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Skill Seekers

Skill Seekers

Prerequisites

前置要求

bash
pip install skill-seekers
bash
pip install skill-seekers

Or: uv pip install skill-seekers

Or: uv pip install skill-seekers

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Commands

命令

SourceCommand
Local code
skill-seekers-codebase --directory ./path
Docs URL
skill-seekers scrape --url https://...
GitHub
skill-seekers github --repo owner/repo
PDF
skill-seekers pdf --file doc.pdf
来源命令
本地代码
skill-seekers-codebase --directory ./path
文档URL
skill-seekers scrape --url https://...
GitHub
skill-seekers github --repo owner/repo
PDF
skill-seekers pdf --file doc.pdf

Quick Start

快速开始

bash
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bash
undefined

Analyze local codebase

分析本地代码库

skill-seekers-codebase --directory /path/to/project --output output/my-skill/
skill-seekers-codebase --directory /path/to/project --output output/my-skill/

Package for Claude

为Claude打包

yes | skill-seekers package output/my-skill/ --no-open
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yes | skill-seekers package output/my-skill/ --no-open
undefined

Options

选项

FlagDescription
--depth surface/deep/full
Analysis depth
--skip-patterns
Skip pattern detection
--skip-test-examples
Skip test extraction
--ai-mode none/api/local
AI enhancement
标志描述
--depth surface/deep/full
分析深度
--skip-patterns
跳过模式检测
--skip-test-examples
跳过测试用例提取
--ai-mode none/api/local
AI增强模式

Skill_Seekers Codebase

Skill_Seekers 代码库分析结果

Description

描述

Local codebase analysis and documentation generated from code analysis.
Path: 
/home/lyh/Documents/Skill_Seekers
Files Analyzed: 140 Languages: Python Analysis Depth: deep
基于代码分析生成的本地代码库分析与文档。
路径: 
/home/lyh/Documents/Skill_Seekers
已分析文件数: 140 涉及语言: Python 分析深度: deep

When to Use This Skill

适用场景

Use this skill when you need to:
  • Understand the codebase architecture and design patterns
  • Find implementation examples and usage patterns
  • Review API documentation extracted from code
  • Check configuration patterns and best practices
  • Explore test examples and real-world usage
  • Navigate the codebase structure efficiently
当你需要以下操作时，可使用本Skill：
  • 理解代码库架构与设计模式
  • 查找实现示例与使用模式
  • 查看从代码中提取的API文档
  • 检查配置模式与最佳实践
  • 探索测试示例与实际使用场景
  • 高效浏览代码库结构

⚡ Quick Reference

⚡ 快速参考

Codebase Statistics

代码库统计

Languages:
  • Python: 140 files (100.0%)
Analysis Performed:
  • ✅ API Reference (C2.5)
  • ✅ Dependency Graph (C2.6)
  • ✅ Design Patterns (C3.1)
  • ✅ Test Examples (C3.2)
  • ✅ Configuration Patterns (C3.4)
  • ✅ Architectural Analysis (C3.7)
涉及语言:
  • Python: 140个文件 (100.0%)
已执行的分析:
  • ✅ API参考文档 (C2.5)
  • ✅ 依赖关系图 (C2.6)
  • ✅ 设计模式 (C3.1)
  • ✅ 测试示例 (C3.2)
  • ✅ 配置模式 (C3.4)
  • ✅ 架构分析 (C3.7)

🎨 Design Patterns Detected

🎨 检测到的设计模式

From C3.1 codebase analysis (confidence > 0.7)
  • Factory: 44 instances
  • Strategy: 28 instances
  • Observer: 8 instances
  • Builder: 6 instances
  • Command: 3 instances
Total: 90 high-confidence patterns
See 
references/patterns/
for complete pattern analysis
来自C3.1代码库分析（置信度>0.7）
  • Factory（工厂模式）: 44个实例
  • Strategy（策略模式）: 28个实例
  • Observer（观察者模式）: 8个实例
  • Builder（建造者模式）: 6个实例
  • Command（命令模式）: 3个实例
总计: 90个高置信度模式
完整模式分析请查看 
references/patterns/

