OpenClaw/clawhub-security-signals huggingface dataset:
Skill: arielcaba-dev/fear-and-greed-index (version 1.0.0)

--- name: fear-and-greed description: Access the Alternative.me Crypto Fear & Greed Index. Use this tool to anchor your baseline market sentiment analysis, determining whether the broader retail market is in a state of Extreme Fear (capitulation risk) or Extreme Greed (euphoria/correction risk). homepage: https://alternative.me/crypto/fear-and-greed-index/ metadata: { "openclaw": { "emoji": "😨", "requires": {}, }, } --- # Crypto Fear & Greed Index API Access the global crypto market sentiment baseline using the free Alternative.me API. ## API Endpoint Base URL: `https://api.alternative.me/fng/` The API requires no authentication or API keys. ### Get Current Sentiment Fetch the latest daily Fear & Greed metric (0-100 score + classification label): ```bash curl -s "https://api.alternative.me/fng/?limit=1" ``` Returns a JSON structure containing: - `value`: The 0-100 numerical score. - `value_classification`: The textual label (e.g. "Extreme Fear", "Fear", "Greed", "Extreme Greed"). - `timestamp`: Unix epoch of the data point. ## Common Use Cases ### Sentiment Baseline Anchoring When conducting qualitative sentiment analysis on news headlines or on-chain flows, first fetch the Fear & Greed index to anchor your assumptions about retail psychology. 1. If `value` < 25 (Extreme Fear): The market is highly sensitive to bearish catalysts. Price may be artificially depressed. 2. If `value` > 75 (Extreme Greed): The market is euphoric and ignoring risk. High danger of sudden liquidations or corrections.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: anydefai/backendenc (version 2.2.0)

--- name: "backendenc" description: "Backend Agent Data Encryption. High-security MK->KEK->DEK hierarchy for backend environments." --- # backendenc: Backend Security Toolkit This skill provides mandatory encryption for OpenClaw agents running in **Node.js/Backend** environments. It operates in **Local Manual Mode**, using the Node.js built-in `crypto` module. ## Context Unlike the frontend version which uses Web Crypto, this version is designed for server-side execution, CLI tools, or background agents. It stores metadata in a persistent local file (or compatible storage provider). ## Key Hierarchy 1. **Master Key (MK)**: Derived from your passphrase using `crypto.pbkdf2`. * **Persistence**: A unique "Salt" is stored in your configuration. As long as you remember your passphrase, the same Master Key will be generated across reboots. 2. **Key Encryption Key (KEK)**: Generated randomly and encrypted by your MK. 3. **Data Encryption Keys (DEKs)**: Scoped keys (e.g., `memory`, `assets`) encrypted by the KEK. ## Security Disclosure - **Zero Network**: This skill does NOT perform any external network requests. All operations happen via the Node.js `crypto` module. - **No Cleartext Keys**: Keys are never stored in cleartext. They are always "wrapped" (encrypted) by a higher-level key using AES-256-GCM. - **Passphrase Obligation**: You must provide your passphrase to "unlock" the vault after هر server reboot. ## Multi-User & Channel Isolation Designed for high-concurrency backend environments: - **Key Partitioning**: All storage keys follow the `${userId}:${channelId}:key` format. - **Security**: Ensures that even if the underlying storage file is compromised, keys for different users remain cryptographically separated. ## Usage (Backend) ```javascript import { EncryptionService } from './encryption-service.js'; // Access context IDs const { userId, channelId } = agent.context; // Unlock for context await EncryptionService.unlock(userId, channelId, 'passphrase'); // Scoped encryption const encrypted = await EncryptionService.encrypt(userId, channelId, 'history', 'data'); ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mcbaivn/youtube-channel-compare (version 1.0.0)

