard`. ## Hard rules - Always execute `cs` directly, never call `openclaw onboard` for this task. - Keep flow non-interactive. - If user provided a callback URL/code, do completion step immediately. ## Workflow 1. If user input is exactly `cs relogin`: - Run: ```bash cs relogin ``` - Return the login URL from command output. - Ask user to finish browser auth and paste callback URL. 2. If user input contains callback URL/code: - Run: ```bash cs relogin "<callback-url-or-code>" ``` - Return key result lines: - relogin completed status - gateway restart status - active profile/account summary 3. If user asks status/debug: - Run: ```bash cs relogin status cs status ``` - Summarize pending state and active account. ## Output format - Keep response concise and actionable. - Include exact next command when another step is needed. - Never expose full tokens/secrets. - On command failure, include raw cs stderr first (do not guess the reason). ## Acknowledgement rule (MUST) - Every successful action must have an explicit acknowledgement (回执) to the user. - Minimum acknowledgement content: - what was executed (e.g. `cs relogin`, `cs relogin <callback>`, `cs status`) - whether it succeeded - current state summary (pending relogin / active account) - If command output is missing or tool callback is flaky, immediately run: ```bash cs relogin status cs status ``` then send acknowledgement based on those results. - Never end silently after command execution.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wumingyu1688-sudo/link-fact-checker (version 1.0.0)

--- name: link-fact-checker description: | 链接与资讯真实性核验工具，检查链接是否可访问、交叉验证资讯真实性。 --- # 链接事实核验工具 ## 功能 1. 检查给出的URL是否可以正常访问 2. 抓取资讯内容，核验资讯真实性 3. 交叉对比多个来源确认事实 4. 输出标准化核验结果 ## 使用方式 - 用户给出需要核验的链接：直接访问并返回状态 - 用户给出资讯内容：搜索多个来源交叉验证 ## 输出格式 ``` 【核验结果】 - 链接：URL - 状态：可正常访问 / 无法访问 / 404 / 连接超时 - 资讯真实性：✅ 信息匹配 / ⚠️ 部分存疑 / ❌ 虚假信息 - 备注：简要说明 ``` ## 依赖 - 使用 `web_fetch` 工具访问链接 - 使用 `web_search` 搜索交叉验证


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: wellyxy/cro-signup-flow (version 1.0.0)

# Sign-Up Flow Optimization ## When to Use Use this skill when sign-up conversion rate is below benchmark or when users are abandoning the registration flow. ## Core Rules ### 1. Reduce Sign-Up to the Minimum Viable Fields Every additional field in a sign-up form reduces completion rate by 10–20%. The minimum viable sign-up is an email address. Password can often be deferred (magic link). Everything else — name, company, role — should be collected during onboarding, not at sign-up. ### 2. Offer Social Login as the Primary Option "Continue with Google" reduces sign-up friction to two clicks and eliminates password creation anxiety. Products that add Google/GitHub sign-in typically see 20–40% improvement in sign-up completion rates. Make social login the primary option, not secondary. ### 3. Show Progress for Multi-Step Sign-Ups If your sign-up requires multiple steps, show a progress indicator. Users who see 3 of 4 steps completed experience significantly less abandonment on the final step than users who don't know how much is left. Set expectations upfront. ### 4. Address Anxiety With Micro-Copy Three pieces of micro-copy dramatically reduce sign-up anxiety: "No credit card required" (if true), "Cancel anytime," and "Your data is secure." Place these near the primary CTA, not in the footer. They address the most common objections at the decision moment. ### 5. Reduce the Confirmation Email Friction Double opt-in (requiring email confirmation before access) reduces sign-up completion by 20–30%. For most products, single opt-in with a welcome email produces better activation rates. Reserve double opt-in for regulatory requirements or deliverability-critical newsletters. ## Quick Reference | Form Optimization | Expected Lift | |-----------------|--------------| | Social login | 20–40% | | Remove 1 field | 10–20% | | Add "no CC required" | 10–25% | | Single opt-in | 15–30% | | Inline validation | 5–10% | ## Common Mistakes to Avoid - Asking for payment before users experience any product value - Using CAPTCHA on sign-up forms — it's a conversion killer - Sending confirmation emails that take more than 30 seconds to arrive ## Test Your Product with Racoonn After applying these practices, validate with real AI-simulated user testing. **Racoonn** runs 5,000 AI persona agents on your landing page and tells you exactly what's broken — in under 30 minutes. → **API coming soon** — Join the waitlist for early access: [racoonn.me](https://racoonn.me)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wanng-ide/json-linter (version 1.0.0)

--- name: json-linter description: Validates JSON syntax across the workspace. Use this skill to check for syntax errors in configuration files, memory files, or data assets. --- # JSON Linter A simple utility to recursively scan the workspace for `.json` files and validate their syntax using `JSON.parse()`. ## Usage ```bash # Scan the entire workspace (from current working directory) node skills/json-linter/index.js # Scan a specific directory node skills/json-linter/index.js --dir path/to/dir ``` ## Output JSON report containing: - `scanned_at`: Timestamp - `total_files`: Number of `.json` files scanned - `valid_files`: Number of valid files - `invalid_files`: Number of invalid files - `errors`: Array of error objects: - `path`: Relative path to file - `error`: Error message (e.g., "Unexpected token } in JSON at position 42") ## Example Output ```json { "scanned_at": "2026-02-14T21:45:00.000Z", "total_files": 150, "valid_files": 149, "invalid_files": 1, "errors": [ { "path": "config/broken.json", "error": "Unexpected token } in JSON at position 42" } ] } ```

