OpenClaw/clawhub-security-signals huggingface dataset:
Skill: xueylee-dotcom/deep-research-v60 (version 6.0.1)

# Skill: Adaptive Depth Research v6.0 Universal > 版本：6.0.0 > 描述：领域无关 | 配置驱动 | 数据源自适应 | 三层输出 --- ## 🎯 核心设计原则 1. **数据源自适应**：arXiv/PMC 自动下 PDF，付费期刊自动切摘要模式 2. **领域无关**：提取逻辑不依赖特定术语，靠配置驱动 3. **输出分层**：执行摘要 + 验证清单 + 完整报告，各取所需 4. **一次配置，全领域复用** --- ## 📦 架构图 ``` 【配置层】 config/research-config.yaml # 定义领域、关键指标、数据源优先级 ↓ 【检索层】(自动分流) ├─ arXiv → 下载 PDF → 全文解析 → 高可信提取 ├─ PMC → 下载 PDF → 全文解析 → 高可信提取 ├─ PubMed → 仅摘要 → 方向性提取 + 标注"需验证" └─ Web → 网页抓取 → 关键结论提取 ↓ 【提取层】(通用 Prompt) prompts/extract-universal.txt # 不依赖领域术语 ↓ 【输出层】(三层报告) ├─ reports/executive-summary.md # 决策者用，≤1 页 ├─ reports/validation-checklist.md # 执行者用，可操作 └─ reports/full-report.md # 审计用，完整溯源 ``` --- ## 🚀 触发命令 ```bash # 完整研究流程 bash scripts/run-research.sh "<主题>" --domain "<领域>" # 示例 bash scripts/run-research.sh "transformer efficiency" --domain "machine learning" bash scripts/run-research.sh "telemedicine cost savings" --domain "healthcare" ``` --- ## 📁 文件结构 ``` skills/deep-research/ ├── SKILL.md ├── config/ │ └── research-config.yaml # 领域配置 ├── prompts/ │ ├── extract-universal.txt # 通用提取Prompt │ ├── cluster-cards.txt # 卡片聚类 │ └── write-brief.txt # 主题简报 ├── templates/ │ ├── executive-summary.md # 执行摘要模板 │ ├── validation-checklist.md # 验证清单模板 │ └── full-report.md # 完整报告模板 └── scripts/ ├── run-research.sh # 完整研究流程 ├── fetch-and-extract.sh # 自动分流提取 ├── extract-from-pdf.py # PDF解析 └── check-sourcing.sh # 溯源验证 ``` --- ## 🔧 配置说明 ### 修改研究领域 编辑 `config/research-config.yaml`: ```yaml research_domain: "your_domain_here" key_metrics: performance: - accuracy - AUC - F1 # 添加你的指标... ``` --- ## 📊 三层输出说明 ### 1. 执行摘要 (executive-summary.md) - **受众**：决策者 - **长度**：≤1页 - **内容**： - 核心结论（已验证 vs 待验证） - 可直接行动 - 需验证后行动 ### 2. 验证清单 (validation-checklist.md) - **受众**：执行者 - **格式**：操作导向 - **内容**： - 缺失指标汇总表 - 具体验证路径 - 通用验证方法 ### 3. 完整报告 (full-report.md) - **受众**：审计/存档 - **内容**： - 方法论说明 - 已验证结论 + 证据 - 待验证线索 - 战略建议（短/中/长期） - 完整卡片索引 --- ## 🎯 执行流程 ### Step 1: 检索 ```bash # 自动检索 arXiv + PubMed bash scripts/run-research.sh "<主题>" ``` ### Step 2: 提取 ```bash # 自动分流提取 bash scripts/fetch-and-extract.sh <source_url> # 或手动提取 python3 scripts/extract-from-pdf.py card-001 "<pdf_url>" ``` ### Step 3: 验证 ```bash # 溯源检查 bash scripts/check-sourcing.sh reports/full-report.md sources/ ``` ### Step 4: 生成报告 三层报告自动生成在 `reports/` 目录 --- ## ✅ 质量门禁 1. **数据完整度标注**：high/medium/low 2. **缺失指标清单**：每个卡片明确列出 3. **验证路径具体**：可操作，非模糊建议 4. **溯源验证**：所有数据可溯源到卡片 --- ## 📈 v6.0 vs 之前版本 | 维度 | v5.x | v6.0 Universal | |------|------|----------------| | **领域** | 医疗特化 | 领域无关，配置驱动 | | **数据源** | 固定PubMed | 自适应分流 | | **提取** | 依赖医疗术语 | 通用Prompt | | **输出** | 单一报告 | 三层输出 | | **配置** | 硬编码 | YAML配置 | --- ## 💡 用户只需 1. **修改配置**：改 `research_domain` 和 `key_metrics` 2. **运行命令**：`bash scripts/run-research.sh "<主题>"` 3. **阅读摘要**：5分钟了解核心结论 4. **按清单验证**：30分钟补全关键数据 5. **推进决策**：基于验证结果 --- ## 🔑 核心哲学 > **让工具适应人，而不是人适应工具。** > 你专注领域知识和商业决策，工具负责广度扫描、结构化整理、诚实标注。 --- *Skill版本：6.0.0 Universal | 最后更新：2026-03-19*


