Claude-Code-Workflow/.claude/commands/memory/load-skill-memory.md

name, description, argument-hint, allowed-tools, examples

name	description	argument-hint	allowed-tools	examples
load-skill-memory	Load SKILL package documentation with progressive levels based on task requirements	<skill_name> [--level 0|1|2|3] [task description]	Bash(*), Read(*), Glob(*)	/memory:load-skill-memory hydro_generator_module "分析热模型实现" /memory:load-skill-memory workflow-system --level 2 "修改任务调度逻辑" /memory:load-skill-memory Claude_dms3 "学习文档生成流程"

Memory Load SKILL Command (/memory:load-skill-memory)

1. Overview

The memory:load-skill-memory command loads SKILL package documentation with progressive levels (0-3) based on task requirements. It validates SKILL existence and intelligently selects appropriate documentation depth.

Core Philosophy:

• Two-Step Validation: Check SKILL existence before loading
• Progressive Loading: Load only necessary documentation based on task complexity
• Token Optimization: Start with minimal context, expand as needed
• Task-Driven Selection: Automatically select level based on task description

2. Parameters

• <skill_name> (Required): Name of SKILL package to load

  • Example: hydro_generator_module
  • Example: Claude_dms3
  • Example: multiphysics_network

• --level <0|1|2|3> (Optional): Force specific documentation level

  • Level 0: Quick Start (~2K tokens) - Overview only
  • Level 1: Core Modules (~10K tokens) - Key module APIs
  • Level 2: Complete (~25K tokens) - All modules + architecture
  • Level 3: Deep Dive (~40K tokens) - Everything including examples
  • Default: Auto-select based on task description

• [task description] (Optional): Task context for auto-level selection

  • Example: "分析热模型实现"
  • Example: "修改builder pattern"
  • Example: "学习参数系统设计"

3. Two-Step Execution Flow

Step 1: Validate SKILL Existence

Check SKILL.md File:

bash(test -f .claude/skills/{skill_name}/SKILL.md && echo "exists" || echo "not_found")

Validation Logic:

• If exists: Continue to Step 2
• If not_found: Return error message with available SKILLs:

  bash(ls -1 .claude/skills/ 2>/dev/null || echo "No SKILLs available")
  

Error Message Format:

❌ SKILL '{skill_name}' not found.

Available SKILLs:
- hydro_generator_module
- Claude_dms3
- multiphysics_network

Use: /memory:load-skill-memory <skill_name> [task description]

Step 2: Load Documentation by Level

Level Selection Logic:

1. Explicit Level (--level specified): Use specified level directly

2. Task-Based Auto-Selection:

   • Level 0 (Quick Start):

     • Keywords: "overview", "介绍", "what is", "概述", "快速了解"
     • Use case: Initial exploration, understanding basic capabilities

   • Level 1 (Core Modules):

     • Keywords: "分析", "analyze", "review", "理解", "understand", "API"
     • Use case: Deep analysis of specific modules, API review

   • Level 2 (Complete):

     • Keywords: "修改", "modify", "重构", "refactor", "设计", "architecture"
     • Use case: Modifying code, understanding system design

   • Level 3 (Deep Dive):

     • Keywords: "实现", "implement", "开发", "develop", "示例", "example"
     • Use case: New feature implementation, need complete context

3. Default: Level 1 if no task description and no --level flag

Loading Implementation:

# Read SKILL.md to get documentation structure
Read(.claude/skills/{skill_name}/SKILL.md)

# Based on selected level, read corresponding files
case $level in
  0)
    Read(../../../.workflow/docs/{skill_name}/README.md)
    ;;
  1)
    Read(../../../.workflow/docs/{skill_name}/README.md)
    # Read Core Module README.md files listed in SKILL.md Level 1
    ;;
  2)
    # Load all Level 1 docs
    Read(../../../.workflow/docs/{skill_name}/ARCHITECTURE.md)
    # Read all module README.md + API.md files
    ;;
  3)
    # Load all Level 2 docs
    Read(../../../.workflow/docs/{skill_name}/EXAMPLES.md)
    # Read complete documentation set
    ;;
esac

4. Output Format

Success Output:

✅ Loaded SKILL: {skill_name} (Level {level})

📦 Documentation Loaded:
- README.md (~2K tokens)
[Level 1+]
- analysis/README.md
- builders/README.md
- params/README.md
[Level 2+]
- ARCHITECTURE.md
- Complete module APIs
[Level 3+]
- EXAMPLES.md

