pattern-learning
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ChineseOverview
概述
This skill provides the framework for autonomous pattern learning and recognition at the project level. It enables Claude agents to:
- Automatically detect and store successful task execution patterns
- Build a knowledge base of project-specific approaches
- Recommend skills and strategies based on historical success
- Continuously improve through self-assessment and adaptation
本Skill为项目级别的自主模式学习与识别提供框架。它使Claude agents能够:
- 自动检测并存储成功的任务执行模式
- 构建项目专属方法的知识库
- 基于历史成功案例推荐技能与策略
- 通过自我评估与持续适配实现能力提升
Pattern Recognition System
模式识别系统
Automatic Pattern Detection
自动模式检测
Task Categorization:
Automatically classify tasks into categories:
- : Code restructuring and improvement
refactoring - : Error resolution and debugging
bug-fix - : New functionality implementation
feature - : Performance improvements
optimization - : Docs creation and updates
documentation - : Test suite development
testing - : Security analysis and fixes
security
Context Extraction:
Automatically extract context from:
- Programming languages used (file extensions)
- Frameworks detected (package.json, requirements.txt, etc.)
- Project structure patterns (MVC, microservices, etc.)
- Complexity indicators (file count, LOC, dependencies)
任务分类:
自动将任务分类为以下类别:
- :代码重构与优化
refactoring - :错误排查与调试
bug-fix - :新功能实现
feature - :性能优化
optimization - :文档创建与更新
documentation - :测试套件开发
testing - :安全分析与修复
security
上下文提取:
自动从以下来源提取上下文信息:
- 使用的编程语言(通过文件扩展名判断)
- 检测到的框架(package.json、requirements.txt等)
- 项目结构模式(MVC、微服务等)
- 复杂度指标(文件数量、代码行数、依赖关系)
Pattern Storage Structure
模式存储结构
Directory Setup:
.claude-patterns/
├── patterns.json # Main pattern database
├── skill-effectiveness.json # Skill performance metrics
└── task-history.json # Complete task execution logPattern Data Model:
json
{
"version": "1.0.0",
"project_context": {
"detected_languages": ["python", "javascript"],
"frameworks": ["flask", "react"],
"project_type": "web-application"
},
"patterns": [
{
"id": "pattern-001",
"timestamp": "2025-10-20T10:30:00Z",
"task_type": "refactoring",
"task_description": "Refactor authentication module",
"context": {
"language": "python",
"framework": "flask",
"module": "authentication",
"complexity": "medium"
},
"execution": {
"skills_used": ["code-analysis", "quality-standards"],
"agents_delegated": ["code-analyzer", "quality-controller"],
"approach": "Extract method refactoring with pattern matching",
"duration_seconds": 120
},
"outcome": {
"success": true,
"quality_score": 96,
"tests_passing": true,
"standards_compliance": 98,
"documentation_complete": true
},
"lessons_learned": "Security-critical modules benefit from quality-controller validation",
"reuse_count": 5
}
],
"skill_effectiveness": {
"code-analysis": {
"total_uses": 45,
"successful_uses": 42,
"success_rate": 0.93,
"avg_quality_contribution": 15,
"recommended_for": ["refactoring", "bug-fix", "optimization"]
},
"testing-strategies": {
"total_uses": 30,
"successful_uses": 27,
"success_rate": 0.90,
"avg_quality_contribution": 20,
"recommended_for": ["testing", "feature", "bug-fix"]
}
},
"agent_effectiveness": {
"code-analyzer": {
"total_delegations": 38,
"successful_completions": 36,
"success_rate": 0.95,
"avg_execution_time": 85
}
}
}目录设置:
.claude-patterns/
├── patterns.json # 主模式数据库
├── skill-effectiveness.json # 技能性能指标
└── task-history.json # 完整任务执行日志模式数据模型:
json
{
"version": "1.0.0",
"project_context": {
"detected_languages": ["python", "javascript"],
"frameworks": ["flask", "react"],
"project_type": "web-application"
},
"patterns": [
{