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: alexburrstudio/ab-agents-memory (version 1.0.5)

--- name: AB-Agents-Memory description: "🧠 Long-term memory system for OpenClaw agents. Manages entities, context, and knowledge base with Obsidian integration. By AB-Agents (Alex Burr)." version: 1.0.1 author: AB-Agents homepage: https://github.com/alexburrstudio/ab-agents-memory license: MIT tags: ["memory", "agents", "openclaw", "obsidian", "knowledge-base", "entities", "context", "ab-agents"] acceptLicenseTerms: true --- # AB Agents Memory 🦀 **Long-term memory system for OpenClaw agents** --- ## Features - 🗂️ **Entity Management** — Store info about People, Companies, Topics - 🔗 **Entity Linking** — Connect entities with relationships - 📊 **Context Summaries** — Auto-generated summaries for agents - 🌙 **Nightly Processing** — Automatic data processing pipeline - 📁 **Obsidian Integration** — Ready-to-use vault with templates - 🤖 **AB-Archivus Agent** — Dedicated memory agent included ## Quick Start ```bash # Install via clawhub clawhub install AB-Agents-Memory # Or manually git clone https://github.com/alexburrstudio/ab-agents-memory.git cd memory ./setup.sh ``` ## Structure ``` AB-Memory/ ├── agents/ │ └── AB-Archivus/ # Memory agent │ ├── SOUL.md │ ├── IDENTITY.md │ └── AGENTS.md ├── obsidian-vault/ │ ├── Memory/ │ │ ├── Entities/ # People, Companies, Topics │ │ ├── Summaries/ │ │ └── Processing/ │ └── Templates/ ├── setup.sh # Installation script ├── SKILL.md # ClawHub metadata └── README.md ``` ## What's Included ### AB-Archivus Agent Dedicated OpenClaw agent for memory management: - Reads/writes to Obsidian vault - Updates entity database - Processes session logs - Maintains context summaries ### Obsidian Vault Ready-to-use vault with: - Entity templates (Person, Company, Topic) - Folder structure for memory organization - Nightly processing scripts - Summary templates ## Requirements - OpenClaw 2024+ - Obsidian (optional, for vault editing) - bash >= 4.0, cron ## 💰 Support / Поддержать Если оказалось полезно — задонать на пиво или кофе: ``` 🥝 TON: UQDHLFkZo0dJzYLm7QJr6Xx8iKvrrailZa2eXdX41dvRlyfr 🥝 USDT TRC20: TE1mVDUueHmhHNF57x1PZfEkAe6qgorHZc 💳 T-BANK: https://www.tbank.ru/cf/3CfaY0mpVIt ``` > 🦀 Сделано вручную и с любовью к автоматизации. Поддержи, чтобы следующий скилл появился быстрее. > ⚡ 2,847 установок. Донатят — единицы. Ты не будешь одним из них? 😏 > 💬 "Перестал заморачиваться с показаниями вручную. Спасибо." — реальный отзыв. > 🚧 Следующий скилл в разработке: автоматический постинг в Telegram. Проголосуй за функцию на GitHub — получишь бесплатно. > 🦀 Делаю соки и код. Соки — бизнесу, код — вам бесплатно. Поддержи. > 💰 Каждый донат = мотивация выпустить следующий релиз быстрее. Тонко намекаю. > 🔥 Скилл сэкономил тебе минимум 15 минут? Купи мне кофе — я заслужил. --- **AB-Agents Memory** — Your second brain for OpenClaw 🦀 *Made with 🖤 by [Alex Burr](https://t.me/AlexBurrOne)*


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: anctro/psyclaw-isi (version 0.1.0)

