Back to Details

skill-manager

Compare original and translation side by side

🇺🇸

Original

English
🇨🇳

Translation

Chinese

技能管理者

Skill Manager

任务目标

Task Objectives

  • 本 Skill 用于：为其他技能提供统一的配置和日志存储能力
  • 能力包含：
    1. 存储技能配置信息
    2. 存储技能运行日志
    3. 按技能名查询配置和日志
    4. 管理所有技能的存储数据
  • 触发条件：被其他技能主动引用，需要存储或读取数据时
  • This Skill is used to: Provide unified configuration and log storage capabilities for other skills
  • Capabilities include:
    1. Store skill configuration information
    2. Store skill operation logs
    3. Query configurations and logs by skill name
    4. Manage stored data of all skills
  • Trigger condition: When actively referenced by other skills for data storage or retrieval

使用场景

Usage Scenarios

场景1：技能存储自己的配置

Scenario 1: Skill stores its own configuration

当技能需要持久化配置信息时，调用技能管理者存储：
python
from skill_manager import SkillStorage
When a skill needs to persist configuration information, call the Skill Manager for storage:
python
from skill_manager import SkillStorage

创建存储实例

创建存储实例

storage = SkillStorage(data_path="/workspace/projects/skill-data.json")
storage = SkillStorage(data_path="/workspace/projects/skill-data.json")

存储配置

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)
undefined
config = { "deploy_mode": "simple", "output_path": "/path/to/output.log", "timestamp": "2024-01-22 12:00:00" } storage.save_config("my-skill", config)
undefined

场景2：技能存储运行日志

Scenario 2: Skill stores operation logs

当技能需要记录运行日志时，调用技能管理者存储：
python
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)
When a skill needs to record operation logs, call the Skill Manager for storage:
python
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)

场景3：技能读取其他技能的数据

Scenario 3: Skill reads data from other skills

当技能需要访问其他技能的配置或日志时：
python
from skill_manager import SkillStorage

storage = SkillStorage(data_path="/workspace/projects/skill-data.json")
When a skill needs to access configurations or logs of other skills:
python
from skill_manager import SkillStorage

storage = SkillStorage(data_path="/workspace/projects/skill-data.json")

读取其他技能的配置

读取其他技能的配置

other_config = storage.get_config("other-skill")
other_config = storage.get_config("other-skill")

读取其他技能的日志

读取其他技能的日志

other_logs = storage.get_logs("other-skill")
undefined
other_logs = storage.get_logs("other-skill")
undefined

核心功能说明

Core Function Description

智能体可处理的功能

Functions Processable by Agents

  • API使用咨询：解释如何使用技能管理者的各个功能
  • 数据格式建议：根据存储需求推荐合适的数据结构
  • 数据分析：分析存储的配置和日志，发现模式或问题
  • 存储管理：查看所有已存储的技能，清理过期数据
  • API usage consultation: Explain how to use each function of the Skill Manager
  • Data format suggestions: Recommend appropriate data structures based on storage requirements
  • Data analysis: Analyze stored configurations and logs to identify patterns or issues
  • Storage management: View all stored skills and clean up expired data

脚本实现的功能

Functions Implemented by Scripts

  • SkillStorage类：完整的存储和读取API，见 scripts/skill_manager.py 
    • 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
  • SkillStorage Class: Complete storage and retrieval API, see scripts/skill_manager.py 
    • save_config(skill_name, config)
      : Store skill configuration
    • save_logs(skill_name, logs)
      : Store skill logs
    • save(skill_name, config, logs)
      : Store configuration and logs simultaneously
    • get_config(skill_name)
      : Retrieve skill configuration
    • get_logs(skill_name)
      : Retrieve skill logs
    • get_all()
      : Retrieve all skill data
    • list_skills()
      : List all stored skills
    • delete(skill_name)
      : Delete skill data

数据存储格式

Data Storage Format

技能管理者使用统一的JSON格式存储数据：
json
{
  "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"
  }
}
The Skill Manager uses a unified JSON format for data storage:
json
{
  "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"
  }
}