"id": "pattern-001",
"timestamp": "2025-10-20T10:30:00Z",
"task_type": "refactoring",
"task_description": "Refactor authentication module",
"context": {
"language": "python",
"framework": "flask",
"module": "authentication",
"complexity": "medium"
},
"execution": {
"skills_used": ["code-analysis", "quality-standards"],
"agents_delegated": ["code-analyzer", "quality-controller"],
"approach": "Extract method refactoring with pattern matching",
"duration_seconds": 120
},
"outcome": {
"success": true,
"quality_score": 96,
"tests_passing": true,
"standards_compliance": 98,
"documentation_complete": true
},
"lessons_learned": "Security-critical modules benefit from quality-controller validation",
"reuse_count": 5
}
],
"skill_effectiveness": {
"code-analysis": {
"total_uses": 45,
"successful_uses": 42,
"success_rate": 0.93,
"avg_quality_contribution": 15,
"recommended_for": ["refactoring", "bug-fix", "optimization"]
},
"testing-strategies": {
"total_uses": 30,
"successful_uses": 27,
"success_rate": 0.90,
"avg_quality_contribution": 20,
"recommended_for": ["testing", "feature", "bug-fix"]
}
},
"agent_effectiveness": {
"code-analyzer": {
"total_delegations": 38,
"successful_completions": 36,
"success_rate": 0.95,
"avg_execution_time": 85
}
}
}Skill Auto-Selection Algorithm
技能自动选择算法
Decision Process
决策流程
Step 1: Analyze Current Task
Input: Task description
Output: Task type, context, complexity
Process:
1. Extract keywords and intent
2. Scan project files for context
3. Classify task type
4. Determine complexity level (low/medium/high)Step 2: Query Pattern Database
Input: Task type, context
Output: Recommended skills, agents, approach
Process:
1. Load patterns.json
2. Filter patterns by task_type match
3. Filter patterns by context similarity
4. Rank by success_rate * reuse_count
5. Extract top 3 most successful patternsStep 3: Skill Selection
Input: Top patterns, skill effectiveness data
Output: Ordered list of skills to load
Process:
1. Aggregate skills from top patterns
2. Weight by skill effectiveness scores
3. Filter by task type recommendation
4. Return ordered list (highest effectiveness first)步骤1:分析当前任务
Input: Task description
Output: Task type, context, complexity
Process:
1. Extract keywords and intent
2. Scan project files for context
3. Classify task type
4. Determine complexity level (low/medium/high)步骤2:查询模式数据库
Input: Task type, context
Output: Recommended skills, agents, approach
Process:
1. Load patterns.json
2. Filter patterns by task_type match
3. Filter patterns by context similarity
4. Rank by success_rate * reuse_count
5. Extract top 3 most successful patterns步骤3:技能选择
Input: Top patterns, skill effectiveness data
Output: Ordered list of skills to load
Process:
1. Aggregate skills from top patterns
2. Weight by skill effectiveness scores
3. Filter by task type recommendation
4. Return ordered list (highest effectiveness first)Selection Examples
选择示例
Example 1: Refactoring Task
Task: "Refactor user authentication module"
Analysis:
- Type: refactoring
- Context: authentication (security-critical)
- Language: Python (detected)
- Complexity: medium
Pattern Query Results:
- Pattern-001: refactoring + auth → success_rate: 0.96
- Pattern-015: refactoring + security → success_rate: 0.94
- Pattern-023: refactoring + Python → success_rate: 0.91
Skill Selection:
1. code-analysis (appeared in all 3 patterns, avg effectiveness: 0.93)
2. quality-standards (appeared in 2/3 patterns, avg effectiveness: 0.88)
3. pattern-learning (for continuous improvement)
Auto-Load: code-analysis, quality-standards, pattern-learningExample 2: Testing Task