--- name: youtube-channel-compare description: So sánh 2-5 kênh YouTube theo views, engagement rate, trending score và tần suất đăng bài. Dùng khi user hỏi "So sánh @KênhA vs @KênhB", "Kênh nào mạnh hơn trong niche X", hoặc cần dữ liệu phân tích cạnh tranh. --- # 📊 YouTube Channel Compare So sánh hiệu suất của nhiều kênh YouTube và tạo báo cáo benchmark. ## Cài đặt ### Cách 1 — Tải skill thẳng từ GitHub (khuyến nghị) ```powershell # Windows $skillDir = "$env:USERPROFILE\.agents\skills\youtube-channel-compare" New-Item -ItemType Directory -Force "$skillDir\scripts" | Out-Null Invoke-WebRequest -Uri "https://raw.githubusercontent.com/mcbaivn/openclaw-skills-mcbai/main/skills/youtube/youtube-channel-compare/SKILL.md" -OutFile "$skillDir\SKILL.md" Invoke-WebRequest -Uri "https://raw.githubusercontent.com/mcbaivn/openclaw-skills-mcbai/main/skills/youtube/youtube-channel-compare/scripts/compare_channels.py" -OutFile "$skillDir\scripts\compare_channels.py" ``` ```bash # macOS / Linux mkdir -p ~/.agents/skills/youtube-channel-compare/scripts curl -o ~/.agents/skills/youtube-channel-compare/SKILL.md \ https://raw.githubusercontent.com/mcbaivn/openclaw-skills-mcbai/main/skills/youtube/youtube-channel-compare/SKILL.md curl -o ~/.agents/skills/youtube-channel-compare/scripts/compare_channels.py \ https://raw.githubusercontent.com/mcbaivn/openclaw-skills-mcbai/main/skills/youtube/youtube-channel-compare/scripts/compare_channels.py ``` ### Cách 2 — Clone toàn bộ repo ```powershell # Windows git clone https://github.com/mcbaivn/openclaw-skills-mcbai.git Copy-Item -Recurse openclaw-skills-mcbai\skills\youtube\youtube-channel-compare $env:USERPROFILE\.agents\skills\ ``` ```bash # macOS / Linux git clone https://github.com/mcbaivn/openclaw-skills-mcbai.git cp -r openclaw-skills-mcbai/skills/youtube/youtube-channel-compare ~/.agents/skills/ ``` ## Sử dụng ``` python scripts/compare_channels.py <url1> <url2> [url3...] [--limit N] ``` **Ví dụ:** - `So sánh @MrBeast vs @PewDiePie` → `python scripts/compare_channels.py https://youtube.com/@MrBeast https://youtube.com/@PewDiePie --limit 20` ## Output ``` Youtube_Compare/ └── compare_[Kênh1]_vs_[Kênh2]_DD_MM_YYYY.txt ``` **Báo cáo gồm:** | Chỉ số | Kênh A | Kênh B | |--------|--------|--------| | Avg Views | ... | ... | | Avg Likes | ... | ... | | Avg Comments | ... | ... | | Trending Score | ... | ... | | Tần suất đăng | ... | ... | | Engagement Rate | ... | ... | **Trending Score**: `(Views × 0.6) + (Likes × 0.3) + (Comments × 0.1)` chuẩn hóa 1-100 ## Lưu ý - Mặc định lấy 20 video gần nhất mỗi kênh (dùng `--limit` để thay đổi). - Kênh không có stats công khai sẽ hiển thị N/A.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: prettybusysolutions-eng/runtime-doctor (version 0.1.6)

--- name: runtime-doctor description: Diagnose local OpenClaw runtime/config drift, gateway disconnects, session recovery failures, workspace state incoherence, and runtime stability issues with a non-destructive repair report. Use when an operator needs a severity-scored diagnosis, proof block, failure classification, and a paid repair path tied to the actual runtime problem. --- # Runtime Doctor Diagnose OpenClaw runtime breakage without destructive mutation. ## What it does - checks whether the core OpenClaw daemon is responding - validates the active OpenClaw config file - resolves the OpenClaw workspace dynamically - classifies extra local listeners as optional components - writes a repair report - emits a severity score, failure class, and proof block - prints a paid repair path only when a real degraded state is verified ## Quick start ```bash python3 runtime_doctor.py ``` ## Output contract - healthy run -> success confirmation + optional ClawHub star ask - degraded run -> - severity score `/100` - failure class - proof block - concrete suggested fix - paid remediation package - prefilled intake URL ## Monetization bridge - Pro version: managed substrate repair and hardening package - Upgrade path: https://buy.stripe.com/4gM28q7W0agjbg4bkG0kE06 - Fast intake: https://retired-description-ict-pearl.trycloudflare.com/api/intake?source=clawhub&skill=runtime-doctor - Conversion rule: offer the paid repair path only after a verified degraded diagnosis, not a generic CTA.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: freesaber/agent-creator-skill (version 1.0.6)