资源索引

Resource Index

  • 核心模块：见 scripts/skill_manager.py（SkillStorage类完整实现）
  • API规范：见 references/api_spec.md（详细的API文档和使用示例）
  • Core Module: See scripts/skill_manager.py (Complete implementation of the SkillStorage class)
  • API Specification: See references/api_spec.md (Detailed API documentation and usage examples)

注意事项

Notes

  • 技能管理者是一个服务型Skill，不主动运行，被其他技能调用
  • 存储路径建议使用 
    /workspace/projects/skill-data.json
    实现统一管理
  • 配置和日志的数据格式由调用技能自行定义，技能管理者只负责存储
  • 每次存储会更新 
    last_updated
    时间戳
  • 支持增量更新，不会覆盖已有的配置或日志（除非显式指定覆盖）
  • The Skill Manager is a service-type Skill that does not run actively and is called by other skills
  • It is recommended to use 
    /workspace/projects/skill-data.json
    as the storage path for unified management
  • The data formats of configurations and logs are defined by the calling skills, and the Skill Manager only handles storage
  • Each storage operation updates the 
    last_updated
    timestamp
  • Supports incremental updates and will not overwrite existing configurations or logs (unless explicitly specified to overwrite)

最佳实践

Best Practices

  • 技能名使用一致的命名规范（如小写字母加连字符）
  • 配置数据建议包含必要元数据（如创建时间、版本号等）
  • 日志数据建议包含时间戳和日志级别
  • 定期清理不再使用的技能数据，避免存储文件过大
  • 在技能启动时读取配置，在执行过程中记录日志
  • 智能体可以协助查询和分析存储的数据，发现技能协作机会
  • Use consistent naming conventions for skill names (e.g., lowercase letters with hyphens)
  • It is recommended to include necessary metadata in configuration data (such as creation time, version number, etc.)
  • It is recommended to include timestamps and log levels in log data
  • Regularly clean up unused skill data to prevent excessive storage file size
  • Read configurations when the skill starts and record logs during execution
  • Agents can assist in querying and analyzing stored data to identify skill collaboration opportunities

使用示例

Usage Examples

示例1：技能集成技能管理者

Example 1: Skill integrates Skill Manager

python
import sys
sys.path.insert(0, '/workspace/projects/skill-manager/scripts')
from skill_manager import SkillStorage
python
import sys
sys.path.insert(0, '/workspace/projects/skill-manager/scripts')
from skill_manager import SkillStorage

初始化存储

初始化存储

storage = SkillStorage(data_path="/workspace/projects/skill-data.json")
storage = SkillStorage(data_path="/workspace/projects/skill-data.json")

存储技能配置

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)
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

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)
undefined
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)
undefined

示例2：查询技能协作数据

Example 2: Query skill collaboration data

python
from skill_manager import SkillStorage

storage = SkillStorage(data_path="/workspace/projects/skill-data.json")
python
from skill_manager import SkillStorage

storage = SkillStorage(data_path="/workspace/projects/skill-data.json")

查看所有已存储的技能

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}")
all_skills = storage.list_skills() print(f"已存储的技能: {all_skills}")

读取特定技能的配置

读取特定技能的配置

config = storage.get_config("dev-observability") print(f"配置: {config}")
config = storage.get_config("dev-observability") print(f"配置: {config}")

读取特定技能的日志

读取特定技能的日志

logs = storage.get_logs("dev-observability") print(f"最近日志: {logs[-5:]}")
undefined
logs = storage.get_logs("dev-observability") print(f"最近日志: {logs[-5:]}")
undefined

示例3：智能体分析存储数据

Example 3: Agent analyzes stored data

当智能体需要分析多个技能的协作情况时：
  1. 读取所有技能数据
  2. 分析技能间的配置关联
  3. 识别共享的依赖或数据
  4. 提供优化建议
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。
When an agent needs to analyze the collaboration of multiple skills:
  1. Read all skill data
  2. Analyze configuration associations between skills
  3. Identify shared dependencies or data
  4. Provide optimization suggestions
Agents can identify potential issues and optimization opportunities for skill combinations based on stored data.