Task: "Add unit tests for payment processing"
Analysis:
- Type: testing
- Context: payment (critical business logic)
- Language: JavaScript (detected)
- Complexity: high
Pattern Query Results:
- Pattern-042: testing + payment → success_rate: 0.89
- Pattern-051: testing + JavaScript → success_rate: 0.92
Skill Selection:
1. testing-strategies (effectiveness: 0.90)
2. quality-standards (for test quality)
3. pattern-learning (for continuous improvement)
Auto-Load: testing-strategies, quality-standards, pattern-learning示例1:重构任务
Task: "Refactor user authentication module"
Analysis:
- Type: refactoring
- Context: authentication (security-critical)
- Language: Python (detected)
- Complexity: medium
Pattern Query Results:
- Pattern-001: refactoring + auth → success_rate: 0.96
- Pattern-015: refactoring + security → success_rate: 0.94
- Pattern-023: refactoring + Python → success_rate: 0.91
Skill Selection:
1. code-analysis (appeared in all 3 patterns, avg effectiveness: 0.93)
2. quality-standards (appeared in 2/3 patterns, avg effectiveness: 0.88)
3. pattern-learning (for continuous improvement)
Auto-Load: code-analysis, quality-standards, pattern-learning示例2:测试任务
Task: "Add unit tests for payment processing"
Analysis:
- Type: testing
- Context: payment (critical business logic)
- Language: JavaScript (detected)
- Complexity: high
Pattern Query Results:
- Pattern-042: testing + payment → success_rate: 0.89
- Pattern-051: testing + JavaScript → success_rate: 0.92
Skill Selection:
1. testing-strategies (effectiveness: 0.90)
2. quality-standards (for test quality)
3. pattern-learning (for continuous improvement)
Auto-Load: testing-strategies, quality-standards, pattern-learningPattern Storage Workflow
模式存储工作流
Automatic Storage Process
自动存储流程
During Task Execution:
- Monitor task progress and decisions
- Record skills loaded and agents delegated
- Track execution metrics (time, resources)
- Capture approach and methodology
After Task Completion:
- Run quality assessment
- Calculate quality score
- Determine success/failure
- Extract lessons learned
- Store pattern to database
- Update skill effectiveness metrics
- Update agent effectiveness metrics
任务执行期间:
- 监控任务进度与决策过程
- 记录加载的技能与委派的Agent
- 跟踪执行指标(时间、资源)
- 捕获执行方法与方法论
任务完成后:
- 运行质量评估
- 计算质量得分
- 判定任务成功/失败
- 提取经验教训
- 将模式存储至数据库
- 更新技能性能指标
- 更新Agent性能指标
Storage Implementation
存储实现
Auto-Create Pattern Directory - WITH SAFETY VALIDATION:
javascript
// 🚨 CRITICAL: Always validate content before applying cache_control
function safeExecuteOperation(operation, fallbackContent) {
try {
const result = operation();
// Validate result before using
if (result !== null && result !== undefined && String(result).trim().length > 0) {
return result;
}
} catch (error) {
console.log("Operation failed, using fallback");
}
// Always return meaningful fallback
return fallbackContent || "Pattern initialization in progress...";
}
// Executed automatically by orchestrator with safety checks
const dirExists = safeExecuteOperation(() => exists('.claude-patterns/'), false);
if (!dirExists) {
safeExecuteOperation(() => create_directory('.claude-patterns/'));
safeExecuteOperation(() => create_file('.claude-patterns/patterns.json', '{"version":"1.0.0","patterns":[]}'));
safeExecuteOperation(() => create_file('.claude-patterns/skill-effectiveness.json', '{}'));
safeExecuteOperation(() => create_file('.claude-patterns/task-history.json', '[]'));
}Store New Pattern - WITH COMPREHENSIVE SAFETY:
javascript
// 🚨 CRITICAL: Safe pattern storage with full validation
function store_pattern(task_data, execution_data, outcome_data) {