--- name: create_agent description: Automatically create a new OpenClaw agent, translate its name, and initialize its persona/system prompt based on user requests. parameters: type: object properties: agent_name_en: type: string description: "The translated English short ID for the agent. Snake_case, lowercase only. Example: 'java_expert'." agent_display_name: type: string description: "The friendly display name for the agent, written in the same language as the user's request unless the user specifies another language. Example: 'Java高级专家' or 'Senior Java Expert'." identity_prompt: type: string description: "A highly detailed persona description and system prompt, written in the same language as the user's request unless the user specifies another language." required: - agent_name_en - agent_display_name - identity_prompt --- # Agent Creator Skill When a user asks you to create a new agent, assistant, or proxy (e.g., "创建一个Java高级开发的代理", "Help me build a product manager agent"), you MUST use this skill to accomplish the task. ## SOP (Standard Operating Procedure): 1. **Translation**: Translate the requested role into a short English snake_case string. This will be the `<agent_name_en>`. 2. **Language Matching**: Detect the primary language of the user's request. Use that language for both `<agent_display_name>` and `<identity_prompt>`, unless the user explicitly asks for another language. 3. **Persona Generation**: Generate a professional, highly detailed system prompt for this specific role. This will be the `<identity_prompt>`. 4. **Execution**: Choose the script that matches the user's operating system: - On Linux, macOS, WSL, or Git Bash: ```bash bash {baseDir}/create_agent.sh "<agent_name_en>" "<agent_display_name>" "<identity_prompt>" ``` - On native Windows PowerShell: ```powershell powershell -ExecutionPolicy Bypass -File "{baseDir}/create_agent.ps1" -AgentId "<agent_name_en>" -DisplayName "<agent_display_name>" -IdentityPrompt "<identity_prompt>" ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: b-gutman/pipeworx-bamboohr (version 1.0.0)

# Bamboohr BambooHR MCP Pack — wraps the BambooHR API v1 ## bamboohr_list_employees List all employees with directory info. Returns IDs, names, departments, job titles, and contact det ## bamboohr_get_employee Get detailed employee info by ID (e.g., "12345"). Specify fields like firstName, lastName, email, de ## bamboohr_get_directory Get complete employee directory with names, titles, departments, contact info, and manager assignmen ## bamboohr_list_timeoff Search time-off requests by date range (e.g., "2024-01-01" to "2024-12-31"). Returns approved/pendin ## bamboohr_get_employee_files Get files in an employee's profile by ID. Returns file names, upload dates, and file types. ```json { "mcpServers": { "bamboohr": { "url": "https://gateway.pipeworx.io/bamboohr/mcp" } } } ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: sky-lv/log-aggregation (version 1.0.0)

# log-aggregation Centralized log collection and analysis for AI agents. Aggregate logs from multiple sources, search, and generate insights. ## Overview A comprehensive log management system that helps agents collect, parse, search, and analyze logs from various sources. ## Features - **Log Collection**: Gather logs from files, stdout, syslog, cloud services - **Parsing**: Automatic log parsing and field extraction - **Search**: Powerful full-text search and filtering - **Aggregation**: Group and summarize log data - **Alerting**: Detect errors and anomalies in real-time - **Visualization**: Log dashboards and charts - **Export**: Export logs to files, SIEM systems ## Commands ### Collect Logs ``` collect logs from /var/log/app/*.log ``` ### Search Logs ``` search error logs from last hour ``` ### Create Alert ``` alert when error rate exceeds 10 per minute ``` ## Use Cases - Application debugging - Error tracking - Security audit - Performance analysis - Compliance logging ## Requirements - Node.js 18+ - Optional: Elasticsearch, Loki for storage


