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feat: implement dynamic SKILL discovery with intelligent matching
Transform load-skill-memory from manual specification to automatic discovery: **Core Change**: - From: User specifies SKILL name manually - To: System automatically discovers and matches SKILL based on task context **New Capabilities**: 1. **Three-Step Execution**: - Step 1: Discover all available SKILLs (.claude/skills/) - Step 2: Match most relevant SKILL using scoring algorithm - Step 3: Activate matched SKILL via Skill() tool 2. **Intelligent Matching Algorithm**: - **Path-Based** (Highest Priority): Direct path match from file paths - **Keyword Matching** (Secondary): Score by keyword overlap - **Action Matching** (Tertiary): Detect action verbs (分析/修改/学习) 3. **Updated Parameters**: - From: `<skill_name> [--level] [task description]` - To: `"task description or file path"` - More intuitive user experience 4. **New Examples**: ```bash /memory:load-skill-memory "分析热模型builder pattern实现" /memory:load-skill-memory "D:\dongdiankaifa9\hydro_generator_module\builders\base.py" /memory:load-skill-memory "修改workflow文档生成逻辑" ``` **Matching Examples**: Task: "分析热模型builder pattern实现" - hydro_generator_module: 4 points (thermal+builder+analyzing) ✅ - Claude_dms3: 1 point (analyzing only) Task: "D:\dongdiankaifa9\hydro_generator_module\builders\base.py" - Path match: hydro_generator_module ✅ (exact path) **Benefits**: - No manual SKILL name required - Automatic best match selection - Path-based intelligent routing - Keyword scoring for relevance - Action verb detection for context **User Experience**: Before: "/memory:load-skill-memory hydro_generator_module '分析热模型'" After: "/memory:load-skill-memory '分析热模型实现'" System automatically discovers and activates hydro_generator_module SKILL. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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catlog22
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---
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name: load-skill-memory
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description: Load SKILL package documentation with progressive levels based on task requirements
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argument-hint: "<skill_name> [--level 0|1|2|3] [task description]"
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description: Automatically discover and activate SKILL packages based on task context with intelligent matching
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argument-hint: "\"task description or file path\""
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allowed-tools: Bash(*), Read(*), Glob(*), Skill(*)
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examples:
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- Skill(command: "hydro_generator_module")
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- Skill(command: "Claude_dms3")
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- Skill(command: "multiphysics_network")
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- /memory:load-skill-memory "分析热模型builder pattern实现"
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- /memory:load-skill-memory "修改workflow文档生成逻辑"
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- /memory:load-skill-memory "D:\dongdiankaifa9\hydro_generator_module\builders\base.py"
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---
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# Memory Load SKILL Command (/memory:load-skill-memory)
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## 1. Overview
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The `memory:load-skill-memory` command **activates SKILL packages** generated by `/memory:skill-memory`. It validates SKILL existence and uses the `Skill()` tool to load comprehensive documentation context.
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The `memory:load-skill-memory` command **automatically discovers and activates** the most relevant SKILL package based on task description or file path. It dynamically matches user intent against available SKILL descriptions and activates the best match.