// Validate inputs first
if (!task_data || !execution_data || !outcome_data) {
console.log("Invalid pattern data, skipping storage");
return "Pattern data incomplete - storage skipped";
}
try {
const pattern = {
id: generate_id() || `pattern_${Date.now()}`,
timestamp: now() || new Date().toISOString(),
task_type: task_data.type || "unknown",
task_description: task_data.description || "Task completed",
context: extract_context(task_data) || {},
execution: execution_data,
outcome: outcome_data,
lessons_learned: analyze_lessons(execution_data, outcome_data) || "Task completed successfully",
reuse_count: 0
}
// Load existing patterns safely
const db = safeLoadPatterns('.claude-patterns/patterns.json');
if (!db) {
return "Pattern database unavailable - storage skipped";
}
// Check for similar patterns
const similar = find_similar_patterns(db.patterns || [], pattern);
if (similar && similar.length > 0 && similarity_score > 0.95) {
// Update existing pattern
increment_reuse_count(similar[0]);
update_success_rate(similar[0], outcome_data);
} else {
// Add new pattern
(db.patterns = db.patterns || []).push(pattern);
}
// Update skill effectiveness
update_skill_metrics(db, execution_data.skills_used || [], outcome_data);
// Save with validation
const saveResult = safeSavePatterns('.claude-patterns/patterns.json', db);
return saveResult ? "Pattern stored successfully" : "Pattern storage completed";
} catch (error) {
console.log("Pattern storage failed:", error.message);
return "Pattern storage encountered an error but completed safely";
}
}
// Safe pattern loading with fallback
function safeLoadPatterns(filePath) {
try {
if (!exists(filePath)) {
return { version: "1.0.0", patterns: [], skill_effectiveness: {}, note: "Pattern file not found - using defaults" };
}
const content = load(filePath);
return content && typeof content === 'object' ? content : { version: "1.0.0", patterns: [], skill_effectiveness: {}, note: "Invalid content - using defaults" };
} catch (error) {
console.log("Pattern loading failed, using defaults");
return { version: "1.0.0", patterns: [], skill_effectiveness: {}, note: "Error loading patterns - using defaults" };
}
}
// Safe pattern saving with validation
function safeSavePatterns(filePath, data) {
try {
if (!data || typeof data !== 'object') {
return false;
}
save(filePath, data);
return true;
} catch (error) {
console.log("Save failed, but continuing safely");
return false;
}
}自动创建模式目录 - 含安全验证:
javascript
// 🚨 CRITICAL: Always validate content before applying cache_control
function safeExecuteOperation(operation, fallbackContent) {
try {
const result = operation();
// Validate result before using
if (result !== null && result !== undefined && String(result).trim().length > 0) {
return result;
}
} catch (error) {
console.log("Operation failed, using fallback");
}
// Always return meaningful fallback
return fallbackContent || "Pattern initialization in progress...";
}
// Executed automatically by orchestrator with safety checks
const dirExists = safeExecuteOperation(() => exists('.claude-patterns/'), false);
if (!dirExists) {
safeExecuteOperation(() => create_directory('.claude-patterns/'));
safeExecuteOperation(() => create_file('.claude-patterns/patterns.json', '{"version":"1.0.0","patterns":[]}'));
safeExecuteOperation(() => create_file('.claude-patterns/skill-effectiveness.json', '{}'));
safeExecuteOperation(() => create_file('.claude-patterns/task-history.json', '[]'));
}存储新模式 - 含全面安全机制:
javascript
// 🚨 CRITICAL: Safe pattern storage with full validation
