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帮助团队持续改进技能，自动发现技能优化机会（如缺少必要信息、格式问题、需要更新版本等），执行安全更新流程（备份、修改、测试、还原），确保技能质量随项目推进不断提升
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Based on SOC occupation classification
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-evolution-driver |
| description | 帮助团队持续改进技能，自动发现技能优化机会（如缺少必要信息、格式问题、需要更新版本等），执行安全更新流程（备份、修改、测试、还原），确保技能质量随项目推进不断提升 |
| dependency | {"python":[],"system":[]} |
从skill-manager存储的技能数据中分析优化机会：
智能体定期检查 skill-manager 存储的数据，识别优化机会：
检查工具：调用 scripts/check_optimization_opportunities.py
# 检查所有技能
python scripts/check_optimization_opportunities.py
# 检查特定技能
python scripts/check_optimization_opportunities.py --skill-name dev-observability
检查内容：
提醒格式：
检测到潜在优化机会：
- 技能：skill-name
- 优化类型：format_improvement
- 描述：SKILL.md缺少version字段
- 是否开始优化？（y/n）
当用户表达以下需求时，直接提醒用户：
若用户同意，调用skill-manager存储优化任务：
{
"task_id": "OPT-001",
"skill_name": "skill-name",
"optimization_type": "format_improvement",
"description": "SKILL.md缺少version字段",
"status": "pending",
"feasibility": "pending",
"backup_path": "",
"old_version": "",
"new_version": "",
"test_result": "",
"notes": "",
"created_at": "2024-01-22 12:00:00",
"updated_at": "2024-01-22 12:00:00"
}
存储方式：更新 skill-evolution-driver 在 skill-manager 中的配置
import sys
sys.path.insert(0, '/workspace/projects/skill-manager/scripts')
from skill_manager import SkillStorage
storage = SkillStorage(data_path="/workspace/projects/skill-data.json")
existing_config = storage.get_config("skill-evolution-driver") or {}
existing_config["optimization_tasks"] = [task_dict] # 任务列表
storage.save_config("skill-evolution-driver", existing_config)
分析该技能的优化可行性：
可行性评估标准：
不可行情况：
更新任务状态：
feasibility: "feasible"feasibility: "not_feasible"，并在notes中说明原因调用 scripts/backup_skill.py 备份技能：
python scripts/backup_skill.py --skill-dir /workspace/projects/skill-name
备份文件格式：skill-name.backup.<timestamp>.skill
更新任务：
backup_path: "/workspace/projects/skill-name.backup.<timestamp>.skill"执行具体的优化操作，并更新版本号：
scripts/update_version.pypython scripts/update_version.py --skill-dir /workspace/projects/skill-name --type patch
版本号类型：
patch：修订版本（v1.0.0 -> v1.0.1）minor：次版本（v1.0.0 -> v1.1.0）major：主版本（v1.0.0 -> v2.0.0）更新任务：
old_version: "v1.0.0"new_version: "v1.0.1"对更新后的技能进行测试验证：
测试内容：
测试脚本：
python scripts/backup_skill.py --validate-only --skill-dir /workspace/projects/skill-name
测试不通过：
scripts/restore_skill.py 还原技能manage_optimization_tasks.py 更新任务状态# 还原技能
python scripts/restore_skill.py --backup-file <备份路径> --skill-dir /workspace/projects/skill-name
# 更新任务状态
python scripts/manage_optimization_tasks.py update \
--task-id OPT-001 \
--status failed \
--test-result failed \
--notes "测试失败：SKILL.md格式错误"
测试通过：
manage_optimization_tasks.py 更新任务状态# 更新任务状态
python scripts/manage_optimization_tasks.py update \
--task-id OPT-001 \
--status completed \
--test-result passed \
--notes "优化成功：已添加version字段" \
--new-version v1.0.1 \
--backup-path /workspace/projects/skill-name.backup.20260122120000.skill
重要：必须更新任务状态为 completed，避免下次检查时重复提醒相同的优化机会。
根据测试结果通知用户：
测试通过：
✓ 技能优化成功
- 技能：skill-name
- 版本：v1.0.0 -> v1.0.1
- 更新内容：添加version字段
- 备份：skill-name.backup.<timestamp>.skill
请重新加载AI交互界面以使用更新后的技能。
测试不通过：
✗ 技能优化失败
- 技能：skill-name
- 原因：测试失败
- 详情：SKILL.md格式错误
- 技能已还原
scripts/check_optimization_opportunities.py 扫描skill-manager数据，自动识别优化机会（过滤已完成的任务）scripts/manage_optimization_tasks.py 提供优化任务的增删改查功能（add/update/list）scripts/backup_skill.py 备份整个skill目录scripts/restore_skill.py 从备份还原技能scripts/update_version.py 增加SKILL.md中的version字段scripts/backup_skill.py --validate-only 验证技能格式completed，避免重复提醒检测到skill-name的SKILL.md缺少version字段。
检测到skill-name的SKILL.md格式不符合规范。
检测到skill-name的优化内容过于复杂。