skill-seekers（技能寻求者）

Skill Seekers

Prerequisites

pip install skill-seekers
# Or: uv pip install skill-seekers

Commands

Quick Start

# Analyze local codebase
skill-seekers-codebase --directory /path/to/project --output output/my-skill/

# Package for Claude
yes | skill-seekers package output/my-skill/ --no-open

Options

Skill_Seekers Codebase

Description

Local codebase analysis and documentation generated from code analysis.

Path: /home/lyh/Documents/Skill_Seekers Files Analyzed: 140 Languages: Python Analysis Depth: deep

When to Use This Skill

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

⚡ Quick Reference

Codebase Statistics

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)

🎨 Design Patterns Detected

*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*

📝 Code Examples

*High-quality examples extracted from test files (C3.2)*

Workflow: test full join multigraph (complexity: 1.00)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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*

⚙️ Configuration Patterns

*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*

📚 Available References

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