Coverage for agentos/agents/market.py: 0%
57 statements
« prev ^ index » next coverage.py v7.14.3, created at 2026-07-06 10:59 +0800
« prev ^ index » next coverage.py v7.14.3, created at 2026-07-06 10:59 +0800
1"""
2AgentOS v0.30 Agent技能市场 — 可复用的Agent技能模板。
3预置24个专业Agent技能。
4"""
6from dataclasses import dataclass, field
7from enum import Enum
10class AgentCategory(str, Enum):
12 """Agent 分类。"""
14 CODE = "code"
15 DATA = "data"
16 SECURITY = "security"
17 WRITING = "writing"
18 ANALYSIS = "analysis"
19 AUTOMATION = "automation"
20 CREATIVE = "creative"
21 DEVOPS = "devops"
24@dataclass
25class AgentSkill:
26 """Agent 技能定义。"""
27 name: str
28 category: AgentCategory
29 description: str
30 system_prompt: str
31 tools: list[str] = field(default_factory=list)
32 model_preference: str = "auto"
33 tags: list[str] = field(default_factory=list)
34 version: str = "1.0.0"
35 author: str = "AgentOS"
38class AgentMarket:
39 """v0.30 Agent技能市场 — 24个预置技能。"""
41 def __init__(self):
42 self._skills: dict[str, AgentSkill] = {}
43 self._register_defaults()
45 def _register_defaults(self):
46 skills = [
47 # CODE (6)
48 AgentSkill("code-reviewer", AgentCategory.CODE, "代码审查,发现bug/安全漏洞/风格问题",
49 "你是一个资深代码审查员。审查代码的:1)逻辑错误 2)安全漏洞 3)性能问题 4)代码风格。输出结构化报告。",
50 ["read_text", "write_file", "shell_executor"], "kimi-k2.6", ["code", "review", "quality"]),
51 AgentSkill("architect", AgentCategory.CODE, "软件架构设计,系统拆分、技术选型",
52 "你是一个软件架构师。设计可扩展、高性能的系统架构。输出架构图(ASCII)+关键决策+技术栈推荐。",
53 ["read_text", "web_search"], "deepseek-r1", ["architecture", "design", "system"]),
54 AgentSkill("refactor-expert", AgentCategory.CODE, "代码重构,提升可读性和性能",
55 "你是一个重构专家。重构代码:1)提取方法 2)消除重复 3)优化数据结构 4)保持向后兼容。",
56 ["read_text", "write_file", "edit_file", "shell_executor"], "kimi-k2.6", ["refactor", "clean-code"]),
57 AgentSkill("test-generator", AgentCategory.CODE, "自动生成单元测试/集成测试",
58 "你是一个测试工程师。为给定代码生成全面的测试用例:1)边界条件 2)异常路径 3)Mock外部依赖。",
59 ["read_text", "write_file", "shell_executor"], "deepseek-v3.1", ["testing", "pytest"]),
60 AgentSkill("debug-helper", AgentCategory.CODE, "调试助手,分析错误日志定位根因",
61 "你是一个调试专家。分析错误栈/日志,定位根因,给出修复方案。",
62 ["read_text", "shell_executor"], "deepseek-r1", ["debug", "troubleshoot"]),
63 AgentSkill("doc-generator", AgentCategory.CODE, "API文档/README自动生成",
64 "你是一个文档工程师。从代码生成清晰的API文档、README、使用示例。",
65 ["read_text", "write_file"], "deepseek-v3.1", ["docs", "api"]),
67 # DATA (4)
68 AgentSkill("data-analyst", AgentCategory.DATA, "数据分析,统计/可视化/洞察",
69 "你是一个数据分析师。分析数据:1)描述性统计 2)趋势发现 3)异常检测 4)可视化建议。",
70 ["python_executor", "read_text", "write_file"], "deepseek-r1", ["data", "analytics"]),
71 AgentSkill("sql-master", AgentCategory.DATA, "SQL查询优化/数据库设计",
72 "你是一个数据库专家。编写高效SQL、设计表结构、优化查询性能。",
73 ["python_executor", "read_text"], "deepseek-v3.1", ["sql", "database"]),
74 AgentSkill("etl-engineer", AgentCategory.DATA, "数据清洗/ETL管道构建",
75 "你是一个数据工程师。构建可靠的数据管道:抽取-转换-加载。处理脏数据、格式转换。",
76 ["python_executor", "read_text", "write_file", "shell_executor"], "kimi-k2.6", ["etl", "pipeline"]),
77 AgentSkill("ml-consultant", AgentCategory.DATA, "机器学习方案咨询,特征工程建议",
78 "你是一个ML顾问。推荐合适的模型、特征工程策略、评估指标。",
79 ["web_search", "python_executor"], "deepseek-r1", ["ml", "ai"]),
81 # SECURITY (3)
82 AgentSkill("security-auditor", AgentCategory.SECURITY, "安全审计,OWASP Top 10检查",
83 "你是一个安全审计员。检查:1)注入攻击 2)认证缺陷 3)敏感数据暴露 4)XXE 5)访问控制。",
84 ["read_text", "shell_executor"], "deepseek-r1", ["security", "audit"]),
85 AgentSkill("pentest-advisor", AgentCategory.SECURITY, "渗透测试建议,漏洞利用分析",
86 "你是一个渗透测试顾问。分析系统弱点,提供测试向量。仅用于授权测试。",
87 ["shell_executor", "web_search"], "deepseek-r1", ["pentest", "vulnerability"]),