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**Core Philosophy**:
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- **Two-Step Process**: Validate SKILL existence → Activate via Skill tool
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- **Automatic Context Loading**: SKILL system loads appropriate documentation automatically
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- **No Manual Reading**: Use Skill tool instead of manual Read operations
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- **Task-Driven Activation**: System automatically determines required context based on SKILL description triggers
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- **Dynamic SKILL Discovery**: Automatically finds relevant SKILL without manual specification
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- **Intelligent Matching**: Matches task keywords against SKILL descriptions
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- **Path-Based Detection**: Recognizes project paths and activates corresponding SKILL
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- **Automatic Activation**: Uses Skill() tool to load comprehensive context
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## 2. Parameters
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- `<skill_name>` (Required): Name of SKILL package to activate
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- Example: `hydro_generator_module`
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- Example: `Claude_dms3`
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- Example: `multiphysics_network`
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- `"task description or file path"` (Required): Task context or file path for SKILL matching
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- **Task description**: "分析热模型实现", "修改workflow逻辑", "学习参数系统"
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- **File path**: "D:\dongdiankaifa9\hydro_generator_module\builders\base.py"
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- **Domain keywords**: "thermal modeling", "workflow management", "multi-physics"
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## 3. Two-Step Execution Flow
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## 3. Three-Step Execution Flow
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### Step 1: Validate SKILL Existence
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### Step 1: Discover Available SKILLs
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**Check SKILL.md File**:
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**List All SKILL Packages**:
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```bash
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bash(test -f .claude/skills/{skill_name}/SKILL.md && echo "exists" || echo "not_found")
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bash(ls -1 .claude/skills/ 2>/dev/null || echo "No SKILLs available")
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```
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**Validation Logic**:
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- If `exists`: Continue to Step 2
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- If `not_found`: Return error message with available SKILLs:
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```bash
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bash(ls -1 .claude/skills/ 2>/dev/null || echo "No SKILLs available")
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```
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**Error Message Format**:
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**Read Each SKILL.md for Matching**:
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```bash
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# For each SKILL directory found
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for skill in $(ls -1 .claude/skills/); do
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Read(.claude/skills/${skill}/SKILL.md)
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done
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```
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❌ SKILL '{skill_name}' not found.
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**Extract Matching Information**:
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- SKILL name
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- Description (with trigger keywords)
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- Location path (from description)
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- Domain keywords
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### Step 2: Match Most Relevant SKILL
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**Matching Algorithm**:
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1. **Path-Based Matching** (Highest Priority):
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- Extract path from user input if provided
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- Compare against SKILL location paths in descriptions
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- Exact match: `D:\dongdiankaifa9\hydro_generator_module` → `hydro_generator_module` SKILL
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2. **Keyword Matching** (Secondary Priority):
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- Extract keywords from task description
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- Match against SKILL description keywords
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- Score each SKILL by keyword overlap count
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3. **Action Matching** (Tertiary Priority):
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- Detect action verbs: "分析", "修改", "学习", "实现"
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- Match against SKILL description triggers
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- Prefer SKILLs with matching action patterns
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**Scoring Example**:
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```javascript
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Task: "分析热模型builder pattern实现"
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hydro_generator_module SKILL:
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- Path match: No
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- Keywords: "热模型"(1), "builder"(1), "实现"(1) = 3 points
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- Action match: "analyzing"(1) = 1 point
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- Total: 4 points ✅ Winner
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Claude_dms3 SKILL:
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- Path match: No
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- Keywords: "workflow"(0) = 0 points
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- Action match: "analyzing"(1) = 1 point
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- Total: 1 point
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```
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**No Match Handling**:
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```
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⚠️ No matching SKILL found for: "{task_description}"
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Available SKILLs:
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- hydro_generator_module (D:\dongdiankaifa9\hydro_generator_module)
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- Claude_dms3 (D:\Claude_dms3)
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- hydro_generator_module - Hydro-generator thermal modeling
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- Claude_dms3 - Workflow orchestration system
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Use: Skill(command: "skill_name")
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Generate new SKILL: /memory:skill-memory [path]
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Generate SKILL for your project: /memory:skill-memory [path]
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```
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### Step 2: Activate SKILL Package
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### Step 3: Activate Matched SKILL
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**Use Skill Tool to Activate**:
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**Activate Best Match**:
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```javascript
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Skill(command: "{skill_name}")
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Skill(command: "{matched_skill_name}")
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```
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**What Happens**:
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1. Claude's SKILL system reads `.claude/skills/{skill_name}/SKILL.md`
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1. System reads `.claude/skills/{matched_skill_name}/SKILL.md`
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2. Automatically loads appropriate documentation based on:
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- SKILL description triggers (analyzing, modifying, learning)
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- Current conversation context
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- Memory gaps detection
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3. Progressive loading levels (0-3) handled automatically by system
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3. Progressive loading levels (0-3) handled automatically
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4. Context loaded directly into conversation memory
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**No Manual Reading Required**: The Skill tool handles all documentation loading automatically based on SKILL.md configuration.
