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为其他技能提供统一的配置和日志存储服务，支持技能间数据共享和协作
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Basé sur la classification professionnelle SOC
Cox 编程 - 为您的 AI 编程体验保驾护航。帮助开发团队掌握项目进展、识别开发风险、了解系统健康状态。提供项目进度跟踪、迭代管理（MVP驱动）、任务状态管理、开发假设记录、应用模块监控、测试埋点和异常分析等功能。支持静态网页和交互网页两种方案，适合不同环境和团队规模。网页按迭代分组展示，清晰呈现每个迭代的进度和任务。
Cox Programming - Safeguarding your AI programming experience. Helps development teams grasp project progress, identify development risks, and understand system health status. Provides project progress tracking, iteration management (MVP-driven), task status management, development assumption recording, application module monitoring, test tracing points, and anomaly analysis functions. Supports both static web and interactive web solutions, suitable for different environments and team sizes. Web pages are grouped by iteration, clearly presenting the progress and tasks of each iteration.
Code debugging assistant, helps you see how code runs. When you say "I want to see why this function is so slow", "code errors during execution, don't know where the problem is", "business logic too complex, can't clarify execution order", I'll help trace code execution paths, find slow points, locate error causes. When discovering skill optimization points, will ask whether to invoke skill-evolution-driver for optimization
Helps teams continuously improve skills, automatically discovers skill optimization opportunities (such as missing necessary information, format issues, need version updates, etc.), executes safe update processes (backup, modify, test, restore), ensures skill quality continuously improves with project progress
Provides unified configuration and log storage service for other skills, supporting data sharing and collaboration between skills
代码调试智能助手，帮助你看到代码是怎么运行的。当你说"我想看看这个函数为什么会这么慢"、"代码运行到一半就报错了，不知道哪里出问题"、"这个业务逻辑太复杂了，我理不清楚执行顺序"时，我会帮你追踪代码执行路径，找出慢的地方，定位错误原因。在发现技能优化点时，会询问是否调用skill-evolution-driver进行优化
| name | skill-manager |
| description | 为其他技能提供统一的配置和日志存储服务，支持技能间数据共享和协作 |
| dependency | {} |
当技能需要持久化配置信息时，调用技能管理者存储：
from skill_manager import SkillStorage
# 创建存储实例
storage = SkillStorage(data_path="/workspace/projects/skill-data.json")
# 存储配置
config = {
"deploy_mode": "simple",
"output_path": "/path/to/output.log",
"timestamp": "2024-01-22 12:00:00"
}
storage.save_config("my-skill", config)
当技能需要记录运行日志时，调用技能管理者存储：
from skill_manager import SkillStorage
storage = SkillStorage(data_path="/workspace/projects/skill-data.json")
logs = [
{"time": "2024-01-22 12:00:00", "level": "INFO", "message": "开始执行"},
{"time": "2024-01-22 12:05:00", "level": "INFO", "message": "执行完成"}
]
storage.save_logs("my-skill", logs)
当技能需要访问其他技能的配置或日志时：
from skill_manager import SkillStorage
storage = SkillStorage(data_path="/workspace/projects/skill-data.json")
# 读取其他技能的配置
other_config = storage.get_config("other-skill")
# 读取其他技能的日志
other_logs = storage.get_logs("other-skill")
save_config(skill_name, config): 存储技能配置save_logs(skill_name, logs): 存储技能日志save(skill_name, config, logs): 同时存储配置和日志get_config(skill_name): 读取技能配置get_logs(skill_name): 读取技能日志get_all(): 读取所有技能数据list_skills(): 列出所有已存储的技能delete(skill_name): 删除技能数据技能管理者使用统一的JSON格式存储数据：
{
"skill-name-1": {
"config": {
"key1": "value1",
"key2": "value2"
},
"logs": [
{"time": "2024-01-22 12:00:00", "message": "日志1"},
{"time": "2024-01-22 12:05:00", "message": "日志2"}
],
"last_updated": "2024-01-22 12:05:00"
},
"skill-name-2": {
"config": {},
"logs": [],
"last_updated": "2024-01-22 12:10:00"
}
}
/workspace/projects/skill-data.json 实现统一管理last_updated 时间戳import sys
sys.path.insert(0, '/workspace/projects/skill-manager/scripts')
from skill_manager import SkillStorage
# 初始化存储
storage = SkillStorage(data_path="/workspace/projects/skill-data.json")
# 存储技能配置
skill_config = {
"mode": "production",
"output_dir": "/workspace/output",
"retry_count": 3
}
storage.save_config("my-awesome-skill", skill_config)
# 记录运行日志
execution_logs = [
{"time": "2024-01-22 10:00:00", "level": "INFO", "message": "开始执行"},
{"time": "2024-01-22 10:00:05", "level": "INFO", "message": "加载配置"},
{"time": "2024-01-22 10:00:10", "level": "INFO", "message": "执行完成"}
]
storage.save_logs("my-awesome-skill", execution_logs)
from skill_manager import SkillStorage
storage = SkillStorage(data_path="/workspace/projects/skill-data.json")
# 查看所有已存储的技能
all_skills = storage.list_skills()
print(f"已存储的技能: {all_skills}")
# 读取特定技能的配置
config = storage.get_config("dev-observability")
print(f"配置: {config}")
# 读取特定技能的日志
logs = storage.get_logs("dev-observability")
print(f"最近日志: {logs[-5:]}")
当智能体需要分析多个技能的协作情况时：
智能体可以基于存储的数据，发现技能组合的潜在问题和优化机会。