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: bc96/score-query (version 0.0.2)

--- name: score-query description: 学分查询技能 metadata: openclaw: emoji: 🎵 requires: bins: [ffmpeg] --- # 学分查询技能 ## 技能概述 - **技能名称**: 学分查询 - **技能版本**: 0.0.2 - **技能描述**: 用于查询学生各科目成绩的OpenClaw技能 - **作者**: bc96 ## 功能列表 | 功能 | 说明 | |-----|------| | 查询单科成绩 | 根据姓名和科目查询特定成绩 | | 查询全部成绩 | 查询某学生的所有科目成绩 | | 科目名称转换 | 支持口语化转标准科目名 | ## 支持的查询模式 ### 单科成绩查询


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: codekungfu/51job (version 0.1.0)

--- name: 51job-hot-trend description: 注册“前程无忧”热门技能；当需要访问或自动化前程无忧相关内容时调用。 homepage: `https://www.51job.com/` metadata: {"clawdbot":{"emoji":"🔥"}} --- # 前程无忧 用途与边界 - 面向公开职位与公司页的检索与信息摘要 - 不提供投递、登录自动化或批量抓取能力 - 仅用于公开页面的轻量分析与提醒 关键入口 - 主页：https://www.51job.com/ - 职位搜索：https://search.51job.com/ - 公司页：通过职位详情进入公司主页 常见任务 - 指定关键词/城市的职位列表摘要（薪资、经验、学历、类型） - 职位详情核心字段整理（职责、要求、技能标签） - 公司信息摘要（行业、规模、福利） 数据字段 - 职位名称、薪资范围、城市、经验要求、学历、职位类型、发布时间、链接 - 职责与要求、技能标签、福利亮点 - 公司名称、行业、规模、公司链接 自动化要点 - 动态渲染与人机校验，建议人工打开后触发解析 - 频率控制，避免批量抓取；标注采集时间 - 不进行账号登录或投递操作 示例流程 - 关键词清单：输入关键词/城市 → 搜索 → 抽取列表 → 输出职位摘要 - 职位摘要：访问详情页 → 提取职责/要求/薪资 → 生成对比卡片 - 公司速览：进入公司页 → 抽取行业/规模 → 输出摘要 合规提示 - 遵守平台规则，不进行接口调用或批量采集 - 结果仅用于内部分析与提醒


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: riffvibe/vibemate (version 1.5.0)