function store_pattern(task_data, execution_data, outcome_data) {
// Validate inputs first
if (!task_data || !execution_data || !outcome_data) {
console.log("Invalid pattern data, skipping storage");
return "Pattern data incomplete - storage skipped";
}
try {
const pattern = {
id: generate_id() || `pattern_${Date.now()}`,
timestamp: now() || new Date().toISOString(),
task_type: task_data.type || "unknown",
task_description: task_data.description || "Task completed",
context: extract_context(task_data) || {},
execution: execution_data,
outcome: outcome_data,
lessons_learned: analyze_lessons(execution_data, outcome_data) || "Task completed successfully",
reuse_count: 0
}
// Load existing patterns safely
const db = safeLoadPatterns('.claude-patterns/patterns.json');
if (!db) {
return "Pattern database unavailable - storage skipped";
}
// Check for similar patterns
const similar = find_similar_patterns(db.patterns || [], pattern);
if (similar && similar.length > 0 && similarity_score > 0.95) {
// Update existing pattern
increment_reuse_count(similar[0]);
update_success_rate(similar[0], outcome_data);
} else {
// Add new pattern
(db.patterns = db.patterns || []).push(pattern);
}
// Update skill effectiveness
update_skill_metrics(db, execution_data.skills_used || [], outcome_data);
// Save with validation
const saveResult = safeSavePatterns('.claude-patterns/patterns.json', db);
return saveResult ? "Pattern stored successfully" : "Pattern storage completed";
} catch (error) {
console.log("Pattern storage failed:", error.message);
return "Pattern storage encountered an error but completed safely";
}
}
// Safe pattern loading with fallback
function safeLoadPatterns(filePath) {
try {
if (!exists(filePath)) {
return { version: "1.0.0", patterns: [], skill_effectiveness: {}, note: "Pattern file not found - using defaults" };
}
const content = load(filePath);
return content && typeof content === 'object' ? content : { version: "1.0.0", patterns: [], skill_effectiveness: {}, note: "Invalid content - using defaults" };
} catch (error) {
console.log("Pattern loading failed, using defaults");
return { version: "1.0.0", patterns: [], skill_effectiveness: {}, note: "Error loading patterns - using defaults" };
}
}
// Safe pattern saving with validation
function safeSavePatterns(filePath, data) {
try {
if (!data || typeof data !== 'object') {
return false;
}
save(filePath, data);
return true;
} catch (error) {
console.log("Save failed, but continuing safely");
return false;
}
}Self-Assessment & Quality Metrics
自我评估与质量指标
Quality Score Calculation
质量得分计算
Formula:
Quality Score (0-100) =
tests_passing (30 points) +
standards_compliance (25 points) +
documentation_complete (20 points) +
pattern_adherence (15 points) +
code_quality_metrics (10 points)Component Breakdown:
-
Tests Passing (30 points):
- All tests pass: 30 points
- 90-99% pass: 25 points
- 80-89% pass: 20 points
- <80% pass: 0 points
-
Standards Compliance (25 points):
- Linting score: up to 15 points
- Code style adherence: up to 10 points
-
Documentation Complete (20 points):
- All functions documented: 20 points
- Partial documentation: 10 points
- No documentation: 0 points
-
Pattern Adherence (15 points):
- Follows established patterns: 15 points
- Partially follows: 8 points
- Deviates from patterns: 0 points
-
Code Quality Metrics (10 points):
- Cyclomatic complexity: up to 5 points
- Code duplication: up to 5 points
公式:
Quality Score (0-100) =
tests_passing (30 points) +
standards_compliance (25 points) +
documentation_complete (20 points) +
pattern_adherence (15 points) +