💡 Token Budget: ~{token_count}K

🎯 Task Context: {task_description}

5. Usage Examples

Example 1: Quick Overview

/memory:load-skill-memory hydro_generator_module "快速了解模块功能"

Auto-Selected Level: 0 (keyword: "快速了解")

Loaded Files:

• .workflow/docs/hydro_generator_module/README.md

Token Budget: ~2K

Example 2: Module Analysis

/memory:load-skill-memory hydro_generator_module "分析builder pattern实现"

Auto-Selected Level: 1 (keyword: "分析")

Loaded Files:

• README.md
• builders/README.md
• builders/API.md
• interfaces/README.md
• params/README.md

Token Budget: ~10K

Example 3: Code Modification

/memory:load-skill-memory Claude_dms3 --level 2 "修改文档生成workflow"

Explicit Level: 2

Loaded Files:

• README.md
• All module README.md + API.md
• ARCHITECTURE.md

Token Budget: ~25K

Example 4: New Feature Implementation

/memory:load-skill-memory multiphysics_network "实现新的电磁耦合器"

Auto-Selected Level: 3 (keyword: "实现")

Loaded Files:

• Complete documentation set
• EXAMPLES.md with 20+ examples

Token Budget: ~40K

6. Level Selection Matrix

Task Type	Keywords	Auto Level	Token Budget
Explore	overview, 介绍, what is, 概述	0	~2K
Analyze	分析, analyze, review, 理解	1	~10K
Modify	修改, modify, 重构, refactor	2	~25K
Implement	实现, implement, 开发, develop	3	~40K

7. Implementation Steps

Pseudo-code:

// Step 1: Validate SKILL existence
skill_path = `.claude/skills/${skill_name}/SKILL.md`
if (!exists(skill_path)) {
  list_available_skills()
  return error_message
}

// Step 2: Determine level
if (--level specified) {
  level = specified_level
} else if (task_description) {
  level = auto_select_level(task_description)
} else {
  level = 1  // Default
}

// Step 3: Read SKILL.md structure
skill_content = Read(skill_path)
doc_structure = parse_progressive_loading(skill_content)

// Step 4: Load documentation by level
loaded_docs = []
for (doc in doc_structure[level]) {
  loaded_docs.push(Read(doc.path))
}

// Step 5: Calculate token count
token_count = estimate_tokens(loaded_docs)

// Step 6: Output summary
output_success_message(skill_name, level, loaded_docs, token_count, task_description)

8. Error Handling

SKILL Not Found

❌ SKILL 'unknown_module' not found.

Available SKILLs:
- hydro_generator_module (D:\dongdiankaifa9\hydro_generator_module)
- Claude_dms3 (D:\Claude_dms3)

Generate new SKILL: /memory:skill-memory [path]

Documentation Missing

⚠️  SKILL 'hydro_generator_module' exists but documentation incomplete.

Missing files:
- .workflow/docs/hydro_generator_module/ARCHITECTURE.md

Regenerate documentation: /memory:skill-memory --regenerate

Invalid Level

❌ Invalid level: 4

Valid levels:
- Level 0: Quick Start (~2K)
- Level 1: Core Modules (~10K)
- Level 2: Complete (~25K)
- Level 3: Deep Dive (~40K)

9. Integration with Other Commands

Workflow Integration:

# 1. Load SKILL context
/memory:load-skill-memory hydro_generator_module "修改thermal template"

# 2. Use loaded context for planning
/workflow:plan "增强thermal template支持动态阻抗"

# 3. Execute implementation
/workflow:execute

Memory Refresh:

# Load fresh context after code changes
/memory:load-skill-memory hydro_generator_module --level 2 "review recent changes"

10. Token Optimization Strategy

Progressive Loading Pattern:

1. Start with Level 0 for exploration
2. Escalate to Level 1 for analysis
3. Load Level 2 for modification tasks
4. Use Level 3 only for complex implementations

Token Budget Guidelines:

• Simple queries: Level 0-1 (2-10K)
• Code review: Level 1-2 (10-25K)
• Feature development: Level 2-3 (25-40K)

Reload Strategy:

• Reload with higher level if insufficient context
• Use explicit --level to override auto-selection

11. Notes

• Read-Only: Does not modify SKILL files or documentation
• Session-Scoped: Loaded documentation valid for current session
• Auto-Selection: Keywords matched case-insensitively
• Path Relative: All documentation paths relative to SKILL.md location
• Token Aware: Automatically estimates token consumption
• Fallback: Defaults to Level 1 if auto-selection unclear