📝 Code Examples

📝 代码示例

High-quality examples extracted from test files (C3.2)
Workflow: test full join multigraph (complexity: 1.00)
python
G = nx.MultiGraph()
G.add_node(0)
G.add_edge(1, 2)
H = nx.MultiGraph()
H.add_edge(3, 4)
U = nx.full_join(G, H)
assert set(U) == set(G) | set(H)
assert len(U) == len(G) + len(H)
assert len(U.edges()) == len(G.edges()) + len(H.edges()) + len(G) * len(H)
U = nx.full_join(G, H, rename=('g', 'h'))
assert set(U) == {'g0', 'g1', 'g2', 'h3', 'h4'}
assert len(U) == len(G) + len(H)
assert len(U.edges()) == len(G.edges()) + len(H.edges()) + len(G) * len(H)
G = nx.MultiDiGraph()
G.add_node(0)
G.add_edge(1, 2)
H = nx.MultiDiGraph()
H.add_edge(3, 4)
U = nx.full_join(G, H)
assert set(U) == set(G) | set(H)
assert len(U) == len(G) + len(H)
assert len(U.edges()) == len(G.edges()) + len(H.edges()) + len(G) * len(H) * 2
U = nx.full_join(G, H, rename=('g', 'h'))
assert set(U) == {'g0', 'g1', 'g2', 'h3', 'h4'}
assert len(U) == len(G) + len(H)
assert len(U.edges()) == len(G.edges()) + len(H.edges()) + len(G) * len(H) * 2
Instantiate DataFrame: See gh-7407 (complexity: 1.00)
python
df = pd.DataFrame([[0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1], [0, 0, 0, 0]], index=[1010001, 2, 1, 1010002], columns=[1010001, 2, 1, 1010002])
test edge removal (complexity: 1.00)
python
embedding_expected.set_data({1: [2, 7], 2: [1, 3, 4, 5], 3: [2, 4], 4: [3, 6, 2], 5: [7, 2], 6: [4, 7], 7: [6, 1, 5]})
assert nx.utils.graphs_equal(embedding, embedding_expected)
Instantiate Graph: test graph1 (complexity: 1.00)
python
G = nx.Graph([(3, 10), (2, 13), (1, 13), (7, 11), (0, 8), (8, 13), (0, 2), (0, 7), (0, 10), (1, 7)])
Instantiate Graph: test graph2 (complexity: 1.00)
python
G = nx.Graph([(1, 2), (4, 13), (0, 13), (4, 5), (7, 10), (1, 7), (0, 3), (2, 6), (5, 6), (7, 13), (4, 8), (0, 8), (0, 9), (2, 13), (6, 7), (3, 6), (2, 8)])
Configuration example: test davis birank (complexity: 1.00)
python
answer = {'Laura Mandeville': 0.07, 'Olivia Carleton': 0.04, 'Frances Anderson': 0.05, 'Pearl Oglethorpe': 0.04, 'Katherina Rogers': 0.06, 'Flora Price': 0.04, 'Dorothy Murchison': 0.04, 'Helen Lloyd': 0.06, 'Theresa Anderson': 0.07, 'Eleanor Nye': 0.05, 'Evelyn Jefferson': 0.07, 'Sylvia Avondale': 0.07, 'Charlotte McDowd': 0.05, 'Verne Sanderson': 0.05, 'Myra Liddel': 0.05, 'Brenda Rogers': 0.07, 'Ruth DeSand': 0.05, 'Nora Fayette': 0.07, 'E8': 0.11, 'E7': 0.09, 'E10': 0.07, 'E9': 0.1, 'E13': 0.05, 'E3': 0.07, 'E12': 0.07, 'E11': 0.06, 'E2': 0.05, 'E5': 0.08, 'E6': 0.08, 'E14': 0.05, 'E4': 0.06, 'E1': 0.05}
Configuration example: test davis birank with personalization (complexity: 1.00)
python
answer = {'Laura Mandeville': 0.29, 'Olivia Carleton': 0.02, 'Frances Anderson': 0.06, 'Pearl Oglethorpe': 0.04, 'Katherina Rogers': 0.04, 'Flora Price': 0.02, 'Dorothy Murchison': 0.03, 'Helen Lloyd': 0.04, 'Theresa Anderson': 0.08, 'Eleanor Nye': 0.05, 'Evelyn Jefferson': 0.09, 'Sylvia Avondale': 0.05, 'Charlotte McDowd': 0.06, 'Verne Sanderson': 0.04, 'Myra Liddel': 0.03, 'Brenda Rogers': 0.08, 'Ruth DeSand': 0.05, 'Nora Fayette': 0.05, 'E8': 0.11, 'E7': 0.1, 'E10': 0.04, 'E9': 0.07, 'E13': 0.03, 'E3': 0.11, 'E12': 0.04, 'E11': 0.03, 'E2': 0.1, 'E5': 0.11, 'E6': 0.1, 'E14': 0.03, 'E4': 0.06, 'E1': 0.1}
test junction tree directed confounders (complexity: 1.00)
python
J.add_edges_from([(('C', 'E'), ('C',)), (('C',), ('A', 'B', 'C')), (('A', 'B', 'C'), ('C',)), (('C',), ('C', 'D'))])
assert nx.is_isomorphic(G, J)
test junction tree directed cascade (complexity: 1.00)
python
J.add_edges_from([(('A', 'B'), ('B',)), (('B',), ('B', 'C')), (('B', 'C'), ('C',)), (('C',), ('C', 'D'))])
assert nx.is_isomorphic(G, J)
test junction tree undirected (complexity: 1.00)
python