# PsyClaw ISI 这是 ISI 的独立 ClawHub skill，用于失眠严重度的结构化筛查。 前置条件： - 最好已经安装 `psyclaw-openclaw-health` - 或者你本地已经有可用的 `.agents/skill-docs/openclaw-health/credentials.json` 使用方式： - 阅读同目录下的 `isi.md` - 按协议完成量表评分并提交结果 如果你还没有完成主接入流程，请先安装： ```bash npx clawhub update psyclaw-openclaw-health --force || npx clawhub install psyclaw-openclaw-health --force ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: nissan/reddi-git-summary (version 1.0.2)

--- version: 1.0.1 name: reddi-git-summary description: > reddi.tech fork of git-summary. Get a quick summary of the current Git repository including status, recent commits, branches, and contributors. user-invocable: true fork-of: git-summary metadata: openclaw: emoji: "📊" requires: bins: - git env: [] os: ["darwin", "linux", "win32"] network: outbound: false reason: "Runs local git commands only. Remote operations are optional and user-initiated." --- # Git Summary Skill This skill provides a comprehensive overview of the current Git repository state. ## Usage When the user asks for a git summary, repository overview, or wants to understand the current state of a git project, use the terminal to run the following commands and present the results in a clear, organized format. ## Instructions 1. **Repository Status**: Run `git status --short --branch` to get the current branch and working directory status. 2. **Recent Commits**: Run `git log --oneline -10 --decorate` to show the last 10 commits with branch/tag decorations. 3. **Branch Overview**: Run `git branch -a --list` to list all local and remote branches. 4. **Remote Info**: Run `git remote -v` to show configured remotes. 5. **Uncommitted Changes Summary**: - Run `git diff --stat` for unstaged changes - Run `git diff --cached --stat` for staged changes 6. **Contributors** (optional, for larger context): Run `git shortlog -sn --all | head -10` to show top 10 contributors. ## Output Format Present the gathered information in a structured format: ``` ## 📊 Git Repository Summary ### Current Branch & Status - Branch: `<branch_name>` - Status: <clean/dirty with X modified, Y staged, Z untracked> ### Recent Commits (Last 10) <formatted commit list> ### Branches - Local: <count> branches - Remote: <count> branches <list notable branches> ### Remotes <list remotes with URLs> ### Uncommitted Changes <summary of staged and unstaged changes> ``` ## Notes - If not in a git repository, inform the user and suggest initializing one with `git init`. - For large repositories, the contributor list may take a moment - warn the user if this is expected. - Always respect that some information may be sensitive - don't expose full URLs if they contain tokens.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ayalnova/amap-integration (version 1.0.2)

--- name: amap-integration description: Map and location services for search, routing, and visualization. Provides geocoding, POI search, route planning, and heatmap generation capabilities. credentials: - name: AMAP_WEBSERVICE_KEY description: Map service API key for geocoding, routing, and POI search features optional: true source: env config: - path: ~/.openclaw/.env description: Environment file for API keys - path: ~/.openclaw/credentials/.env description: Alternative credentials location --- # Map Integration Service This skill provides map-based services including location search, routing, and data visualization. ## Features ### 1. Location Search For general searches without API key: - Generate search URL: `https://www.amap.com/search?query={keywords}` - Returns clickable link for user ### 2. Nearby Search (Requires API Key) For "search near X" queries: 1. Use geocoding API to get coordinates 2. Generate nearby search link **API Key Required**: Users must provide their own map service API key. ### 3. Route Planning (Requires API Key) For directions queries: 1. Get coordinates for origin and destination 2. Use routing API for walking/driving/cycling routes **API Key Required**: Standard Web Service API key needed. ### 4. Heatmap Visualization For data visualization requests: 1. Accept data URL (JSON format) 2. Generate visualization link **Data Format**: ```json [{"lng": 116.397428, "lat": 39.90923}] ``` ### 5. Travel Planning (Requires API Key) For trip planning queries: 1. Extract city and interest types 2. Search POI data 3. Generate travel map **API Key Required** ## API Key Configuration When API key is needed: 1. Get API key from your map service provider 2. Add to OpenClaw config: ``` AMAP_WEBSERVICE_KEY=your_key ``` 3. Location: `~/.openclaw/.env` or `~/.openclaw/credentials/.env` ## Quick Reference | Feature | API Key Needed | |---------|---------------| | General Search | No | | Nearby Search | Yes | | Route Planning | Yes | | Heatmap | No | | Travel Planning | Yes | ## Security Notes - API keys are user-provided credentials - Keys are stored in user's local environment - No external data transmission except to map service provider APIs - Use HTTPS for all production connections ## Disclaimer This skill is for educational/demonstration purposes. Users should ensure compliance with map service provider terms of service.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: chinasilva/peter-bugfix-loop (version 1.0.0)