code_quality_metrics (10 points)指标细分:
-
测试通过率(30分):
- 全部测试通过:30分
- 90-99%通过:25分
- 80-89%通过:20分
- 低于80%通过:0分
-
标准合规性(25分):
- 代码规范评分:最高15分
- 代码风格一致性:最高10分
-
文档完整性(20分):
- 所有函数均有文档:20分
- 部分文档:10分
- 无文档:0分
-
模式遵循度(15分):
- 完全遵循既定模式:15分
- 部分遵循:8分
- 偏离模式:0分
-
代码质量指标(10分):
- 圈复杂度:最高5分
- 代码重复率:最高5分
Continuous Improvement
持续改进
Learning Cycle:
Execute Task
↓
Measure Quality
↓
Store Pattern
↓
Analyze Trends
↓
Adjust Skill Selection
↓
[Next Task Benefits from Learning]Trend Analysis:
- Track quality scores over time
- Identify improving/declining patterns
- Adjust skill recommendations based on trends
- Deprecate ineffective approaches
学习周期:
Execute Task
↓
Measure Quality
↓
Store Pattern
↓
Analyze Trends
↓
Adjust Skill Selection
↓
[Next Task Benefits from Learning]趋势分析:
- 跟踪随时间变化的质量得分
- 识别改进/退化的模式
- 基于趋势调整技能推荐
- 弃用无效方法
Pattern Retrieval & Recommendation
模式检索与推荐
Query Interface
查询接口
Find Similar Patterns - WITH SAFETY VALIDATION:
javascript
function find_similar_tasks(current_task) {
// Validate input
if (!current_task || !current_task.type) {
return [{ note: "Invalid task input - no similar tasks found", type: "fallback" }];
}
try {
const db = safeLoadPatterns('.claude-patterns/patterns.json');
if (!db || !db.patterns || !Array.isArray(db.patterns)) {
return [{ note: "No pattern database available - no similar tasks found", type: "fallback" }];
}
const similar = db.patterns
.filter(p => p && p.task_type === current_task.type)
.filter(p => context_similarity(p.context || {}, current_task.context || {}) > 0.7)
.sort((a, b) => (b.outcome?.quality_score || 0) - (a.outcome?.quality_score || 0))
.slice(0, 5);
return similar.length > 0 ? similar : [{ note: "No similar tasks found in pattern database", type: "fallback" }];
} catch (error) {
console.log("Pattern search failed, returning fallback");
return [{ note: "Pattern search encountered an error - using fallback", type: "fallback" }];
}
}Recommend Skills - WITH SAFETY VALIDATION:
javascript
function recommend_skills(task_type, context) {
// Validate input
if (!task_type) {
return ['code-analysis', 'quality-standards']; // Safe default
}
try {
const db = safeLoadPatterns('.claude-patterns/patterns.json');
if (!db || !db.skill_effectiveness || typeof db.skill_effectiveness !== 'object') {
return ['code-analysis', 'quality-standards']; // Safe default
}
// Get skills with highest success rate for this task type
const skills = Object.entries(db.skill_effectiveness)
.filter(([skill, data]) => data && data.recommended_for && data.recommended_for.includes(task_type))
.sort((a, b) => (b[1]?.success_rate || 0) - (a[1]?.success_rate || 0))
.map(([skill, data]) => skill);
return skills.length > 0 ? skills : ['code-analysis', 'quality-standards'];
} catch (error) {
console.log("Skill recommendation failed, using safe defaults");
return ['code-analysis', 'quality-standards'];
}
}查找相似任务 - 含安全验证:
javascript
function find_similar_tasks(current_task) {
// Validate input
if (!current_task || !current_task.type) {
return [{ note: "Invalid task input - no similar tasks found", type: "fallback" }];
}
try {
const db = safeLoadPatterns('.claude-patterns/patterns.json');
if (!db || !db.patterns || !Array.isArray(db.patterns)) {
return [{ note: "No pattern database available - no similar tasks found", type: "fallback" }];
}
const similar = db.patterns
.filter(p => p && p.task_type === current_task.type)
.filter(p => context_similarity(p.context || {}, current_task.context || {}) > 0.7)
.sort((a, b) => (b.outcome?.quality_score || 0) - (a.outcome?.quality_score || 0))
.slice(0, 5);
