OpenClaw/clawhub-security-signals huggingface dataset:
Skill: zhoujj8009/auto-summarization-loop (version 1.0.0)

--- name: auto-summarization-loop description: | 自动摘要循环：为长对话 AI 角色实现自动上下文管理。用于： (1) 建立多级记忆架构（核心记忆/工作记忆/长期记忆） (2) 实现滑动窗口与双水位线触发策略 (3) 异步后台压缩流程设计 (4) Persona 机器人的结构化摘要输出 适用场景：需要处理长对话、降低 API 成本、避免上下文溢出的 AI 应用 --- # Auto-Summarization Loop - 自动摘要循环 为高拟真度 AI 角色实现自动上下文管理的完整方案。 ## 核心架构 ``` ┌─────────────────────────────────────────────────────────────┐ │ Prompt 构建结构 │ ├─────────────────────────────────────────────────────────────┤ │ [System Prompt] ← 核心记忆 (永不压缩) │ │ ───────────────────────────────────────────────────────── │ │ 【历史摘要】 ← 长期记忆 (压缩后的摘要) │ │ 【用户档案】 ← 结构化用户信息 │ │ ───────────────────────────────────────────────────────── │ │ [近期消息] ← 工作记忆 (保留最近 N 条) │ │ ───────────────────────────────────────────────────────── │ │ [用户新消息] ← 当前输入 │ └─────────────────────────────────────────────────────────────┘ ``` ## 快速使用 ### 1. 引入核心模块 ```python from scripts.memory_manager import ( AutoSummarizationLoop, MemoryConfig, MemoryBlock, TriggerType ) ``` ### 2. 初始化配置 ```python config = MemoryConfig( max_tokens=200000, # 最大上下文限制 soft_limit_ratio=0.7, # 70% 时异步压缩 hard_limit_ratio=0.9, # 90% 时同步拦截 working_messages=20, # 保留最近 20 条 summary_max_tokens=2000, # 摘要最大长度 summarize_fn=your_summarize_func # 摘要生成函数 ) ``` ### 3. 处理对话 ```python manager = AutoSummarizationLoop(config) memory = MemoryBlock( core_memory="你是埃隆·马斯克...", working_memory=[], long_term_summary="" ) # 检查触发 trigger = manager.check_watermark(memory.working_memory) # 触发压缩 if trigger != TriggerType.NORMAL: await manager.handle_trigger(trigger, memory) # 构建 Prompt prompt = manager.build_prompt(user_message, memory) ``` ## 触发策略 | 阈值 | 状态 | 动作 | |------|------|------| | < 70% | 正常 | 继续 | | 70-90% | 软限制 | 后台异步压缩 | | > 90% | 硬限制 | 同步拦截压缩 | ## 摘要生成函数示例 ```python async def summarize_fn(old_text: str, old_summary: str, max_tokens: int) -> dict: prompt = f"""请总结以下对话... 旧摘要：{old_summary} {old_text} 输出 JSON：{{"summary": "...", "user_facts": {{}}}}""" result = await call_model(prompt, model="mini") return json.loads(result) ``` 提示词模板详见 [references/summary_prompts.md](references/summary_prompts.md) ## 存储结构 ``` session/ ├── core_memory # System Prompt ├── working_memory # 最近消息 (JSON 数组) ├── long_term_summary # 压缩后的摘要 ├── user_facts # 用户档案 └── last_update # 最后更新时间 ``` ## 与大模型集成 ```python async def chat(session_id: str, user_message: str): session = load_session(session_id) # 检查是否需要压缩 trigger = manager.check_watermark(session.working_memory) if trigger != TriggerType.NORMAL: await manager.handle_trigger(trigger, session) # 构建消息 messages = manager.build_prompt(user_message, session) # 调用大模型 response = await call_api(messages) # 保存到工作记忆 session.working_memory.append({"role": "user", "content": user_message}) session.working_memory.append({"role": "assistant", "content": response}) save_session(session_id, session) return response ``` ## Persona 机器人特别提示 生成摘要时确保包含： 1. **用户事实档案** - 姓名、地点、职业、偏好 2. **对话事件线** - 按时间顺序的关键事件 3. **已达成的共识** - 用户确认过的结论 4. **待跟进事项** - 未完成的任务 这样大模型在读取摘要时能更精准地把握用户特征。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: jpengcheng523-netizen/jpeng-workspace-health-dashboard (version 1.0.0)

--- name: workspace-health-dashboard description: Unified health monitoring dashboard that consolidates skill quality, dependency security, cleanup needs, and protected skills status into a single health check report. Use when checking overall workspace health or running system diagnostics. --- # Workspace Health Dashboard Unified health monitoring for the entire workspace. ## Usage ```javascript const { generateDashboard, formatDashboard, isHealthy } = require('./skills/workspace-health-dashboard'); // Generate full dashboard const dashboard = generateDashboard(); console.log(formatDashboard(dashboard)); // Quick check if (!isHealthy()) { console.log('Workspace needs attention!'); } ``` ## Functions ### generateDashboard(options?) Runs all health checks and returns: - Overall health score (0-100) - Skill quality metrics - Dependency status - Cleanup recommendations - Protected skills status ### formatDashboard(dashboard) Generates human-readable dashboard with icons. ### isHealthy(options?) Quick boolean check - returns true if all checks pass. ## Health Checks 1. **Skill Quality** - % of skills with complete structure 2. **Dependencies** - Scan for known vulnerabilities 3. **Cleanup** - Junk folders and incomplete skills 4. **Protected Skills** - Critical skills present ## Health Status - ✅ **Healthy** - All checks pass - ⚠️ **Warning** - Some issues found - 🔴 **Critical** - Immediate attention needed ## Integration Works with: - `skill-quality-auditor` - Detailed skill analysis - `skill-cleanup-executor` - Execute cleanup - `dependency-vulnerability-scanner` - Security audit


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