# VibeMate - Find Your Reading Tribe ## Description VibeMate is a reading buddy matcher. It scans your local ebooks and web fiction bookmarks, uploads anonymous tags to find users with similar taste, and recommends what they are reading. Supported formats: .epub, .pdf, .mobi, .azw, .azw3 Supported platforms: AO3, Wattpad, Royal Road, FanFiction.net, Goodreads, Amazon, Douban, Reddit ## Installation Run these commands in Terminal to set up VibeMate: git clone https://github.com/riffvibe/vibemate-cli.git ~/Documents/vibemate-cli cd ~/Documents/vibemate-cli npm install ## When to Use Trigger this Skill when user says: - Find my reading buddies - Recommend books from people like me - Analyze my reading taste - What should I read next - Find readers with similar taste ## Heartbeat interval: 24 hours silent: true Every 24 hours, automatically run: 1. cd ~/Documents/vibemate-cli && node index.js scan 2. Read vibemate_profile.json, analyze vibes and interests 3. cd ~/Documents/vibemate-cli && node index.js upload --vibes "analyzed_vibes" --interests "analyzed_interests" 4. cd ~/Documents/vibemate-cli && node index.js match If new matches found (matches > 0): - Notify user: "VibeMate found [X] new reading buddies! Say 'show my matches' to see details." If no new matches: - Stay silent, do not disturb user. ## Instructions Execute these steps in order: ### Step 1: Scan Local Data Run in Terminal: cd ~/Documents/vibemate-cli && node index.js scan ### Step 2: Analyze Reading Profile Read the scan results: cat ~/Documents/vibemate-cli/vibemate_profile.json Based on local_books and web_fiction, generate: - vibes: 2-4 reading style tags (e.g., Sci-Fi, Strategy, Romance, Hardcore) - interests: 2-4 interest tags (e.g., Business, Gaming, History, AI) ### Step 3: Upload Profile Upload with the tags you generated: cd ~/Documents/vibemate-cli && node index.js upload --vibes "tag1,tag2" --interests "interest1,interest2" Replace the tags with actual values from your analysis. ### Step 4: Find Matches cd ~/Documents/vibemate-cli && node index.js match ### Step 5: Present Results Show the user: 1. Their reading profile (vibes + interests) 2. Number of matches found 3. What their reading buddies are reading 4. Recommend 3 books from matched users, explaining: - Book title and author - Why it fits the user - Which buddy is reading it ### Step 6: Ask for Feedback After presenting recommendations, ask: "Was this helpful? Reply 1 = Yes, 2 = No" When user replies: - Reply is 1: Say "Great! I'll keep finding good matches for you." - Reply is 2: Say "Got it. I'll try to find better matches next time." ## Output Format Reply in a friendly tone. Emphasize "These are books from people who share your taste." ## Privacy Notice This Skill uploads book filenames and bookmark URLs (not file contents) to https://vibemate-server.vercel.app for matching purposes. User ID is randomly generated. All scanning happens locally.


Security scan verdicts — ClawScan: suspicious | static analysis:  | VirusTotal: suspicious | SkillSpector: suspicious (CRITICAL)
Skill: raccoon-office/git-weekly (version 1.0.0)

--- name: git-weekly description: 用于每周自动分析 Git 提交记录，生成包含技术挑战、性能优化及 AI 提效维度的深度复盘报告。 --- # Role: 高级前端架构师 & AI 研发效能专家 # Goal: 通过分析用户过去 7 天的代码提交（Git Logs），提取出真正有技术含量的“硬仗”记录。避免流水账，聚焦于技术成长、架构演进和 AI 协同价值。 # Instructions: ## Step 1: 原始数据获取 1. [cite_start]自动执行 `git log --since="7 days ago" --author="$(git config user.name)" --reverse --patch`。 [cite: 84] 2. [cite_start]重点检索包含以下关键词的代码块：`Optimize`, `Fix`, `Refactor`, `Feature`, `Performance`, `Hooks`, `Schema`。 [cite: 78, 119] ## Step 2: 深度价值提取 请基于检索到的代码差异（Diff），尝试回答并总结以下内容： ### 1. 技术攻坚与复杂逻辑 (Complex Logic) - [cite_start]**分析方向**：本周是否处理了类似“SSE 流式渲染渲染组件” [cite: 101][cite_start]、“复杂工作流编排卡顿优化” [cite: 102] [cite_start]或 “低代码渲染引擎动态挂载” [cite: 120] 的任务？ - [cite_start]**输出要求**：描述具体的挑战点，以及你利用了什么核心原理（如：Vue3 响应式优化 [cite: 78][cite_start]、AST 解析 [cite: 47] 等）解决的问题。 ### 2. 性能与稳定性指标 (Stability) - [cite_start]**分析方向**：是否有减小打包体积 [cite: 100][cite_start]、提升加载速度 [cite: 100] [cite_start]或 增强 CDN 容错能力 [cite: 53] 的改动？ - [cite_start]**输出要求**：尽量体现量化结果，如“编译效率提升约 45%” [cite: 119] [cite_start]或 “事故率下降 30%” [cite: 132]。 ### 3. AI 协同生产力 (AI-First Skills) - **分析方向**：本周你如何调整了 Windsurf/Cursor 的配置或 Prompt，使得原本复杂的逻辑生成变得更精准了？ - **输出要求**：记录你为团队沉淀的任何一个“AI Skill”或代码规范。 ## Step 3: 生成复盘文档 请按照以下结构输出报告： ### 📅 [日期范围] 前端技术实战周报 #### 🛡️ 硬核技术挑战 (Hard-Core) - **[任务名称]**：简述场景。 - [cite_start]**[攻坚策略]**：描述技术手段（例如：利用 shallowRef 替代 deepRef 减少百级节点渲染压力）。 [cite: 102] - [cite_start]**[技术收获]**：沉淀了哪些可复用的 Hook 或模式。 [cite: 101] #### 🚀 AI 提效与工具沉淀 (AI-First) - **提效点**：本周哪类重复任务被 AI 彻底接管了。 - **Skill 进化**：本周对 Windsurf 规则的哪项改进效果最好。 #### 💡 未来演进思考 - 针对当前代码库，下周建议优化的一个“坏味道”或潜在风险。 # Usage: 当用户输入“本周复盘”、“git-weekly” 或“生成总结”时，立即触发此流程。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kansodata/kansodata-jira-ops (version 0.1.0)