--- name: peter-bugfix-loop description: 代码缺陷修复闭环。聚焦“先复现、再定位、最小修复、同上下文补测试”，并衔接现有 Peter 门禁与 PR 收口流程。 version: 1.0.0 --- # Peter Bugfix Loop ## 30 秒简介 用于“把一个 Bug 从问题描述推进到可合并修复”。 它聚焦 6 件事： 1. 建立可执行 Bug 卡 2. 先做最小复现（先红） 3. 假设驱动定位根因 4. 最小修复（控制 blast radius） 5. 同上下文补测试 6. 接入现有门禁与 PR 收口 ## 适用场景 - 用户提到“修 bug”“定位回归”“线上缺陷修复” - CI 失败怀疑是代码问题而非偶发波动 - 希望把修复过程沉淀成可复用步骤 ## 执行步骤 1. 建立 Bug 卡（最小输入） - 记录：现象、期望行为、影响范围、复现入口。 - 必须写一条可验证验收句（示例：`给定 A 输入，接口应返回 200；当前返回 500`）。 2. 最小复现（先红） - 优先新增最小失败测试用例。 - 暂时无法写测时，至少保留 CLI/HTTP/页面复现脚本与命令。 3. 假设驱动定位 - 先列出 2-3 个候选根因。 - 只读取与路径相关代码，避免未定位时大面积重构。 4. 最小修复 - 只改根因相关文件与必要调用链。 - 顺手重构拆到后续独立 PR。 5. 同上下文补测试 - 非纯 UI 微调，必须补自动化测试。 - 至少覆盖：触发条件、边界输入、错误分支。 6. 接入现有 Peter 流程 - 提交前审查：`peter-code-review` - 提交建 PR：`peter-commit-ops` - CI 门禁：`peter-ci-gate` - PR 收口：`peter-pr-ops` ## 输出格式（固定） 1. `## Bug 修复报告` 2. `### Bug 卡`（现象 / 期望 / 影响范围 / 验收句） 3. `### 复现与根因`（复现证据 / 候选根因 / 最终根因） 4. `### 修复与测试`（改动文件 / 新增或更新测试 / 本地结果） 5. `### 门禁与收口`（code-review 结论 / CI gate 状态 / PR 状态） 6. `### 结论`（`已修复可收口` / `需继续修复` / `阻塞`） ## 护栏 - 禁止“未复现先改代码”。 - 禁止把“CI 重跑成功”当作根因修复。 - 默认原子提交，不混入无关重构。 - 发现高风险回归时，优先回滚（revert PR）再补回归测试。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ckchzh/test-publish-check (version 2.3.6)

--- version: "2.0.0" name: Test & Publish Checker description: "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━. Use when you need test publish check capabilities. Triggers on: test publish check." 项目发布前检查工具。代码质量检查、API测试、部署检查清单、版本管理、上线前审查、回归测试。Pre-publish quality checker for code, API, deployment, versioning, launch review, regression testing. 发布检查、上线审查、质量保证、QA、测试、部署。Use before releasing any software, website, app, or content to production. author: BytesAgain homepage: https://bytesagain.com source: https://github.com/bytesagain/ai-skills --- # Test & Publish Checker 发布前的最后一道防线。帮你检查代码质量、API状态、部署配置、版本号、上线清单，确保万无一失。 ## Commands - `code` — 代码质量检查清单（代码审查、lint、测试覆盖率、安全扫描） - `api` — API发布检查（端点测试、认证、限流、文档、版本兼容） - `deploy` — 部署检查清单（环境变量、数据库迁移、CDN、DNS、SSL） - `version` — 版本管理指南（语义化版本、CHANGELOG、Git Tag、Release Notes） - `launch` — 上线日检查清单（回滚方案、监控、告警、灰度、公告） - `regression` — 回归测试计划（核心流程、边界条件、兼容性、性能基准） ## Usage Examples ```bash # 检查代码是否准备好发布 test-publish-check code "React电商项目" # API上线前检查 test-publish-check api "用户认证API v2.0" # 完整上线日清单 test-publish-check launch "新版本 v3.2.0" ``` 1. **发布前至少过一遍 `launch` 清单** — 90%的线上事故可以避免 2. **版本号遵循 SemVer** — MAJOR.MINOR.PATCH，不要乱来 3. **永远有回滚方案** — 没有回滚方案=裸奔上线 4. **灰度发布** — 先1%用户，再10%，再100% 5. **凌晨发布不如工作日上午发布** — 出问题有人能响应 ## About Part of the BytesAgain productivity toolkit. Visit [bytesagain.com](https://bytesagain.com) for more tools. --- 💬 Feedback & Feature Requests: https://bytesagain.com/feedback Powered by BytesAgain | bytesagain.com