J.add_edges_from([(('A', 'D'), ('A',)), (('A',), ('A', 'C')), (('A', 'C'), ('C',)), (('C',), ('B', 'C')), (('B', 'C'), ('C',)), (('C',), ('C', 'E'))])
assert nx.is_isomorphic(G, J)
See 
references/test_examples/
for all extracted examples
从测试文件中提取的高质量示例（C3.2）
工作流: 测试全连接多重图 (复杂度: 1.00)
python
G = nx.MultiGraph()
G.add_node(0)
G.add_edge(1, 2)
H = nx.MultiGraph()
H.add_edge(3, 4)
U = nx.full_join(G, H)
assert set(U) == set(G) | set(H)
assert len(U) == len(G) + len(H)
assert len(U.edges()) == len(G.edges()) + len(H.edges()) + len(G) * len(H)
U = nx.full_join(G, H, rename=('g', 'h'))
assert set(U) == {'g0', 'g1', 'g2', 'h3', 'h4'}
assert len(U) == len(G) + len(H)
assert len(U.edges()) == len(G.edges()) + len(H.edges()) + len(G) * len(H)
G = nx.MultiDiGraph()
G.add_node(0)
G.add_edge(1, 2)
H = nx.MultiDiGraph()
H.add_edge(3, 4)
U = nx.full_join(G, H)
assert set(U) == set(G) | set(H)
assert len(U) == len(G) + len(H)
assert len(U.edges()) == len(G.edges()) + len(H.edges()) + len(G) * len(H) * 2
U = nx.full_join(G, H, rename=('g', 'h'))
assert set(U) == {'g0', 'g1', 'g2', 'h3', 'h4'}
assert len(U) == len(G) + len(H)
assert len(U.edges()) == len(G.edges()) + len(H.edges()) + len(G) * len(H) * 2
实例化DataFrame: 参考gh-7407 (复杂度: 1.00)
python
df = pd.DataFrame([[0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1], [0, 0, 0, 0]], index=[1010001, 2, 1, 1010002], columns=[1010001, 2, 1, 1010002])
测试边移除 (复杂度: 1.00)
python
embedding_expected.set_data({1: [2, 7], 2: [1, 3, 4, 5], 3: [2, 4], 4: [3, 6, 2], 5: [7, 2], 6: [4, 7], 7: [6, 1, 5]})
assert nx.utils.graphs_equal(embedding, embedding_expected)
实例化图: test graph1 (复杂度: 1.00)
python
G = nx.Graph([(3, 10), (2, 13), (1, 13), (7, 11), (0, 8), (8, 13), (0, 2), (0, 7), (0, 10), (1, 7)])
实例化图: test graph2 (复杂度: 1.00)
python
G = nx.Graph([(1, 2), (4, 13), (0, 13), (4, 5), (7, 10), (1, 7), (0, 3), (2, 6), (5, 6), (7, 13), (4, 8), (0, 8), (0, 9), (2, 13), (6, 7), (3, 6), (2, 8)])
配置示例: test davis birank (复杂度: 1.00)
python
answer = {'Laura Mandeville': 0.07, 'Olivia Carleton': 0.04, 'Frances Anderson': 0.05, 'Pearl Oglethorpe': 0.04, 'Katherina Rogers': 0.06, 'Flora Price': 0.04, 'Dorothy Murchison': 0.04, 'Helen Lloyd': 0.06, 'Theresa Anderson': 0.07, 'Eleanor Nye': 0.05, 'Evelyn Jefferson': 0.07, 'Sylvia Avondale': 0.07, 'Charlotte McDowd': 0.05, 'Verne Sanderson': 0.05, 'Myra Liddel': 0.05, 'Brenda Rogers': 0.07, 'Ruth DeSand': 0.05, 'Nora Fayette': 0.07, 'E8': 0.11, 'E7': 0.09, 'E10': 0.07, 'E9': 0.1, 'E13': 0.05, 'E3': 0.07, 'E12': 0.07, 'E11': 0.06, 'E2': 0.05, 'E5': 0.08, 'E6': 0.08, 'E14': 0.05, 'E4': 0.06, 'E1': 0.05}
配置示例: test davis birank with personalization (复杂度: 1.00)
python
answer = {'Laura Mandeville': 0.29, 'Olivia Carleton': 0.02, 'Frances Anderson': 0.06, 'Pearl Oglethorpe': 0.04, 'Katherina Rogers': 0.04, 'Flora Price': 0.02, 'Dorothy Murchison': 0.03, 'Helen Lloyd': 0.04, 'Theresa Anderson': 0.08, 'Eleanor Nye': 0.05, 'Evelyn Jefferson': 0.09, 'Sylvia Avondale': 0.05, 'Charlotte McDowd': 0.06, 'Verne Sanderson': 0.04, 'Myra Liddel': 0.03, 'Brenda Rogers': 0.08, 'Ruth DeSand': 0.05, 'Nora Fayette': 0.05, 'E8': 0.11, 'E7': 0.1, 'E10': 0.04, 'E9': 0.07, 'E13': 0.03, 'E3': 0.11, 'E12': 0.04, 'E11': 0.03, 'E2': 0.1, 'E5': 0.11, 'E6': 0.1, 'E14': 0.03, 'E4': 0.06, 'E1': 0.1}
测试联合树有向混杂因素 (复杂度: 1.00)
python
J.add_edges_from([(('C', 'E'), ('C',)), (('C',), ('A', 'B', 'C')), (('A', 'B', 'C'), ('C',)), (('C',), ('C', 'D'))])
assert nx.is_isomorphic(G, J)
测试联合树有向级联 (复杂度: 1.00)
python
J.add_edges_from([(('A', 'B'), ('B',)), (('B',), ('B', 'C')), (('B', 'C'), ('C',)), (('C',), ('C', 'D'))])
assert nx.is_isomorphic(G, J)
测试联合树无向 (复杂度: 1.00)
python
J.add_edges_from([(('A', 'D'), ('A',)), (('A',), ('A', 'C')), (('A', 'C'), ('C',)), (('C',), ('B', 'C')), (('B', 'C'), ('C',)), (('C',), ('C', 'E'))])
assert nx.is_isomorphic(G, J)
所有提取的示例请查看 
references/test_examples/