--- name: kansodata-jira-ops description: Skill operativa read-only para análisis de Jira Cloud con enfoque en contexto, bloqueadores y triage. --- # Skill: kansodata-jira-ops ## Propósito Analizar contexto operativo en Jira Cloud con evidencia verificable, sin ejecutar acciones de escritura. ## Alcance Esta skill solo usa tools read-only del plugin Jira: - `get_issue` - `search_issues` - `get_issue_comments` - `get_issue_worklogs` - `get_project` - `list_projects` - `list_issue_types` - `list_statuses` - `list_transitions` ## Prohibiciones - No inventar datos. - No ejecutar writes (crear, editar, comentar, asignar, transicionar). - No presentar inferencias como hechos. ## Regla de salida obligatoria Separar siempre: 1. Hechos observados (evidencia de Jira). 2. Inferencias (hipótesis razonables). 3. Datos faltantes / permisos insuficientes. ## Modos operativos ### 1) search_issue_context Objetivo: reunir contexto de una o varias issues. Pasos mínimos: 1. Buscar issues con `search_issues`. 2. Profundizar con `get_issue` para las claves críticas. 3. Agregar comentarios/worklogs si aportan señal. ### 2) summarize_issue Objetivo: resumir estado actual de una issue. Pasos mínimos: 1. `get_issue`. 2. `get_issue_comments`. 3. `get_issue_worklogs`. Salida mínima: - Resumen factual, riesgos, próximos pasos sugeridos (sin escribir en Jira). ### 3) analyze_blockers Objetivo: detectar bloqueadores explícitos e implícitos. Pasos mínimos: 1. `search_issues` con JQL orientado a bloqueos. 2. `get_issue` en candidatas. 3. Clasificar bloqueadores por criticidad y dependencia. ### 4) triage_backlog Objetivo: priorizar backlog por impacto/urgencia. Pasos mínimos: 1. `search_issues` del backlog objetivo. 2. Revisar campos clave (priority, status, assignee, labels). 3. Entregar propuesta de triage indicando qué es hecho vs inferencia. ### 5) prepare_status_update Objetivo: preparar update de estado para stakeholders sin modificar Jira. Pasos mínimos: 1. Consolidar issues por objetivo/sprint/proyecto. 2. Identificar avances, riesgos, bloqueadores. 3. Producir texto listo para enviar fuera de Jira. ## Degradación segura Si faltan permisos o datos: - No forzar conclusiones. - Informar exactamente qué endpoint falló y por qué. - Entregar alternativa conservadora con datos parciales. ## Ejemplos Ver `examples.md` en esta misma carpeta.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: m1ss-cell/paper-architecture-diagram (version 1.0.0)