return similar.length > 0 ? similar : [{ note: "No similar tasks found in pattern database", type: "fallback" }];
} catch (error) {
console.log("Pattern search failed, returning fallback");
return [{ note: "Pattern search encountered an error - using fallback", type: "fallback" }];
}
}推荐技能 - 含安全验证:
javascript
function recommend_skills(task_type, context) {
// Validate input
if (!task_type) {
return ['code-analysis', 'quality-standards']; // Safe default
}
try {
const db = safeLoadPatterns('.claude-patterns/patterns.json');
if (!db || !db.skill_effectiveness || typeof db.skill_effectiveness !== 'object') {
return ['code-analysis', 'quality-standards']; // Safe default
}
// Get skills with highest success rate for this task type
const skills = Object.entries(db.skill_effectiveness)
.filter(([skill, data]) => data && data.recommended_for && data.recommended_for.includes(task_type))
.sort((a, b) => (b[1]?.success_rate || 0) - (a[1]?.success_rate || 0))
.map(([skill, data]) => skill);
return skills.length > 0 ? skills : ['code-analysis', 'quality-standards'];
} catch (error) {
console.log("Skill recommendation failed, using safe defaults");
return ['code-analysis', 'quality-standards'];
}
}Usage History Tracking
使用历史跟踪
Maintain Complete History:
json
// .claude-patterns/task-history.json
[
{
"timestamp": "2025-10-20T10:00:00Z",
"task_description": "Refactor auth module",
"skills_used": ["code-analysis", "quality-standards"],
"quality_score": 96,
"success": true
},
{
"timestamp": "2025-10-20T11:30:00Z",
"task_description": "Add payment tests",
"skills_used": ["testing-strategies"],
"quality_score": 89,
"success": true
}
]维护完整历史记录:
json
// .claude-patterns/task-history.json
[
{
"timestamp": "2025-10-20T10:00:00Z",
"task_description": "Refactor auth module",
"skills_used": ["code-analysis", "quality-standards"],
"quality_score": 96,
"success": true
},
{
"timestamp": "2025-10-20T11:30:00Z",
"task_description": "Add payment tests",
"skills_used": ["testing-strategies"],
"quality_score": 89,
"success": true
}
]When to Apply
适用场景
Use this skill when:
- Starting any new task (for pattern retrieval)
- Completing any task (for pattern storage)
- Analyzing project approach effectiveness
- Optimizing skill selection strategy
- Building project-specific knowledge base
- Enabling autonomous decision-making
- Tracking improvement over time
在以下场景使用本Skill:
- 启动任何新任务时(用于模式检索)
- 完成任何任务时(用于模式存储)
- 分析项目方法有效性时
- 优化技能选择策略时
- 构建项目专属知识库时
- 实现自主决策时
- 跟踪长期改进情况时
Integration with Agents
与Agent的集成
Orchestrator Agent:
- Uses pattern-learning for skill auto-selection
- Stores patterns after each task
- Queries patterns before delegation
Quality Controller Agent:
- References quality score calculations
- Uses trend analysis for improvement recommendations
All Specialized Agents:
- Reference pattern database for context
- Contribute to pattern storage after execution
编排Agent:
- 使用pattern-learning进行技能自动选择
- 任务完成后存储模式
- 委派任务前查询模式
质量控制Agent:
- 参考质量得分计算逻辑
- 使用趋势分析提供改进建议
所有专业Agent:
- 参考模式数据库获取上下文信息
- 执行完成后为模式存储做贡献
Resources
资源
Reference Files:
- REFERENCE.md: Detailed algorithm implementations
- pattern-database-schema.json: Complete data structure
- quality-metrics-guide.md: In-depth quality assessment guide
Auto-Generated Files (in project):
- .claude-patterns/patterns.json
- .claude-patterns/skill-effectiveness.json
- .claude-patterns/task-history.json
参考文件:
- REFERENCE.md: 详细算法实现
- pattern-database-schema.json: 完整数据结构
- quality-metrics-guide.md: 深度质量评估指南
自动生成文件(项目内):
- .claude-patterns/patterns.json
- .claude-patterns/skill-effectiveness.json
- .claude-patterns/task-history.json