⚙️ Configuration Patterns

⚙️ 配置模式

From C3.4 configuration analysis
Configuration Files Analyzed: 23 Total Settings: 165 Patterns Detected: 0
Configuration Types:
  • unknown: 23 files
See 
references/config_patterns/
for detailed configuration analysis
来自C3.4配置分析
已分析配置文件数: 23 总设置数: 165 检测到的模式数: 0
配置类型:
  • unknown: 23个文件
详细配置分析请查看 
references/config_patterns/

📚 Available References

📚 可用参考文档

This skill includes detailed reference documentation:
  • API Reference: 
    references/api_reference/
    - Complete API documentation
  • Dependencies: 
    references/dependencies/
    - Dependency graph and analysis
  • Patterns: 
    references/patterns/
    - Detected design patterns
  • Examples: 
    references/test_examples/
    - Usage examples from tests
  • Configuration: 
    references/config_patterns/
    - Configuration patterns
Generated by Skill Seeker | Codebase Analyzer with C3.x Analysis
本Skill包含详细的参考文档：
  • API参考文档: 
    references/api_reference/
    - 完整的API文档
  • 依赖关系: 
    references/dependencies/
    - 依赖关系图与分析
  • 设计模式: 
    references/patterns/
    - 检测到的设计模式
  • 示例: 
    references/test_examples/
    - 来自测试的使用示例
  • 配置: 
    references/config_patterns/
    - 配置模式
由Skill Seeker生成 | 支持C3.x分析的代码库分析工具