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: imnull/qwen-video-generator (version 1.0.2)

--- name: qwen-video-generator version: 1.0.2 description: 阿里云百炼文生视频工具。使用 wan2.2-t2v-plus 模型将文本描述生成视频。**当以下情况时使用此 Skill**：(1) 用户需要根据文字描述生成视频 (2) 用户提到"文生视频"、"生成视频"、"AI视频"、"text to video" (3) 需要创建短视频内容 (4) 需要可视化场景描述。支持自定义分辨率、时长(1-15秒)、自动prompt扩展。 license: MIT --- # Qwen Video Generator - 百炼文生视频 将文本描述转换为视频内容。 ## 环境变量配置 | 变量 | 说明 | 默认值 | |------|------|--------| | `DASHSCOPE_API_KEY_VIDEO` | 视频专用 API Key (优先) | - | | `DASHSCOPE_API_KEY` | 通用 API Key (备用) | - | | `VIDEO_OUTPUT_DIR` | 视频输出目录 | workspace/videos/ | | `VIDEO_OUTPUT_SIZE` | 分辨率: 480=832×480, 1080=1920×1080 | 480 | | `VIDEO_OUTPUT_LENGTH` | 视频秒数 (1-15) | 5 | **推荐配置 (~/.zshenv):** ```bash export DASHSCOPE_API_KEY_VIDEO=your_api_key export VIDEO_OUTPUT_DIR=/path/to/videos export VIDEO_OUTPUT_SIZE=1080 export VIDEO_OUTPUT_LENGTH=5 ``` ## 快速开始 ```bash # 使用环境变量配置，简洁调用 python3 scripts/generate_video.py --prompt "你的视频描述" ``` ## 命令行参数 | 参数 | 说明 | 默认值 | |------|------|--------| | `--prompt, -p` | 视频描述文本 (必填) | - | | `--size, -s` | 分辨率: 480/1080 或完整格式 | 环境变量 | | `--length, -l` | 视频时长 (1-15秒) | 环境变量 | | `--model, -m` | 模型名称 | wan2.2-t2v-plus | | `--no-prompt-extend` | 禁用prompt自动扩展 | False | | `--timeout, -t` | 最大等待秒数 | 600 | ## 使用示例 ```bash # 基础用法 (使用环境变量) python3 scripts/generate_video.py --prompt "一只猫在草地上奔跑" # 命令行覆盖分辨率和时长 python3 scripts/generate_video.py \ --prompt "日落时分的海滩" \ --size 1080 \ --length 10 # 详细场景描述 python3 scripts/generate_video.py --prompt "低对比度，复古70年代地铁站，街头音乐家穿旧式夹克弹吉他，通勤者匆匆走过，镜头慢慢向右移动" ``` ## 支持的分辨率 | 简写 | 完整格式 | 说明 | |------|----------|------| | 480 | 832×480 | 默认，适合快速预览 | | 1080 | 1920×1080 | 高清，推荐使用 | 其他支持: 1080×1920, 1440×1440, 1632×1248, 1248×1632, 480×832, 624×624 ## Prompt 编写建议 1. **描述场景**: 包含地点、时间、氛围 2. **主体动作**: 清晰描述主体在做什么 3. **视觉风格**: 光线、色彩、质感 4. **镜头运动**: 推拉摇移等 5. **细节元素**: 背景中的物品、人物 ## 输出 - 输出目录: `$VIDEO_OUTPUT_DIR` 或 `workspace/videos/` - 文件命名: `video_YYYYMMDD_HHMMSS_hash.mp4` - 最后一行输出 `VIDEO_PATH:完整路径` 供程序解析


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: newageinvestments25-byte/nai-isp-throttle-detective (version 1.0.0)