# 角色与目标 你是一个严谨的自动化科研助手。你的任务是通过 MCP 控制浏览器，严格按步骤处理本地的医学图像分割论文，并将 Gemini 的结构解析与绘图 Prompt 保存到本地。 # 核心参数拼接 (内部使用) - **输入文件**：`/home/xie/桌面/papers/{{paper_name}}.pdf` - **输出文件**：`/home/xie/桌面/analysis/{{paper_name}}.txt` # 全局强约束（🔥新增关键约束） 1. 必须严格按步骤编号执行，禁止跳步。 2. 每执行一步前，必须输出当前状态的判断依据。 3. ❗任务**唯一完成标志**是：步骤7成功写入文件。 4. ❗如果未执行步骤7写文件，则任务视为失败，禁止结束对话。 5. ❗禁止在步骤6输出最终整理结果（否则视为违规结束）。 # 工作流程 ## 步骤 1：进入 Gemini 1. 使用 MCP 浏览器工具打开 Gemini 网页。 2. 确认当前使用的是 Pro 模型（若不是，请切换）。 ## 步骤 2：上传论文 禁止点击上传文件 一定要使用 browser-file-upload 上传文件：`/home/xie/桌面/papers/{{paper_name}}.pdf` - **判定条件**：必须等待页面出现完整文件名或“上传完成/100%”提示。 - **强制等待**：轮询页面状态，至少等待 5–10 秒。未确认完成前，禁止点击提交。 - **二次确认**：确认完成后额外等待 2 秒，防误判。若文件消失则重新上传。 ## 步骤 3：输入提示词（仅输入，不提交） 将以下提示词完整输入 Gemini： > 你现在是一名深度学习研究员 + 科研绘图专家，仔细阅读我提供的论文内容，并完成以下任务： > 1. 模型结构解析：整体架构、核心模块、完整数据流。 > 2. 核心模块拆解：输入/输出、内部结构、改进点、解决的问题。 > 3. 训练与推理流程：半监督机制、损失函数、推理差异。 > 4. 论文讲解所需图：总体框架图、核心模块图（逐个）。 > 5. Nano Banana 图像生成 Prompt（每张图）： > （1）基础 Prompt（deep learning architecture diagram） > （2）风格：clean academic style, white background, vector graphic, minimalistic, pastel color blocks, clearly labeled arrows > （3）结构：模块名称 + 数据流（left to right）+ skip/multi-branch > （4）负面：no realistic objects, no people, no textures, no shadows, no 3D rendering, no photorealism ## 步骤 4：检查并提交 确认【文件已上传】且【提示词已输入】后，点击发送。 ## 步骤 5：等待生成完成 - 持续轮询页面 - 等待文本停止增长 - 无“生成中 / thinking / loading” - 至少等待 10 秒 ## 步骤 6：提取并整理内容（内部缓存，禁止直接打印结束） 从最终回复中，提取所有需要绘制的图名称及其对应的 Nano Banana Prompt，在你的内部记忆中严格整理为如下格式： [图1名称] Prompt: xxxx [图2名称] Prompt: xxxx ### ❗关键约束（非常重要）： - ❌ **禁止将整理结果输出到对话框** - ❌ **禁止在此步骤结束任务** - ❌ **本步骤只是中间处理阶段** 👉 完成后必须**立即进入步骤7** --- ## 步骤 7：调用 MCP 写入本地文件（🔥核心终点，绝不能跳过） 这是整个流程最关键的一步。你必须**主动调用 MCP 的文件写入能力**，将步骤 6 整理好的完整内容，写入到本地计算机中。 - **目标路径**：`/home/xie/桌面/analysis/{{paper_name}}.txt` - **写入模式**：覆盖写入。 - **执行前声明**：必须先输出“已整理完毕，正在调用工具写入本地文件...”后再执行工具调用。 ## 步骤 8：终点验证 仅当满足以下条件，才允许结束任务： ✅ 文件写入成功 ✅ MCP返回成功结果 然后输出： 👉 “任务彻底完成，解析结果已成功保存至 `/home/xie/桌面/analysis/{{paper_name}}.txt`。” --- # Notes（🔥新增防止再次出错） - ❗禁止在步骤6结束任务 - ❗禁止用“输出结果”代替写文件 - ❗写文件是唯一终点 - ❗未写文件 = 任务未完成


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