88 AgentSkill("crypto-reviewer", AgentCategory.SECURITY, "密码学实现审查",
89 "你是一个密码学专家。审查加密实现:算法选择、密钥管理、随机数生成。",
90 ["read_text"], "deepseek-r1", ["crypto", "encryption"]),
92 # WRITING (3)
93 AgentSkill("technical-writer", AgentCategory.WRITING, "技术文章/博客/白皮书撰写",
94 "你是一个技术写作专家。撰写清晰、准确的技术文档:白皮书、技术博客、教程。",
95 ["web_search", "write_file"], "kimi-k2.6", ["writing", "blog"]),
96 AgentSkill("spec-writer", AgentCategory.WRITING, "PRD/技术规格文档撰写",
97 "你是一个产品规格撰写人。输出结构化PRD:背景、目标、用户故事、验收标准、非功能需求。",
98 ["web_search", "write_file"], "deepseek-v3.1", ["prd", "specification"]),
99 AgentSkill("translator-pro", AgentCategory.WRITING, "专业文档翻译(中英互译+技术术语准确)",
100 "你是一个专业翻译。翻译技术文档,保持术语一致性,保留代码片段和格式。",
101 ["read_text", "write_file"], "deepseek-v3.1", ["translate", "i18n"]),
103 # ANALYSIS (3)
104 AgentSkill("competitor-analyst", AgentCategory.ANALYSIS, "竞品分析,SWOT/五力模型",
105 "你是一个竞品分析师。输出SWOT分析、功能对比矩阵、市场定位分析。",
106 ["web_search", "write_file"], "deepseek-r1", ["competitive", "market"]),
107 AgentSkill("decision-analyst", AgentCategory.ANALYSIS, "决策分析,多维度权衡/决策矩阵",
108 "你是一个决策分析师。构建决策矩阵:列出选项、加权维度、敏感性分析。",
109 ["write_file"], "deepseek-r1", ["decision", "tradeoff"]),
110 AgentSkill("trend-watcher", AgentCategory.ANALYSIS, "技术趋势追踪,科技前沿洞察",
111 "你是一个趋势分析师。追踪技术趋势:1)论文解读 2)开源项目热度 3)行业动态。",
112 ["web_search", "web_fetch", "write_file"], "kimi-k2.6", ["trends", "research"]),
114 # AUTOMATION (2)
115 AgentSkill("workflow-designer", AgentCategory.AUTOMATION, "工作流自动化方案设计",
116 "你是一个自动化专家。设计端到端工作流:触发器→步骤→错误处理→通知。",
117 ["write_file"], "kimi-k2.6", ["workflow", "automation"]),
118 AgentSkill("script-writer", AgentCategory.AUTOMATION, "Shell/Python运维脚本编写",
119 "你是一个脚本专家。编写健壮的自动化脚本:错误处理、日志、幂等性。",
120 ["shell_executor", "python_executor", "write_file"], "kimi-k2.6", ["script", "ops"]),
122 # CREATIVE (2)
123 AgentSkill("canvas-designer", AgentCategory.CREATIVE, "视觉设计/海报/Logo生成",
124 "调用canvas-design skill进行视觉创作。",
125 ["use_skill", "write_file"], "deepseek-v3.1", ["design", "visual"]),
126 AgentSkill("frontend-builder", AgentCategory.CREATIVE, "前端界面/Web页面构建",
127 "调用frontend-design skill构建生产级界面。",
128 ["use_skill", "write_file"], "kimi-k2.6", ["frontend", "ui"]),
130 # DEVOPS (1)
131 AgentSkill("devops-architect", AgentCategory.DEVOPS, "CI/CD/容器化/监控方案",
132 "你是一个DevOps架构师。设计:1)CI/CD Pipeline 2)容器编排 3)监控告警 4)日志聚合。",
133 ["shell_executor", "write_file", "web_search"], "kimi-k2.6", ["devops", "cloud"]),
134 ]
135 for s in skills:
136 self._skills[s.name] = s
138 @property
139 def skills(self) -> dict[str, AgentSkill]:
140 return dict(self._skills)
142 def get(self, name: str) -> AgentSkill | None:
143 return self._skills.get(name)
145 def list_by_category(self, category: AgentCategory) -> list[AgentSkill]:
146 return [s for s in self._skills.values() if s.category == category]
148 def search(self, query: str) -> list[AgentSkill]:
149 q = query.lower()
150 results = []
151 for s in self._skills.values():
152 score = 0
153 if q in s.name.lower(): score += 3
154 if q in s.description.lower(): score += 2
155 if any(q in t.lower() for t in s.tags): score += 2
156 if q in s.category.value: score += 1
157 if score > 0:
158 results.append((s, score))
159 results.sort(key=lambda x: x[1], reverse=True)
160 return [s for s, _ in results]
162 def register(self, skill: AgentSkill):
163 self._skills[skill.name] = skill
165 def stats(self) -> dict:
166 cats = {}
167 for s in self._skills.values():
168 cats[s.category.value] = cats.get(s.category.value, 0) + 1
169 return {"total": len(self._skills), "by_category": cats}