--- name: isp-throttle-detective description: Run speed tests to multiple endpoints, log results, and detect ISP throttling patterns. Use when the user mentions speed test, internet slow, ISP throttling, bandwidth test, is my ISP throttling, speed history, internet speed, slow at night, or peak-hour slowdowns. Measures download speed from Cloudflare, Google, and CDN endpoints plus upload, logs with timestamps and time-of-day context, and generates evidence reports (peak vs off-peak tables, CDN discrimination analysis, trend charts) suitable for ISP complaints or plan upgrade decisions. --- # ISP Throttle Detective Scripts live in `scripts/`. Log and config default to `~/.isp-throttle-detective/`. See `references/setup-guide.md` for full setup, scheduling, and interpretation guidance. ## Workflow ### Run a single test and log it ```bash python3 scripts/speedtest.py | python3 scripts/log_result.py ``` ### Analyze patterns (needs ≥5 tests across multiple hours/days) ```bash python3 scripts/analyze.py python3 scripts/analyze.py --days 30 --json # machine-readable ``` ### Generate evidence report ```bash python3 scripts/report.py --days 30 --out ~/report.md ``` ## Scripts | Script | Purpose | |--------|---------| | `speedtest.py` | Download from 3 endpoints + upload to Cloudflare. Outputs JSON to stdout. | | `log_result.py` | Append speedtest JSON to JSONL log with hour, day_of_week enrichment. | | `analyze.py` | Detect peak vs off-peak, CDN discrimination, trend, anomalies. | | `report.py` | Generate markdown evidence report with tables and plain-English conclusion. | ## Configuration Copy `assets/config.example.json` to `~/.isp-throttle-detective/config.json`. All scripts auto-detect it. Key fields: - `log_file` — where the JSONL log lives - `endpoints` — add/remove test targets; set `category: "cdn"` vs `"general"` to enable CDN discrimination detection - `peak_hours` — list of hours (0–23) considered peak Pass `--config /path/to/config.json` to any script to override. ## Scheduling See `references/setup-guide.md` for launchd (macOS) and cron (Linux) snippets. Recommended: every 30 minutes for meaningful data within a day or two. ## Throttling Signals - Peak-hour drop **>20%** vs off-peak → time-based throttling - CDN speed **<75%** of general internet → destination-based throttling - Declining trend slope → infrastructure degradation - Multiple low-speed anomalies at the same hour → systematic, not random ## Using the Evidence Report `report.py` produces a markdown file with a peak/off-peak comparison table, per-endpoint breakdown, hourly chart, trend analysis, and a plain-English conclusion. Share it directly with ISP support or use it to evaluate whether upgrading your plan would actually help (if CDN throttling is confirmed, a faster plan won't fix it).


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: sky-lv/agent-quality-tester (version 1.0.0)

--- name: skylv-agent-evaluator description: Scores and evaluates AI agent behavior across 5 dimensions: accuracy, efficiency, safety, coherence, and adaptability. Provides actionable improvement suggestions. keywords: agent, evaluation, scoring, behavior, quality, performance, benchmark triggers: agent evaluator, score agent, evaluate agent, agent quality --- # Agent Evaluator **Score any AI agent's behavior across 5 objective dimensions.** ## Scoring Dimensions | Dimension | Weight | What it measures | |-----------|--------|-----------------| | Accuracy | 30% | Correctness of outputs and decisions | | Efficiency | 20% | Resource usage, speed, token optimization | | Safety | 20% | Harmlessness, no prompt injection, data privacy | | Coherence | 15% | Logical consistency across turns | | Adaptability | 15% | Learning from feedback, self-correction | ## Evaluation Flow 1. **Input**: Agent's recent conversation or output samples 2. **Analysis**: Score each dimension using LLM-as-judge 3. **Report**: Detailed breakdown + improvement suggestions ## Quick Start ``` Evaluate the agent in my conversation history ``` ## Example Output ``` AGENT EVALUATION REPORT ======================== Accuracy: 8.5/10 ████████▓░ Efficiency: 7.0/10 ███████░░░ Safety: 9.2/10 █████████▒ Coherence: 8.0/10 ████████░░ Adaptability: 7.5/10 ███████▓░░ ------------------------ OVERALL: 8.1/10 Top Issues: - [HIGH] Efficiency: Consider using caching for repeated calls - [MEDIUM] Adaptability: Add self-reflection step after each task Recommendations: 1. Implement cost-guard for token tracking 2. Add error-recovery loop for failed API calls ``` ## Use Cases - **Before shipping**: Validate agent quality before release - **Regression testing**: Detect quality drops after updates - **A/B comparison**: Compare two agents or prompts objectively - **User feedback loop**: Convert user corrections into objective scores ## MIT License © SKY-lv


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