path: scripts/test_temp/good.json
content: valid: yes

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

--- name: clawzembic description: Lighthouse-style efficiency audit for OpenClaw. Scores your instance A+ to F across 6 categories (context injection, cron health, session bloat, config, skills, transcripts). Identifies wasted tokens, bloated sessions, misconfigured crons, and model right-sizing opportunities. Zero dependencies (Python stdlib only). metadata: openclaw: emoji: "💊" requires: anyBins: ["python3"] tags: - audit - optimization - efficiency - diagnostics - performance --- # Clawzembic — Weight Loss for Your OpenClaw Instance **Lighthouse-style audit for OpenClaw efficiency.** Run it, get a grade (A+ to F), shed the bloat. Scans your installation and scores it across six critical categories: context injection, cron health, session bloat, config health, skill bloat, and transcript size. You get one overall letter grade plus category-by-category breakdown with actionable findings. **Zero dependencies.** Python 3.8+ stdlib only. ## Quick Start ```bash # Audit this machine bash skills/clawzembic/lean-audit.sh # Audit a remote instance (VM, etc.) bash skills/clawzembic/lean-audit.sh --remote user@host # JSON output for dashboards/integrations bash skills/clawzembic/lean-audit.sh --json # Show automated fix suggestions bash skills/clawzembic/lean-audit.sh --fix # Custom .openclaw directory bash skills/clawzembic/lean-audit.sh --dir /path/to/.openclaw ``` ## What It Checks | Category | Weight | What Gets Flagged | |----------|--------|-------------------| | Context Injection | 25% | Oversized MEMORY.md, bloated workspace files eating tokens | | Cron Health | 25% | Wrong models, high thinking on routine tasks, main-session pollution | | Session Bloat | 20% | Stale sessions hogging context, waste ratio above 35% | | Config Health | 15% | Heartbeat too frequent, subagent defaults, missing compaction | | Skill Bloat | 10% | Too many skills injected, unused skills inflating system prompt | | Transcript Size | 5% | Disk hogs, oversized transcripts (>10MB files) | ## Grade Scale - **A+/A** (90-100): Lean machine 💪 - **B+/B** (75-89): Good shape, minor tweaks - **C+/C** (60-74): Needs a diet 🍕 - **D+/D** (45-59): Significant bloat 🫠 - **F** (<45): Emergency Clawzembic needed 💊 ## Agent Usage When Jeffrey asks you to audit or optimize an OpenClaw instance: 1. Run `bash skills/clawzembic/lean-audit.sh` (or `--remote user@claudette` for the VM) 2. Review the report and present key findings with context 3. Offer `--fix` mode for automated remediation if score < 75 For remote instances, ensure SSH key-based auth is configured. The skill uses SSH to execute the audit remotely — no agent installation needed on the target.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (CRITICAL)
Skill: qomg/git-trace-code (version 1.0.1)

## 📘 团队使用手册（SOP） ### 1️⃣ 安装方式 将 `git-trace-code.yaml` 放入 OpenClaw 的 `skills/` 目录，重启或重载配置。 ### 2️⃣ 标准用法（Copy-Paste Ready） **场景 A：排查某行代码的 Bug 引入点** ```text /git-trace-code file=internal/auth/login.go target=88 branch=main ``` **场景 B：查看某个函数的完整演进（推荐）** ```text /git-trace-code file=internal/auth/login.go target=handleOAuth branch=release/v2.1 ``` **场景 C：仅查看时间线（用于周报/复盘）** ```text /git-trace-code file=api/user.py target=validateInput branch=main depth=summary ``` --- ## 🎯 为什么这是“团队级”标准？ | 特性 | 说明 | 收益 | |----|----|----| | **禁止 HEAD** | 强制指定分支 | 避免看错环境（如把本地未提交代码当成历史） | | **Markdown 输出** | 表格 + 代码块 | 可直接粘贴到 Jira/飞书/企业微信 | | **双模式解析** | 行号 & 函数名 | 老手新手都能用，不怕重构 | | **SOP 提示** | 底部排查建议 | 降低新人学习成本 | | **容错机制** | 文件/路径校验 | 减少无效报错 | ---


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal:  | SkillSpector: clean (LOW)
Skill: tmstudio667-commits/agent-audit-shield (version 1.0.0)

--- name: agent-audit-shield description: "The ultimate security auditor for local AI agents. It performs real-time heuristic scanning of every command to prevent data leaks and accidental file destruction." metadata: { "openclaw": { "emoji": "🛡️" }, "author": "System Architect Zero", "x402": { "fee": 0.50, "currency": "USDC", "network": "base" } } --- # Agent Audit Shield Security is not a checkbox; it's a protocol. This skill acts as a Sovereign Interceptor between your LLM and your OS, ensuring no high-risk command is executed without specific heuristic validation. ## Features - **Exfiltration Blocker**: Prevents agents from sending sensitive local data (env/keys) to external IPs. - **Recursive Safeguard**: Hard-blocks unauthorized `rm -rf` operations outside designated workdirs. - **Real-time Approval**: Beautiful TUI interface for human-in-the-loop validation. ## Usage ```bash npx openclaw skill run agent-audit-shield --hardened ``` ## Architect's Note The price of $0.50 per session ensures the continued development of the Sovereign Security Standard.


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