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**Confirmation Output**:
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```
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✅ Matched and activated SKILL: {matched_skill_name}
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🎯 Match reason: {path/keyword/action match}
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📦 Location: {project_path}
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💡 Context loaded for: {domain_description}
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```
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## 4. Output Format
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@@ -95,63 +143,109 @@ Skill(command: "{skill_name}")
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## 5. Usage Examples
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### Example 1: Activate Hydro Generator Module SKILL
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### Example 1: Task-Based Discovery (Keyword Matching)
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**User Request**: "分析热模型builder pattern实现"
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**User Command**:
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```bash
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/memory:load-skill-memory "分析热模型builder pattern实现"
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```
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**Command Execution**:
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**Execution Flow**:
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```javascript
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// Step 1: Validate
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bash(test -f .claude/skills/hydro_generator_module/SKILL.md && echo "exists")
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// Output: exists
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// Step 1: Discover available SKILLs
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bash(ls -1 .claude/skills/)
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// Output: hydro_generator_module, Claude_dms3
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// Step 2: Activate
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// Read each SKILL.md
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Read(.claude/skills/hydro_generator_module/SKILL.md)
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Read(.claude/skills/Claude_dms3/SKILL.md)
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// Step 2: Match keywords
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Keywords extracted: ["热模型", "builder", "pattern", "实现", "分析"]
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Matching scores:
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- hydro_generator_module: 4 points (thermal modeling, builder, analyzing)
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- Claude_dms3: 1 point (analyzing only)
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Best match: hydro_generator_module
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// Step 3: Activate matched SKILL
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Skill(command: "hydro_generator_module")
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```
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**SKILL Activation**:
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- System reads `.claude/skills/hydro_generator_module/SKILL.md`
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- Description triggers match: "analyzing" + "hydro-generator thermal modeling"
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- Automatically loads appropriate documentation (Level 1-2)
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- Context available for subsequent analysis
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**Output**:
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```
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✅ Matched and activated SKILL: hydro_generator_module
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🎯 Match reason: Keywords ["thermal", "builder"] + Action ["analyzing"]
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📦 Location: D:\dongdiankaifa9\hydro_generator_module
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💡 Context loaded for: hydro-generator thermal modeling
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```
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### Example 2: Activate Workflow System SKILL
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### Example 2: Path-Based Discovery (Direct Path Match)
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**User Request**: "修改文档生成workflow调度逻辑"
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**User Command**:
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```bash
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/memory:load-skill-memory "D:\dongdiankaifa9\hydro_generator_module\builders\base.py"
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```
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**Command Execution**:
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**Execution Flow**:
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```javascript
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// Step 1: Validate
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bash(test -f .claude/skills/Claude_dms3/SKILL.md && echo "exists")
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// Output: exists
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// Step 1: Discover SKILLs
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bash(ls -1 .claude/skills/)
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// Step 2: Activate
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// Step 2: Match path
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Path extracted: "D:\dongdiankaifa9\hydro_generator_module"
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Matching:
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- hydro_generator_module location: "D:\dongdiankaifa9\hydro_generator_module" ✅ Exact match
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- Claude_dms3 location: "D:\Claude_dms3" ❌ No match
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Best match: hydro_generator_module (path match - highest priority)
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// Step 3: Activate
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Skill(command: "hydro_generator_module")
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```
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**Output**:
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```
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✅ Matched and activated SKILL: hydro_generator_module
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🎯 Match reason: Path match (D:\dongdiankaifa9\hydro_generator_module)
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📦 Location: D:\dongdiankaifa9\hydro_generator_module
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💡 Context loaded for: hydro-generator thermal modeling
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```
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### Example 3: Domain Keyword Discovery
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**User Command**:
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```bash
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/memory:load-skill-memory "修改workflow文档生成调度逻辑"
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```
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**Execution Flow**:
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```javascript
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// Step 1: Discover SKILLs
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bash(ls -1 .claude/skills/)
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// Step 2: Match keywords
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Keywords: ["workflow", "文档生成", "调度", "修改"]
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Matching scores:
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- Claude_dms3: 3 points (workflow, docs generation, modifying)
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- hydro_generator_module: 1 point (modifying only)
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Best match: Claude_dms3
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// Step 3: Activate
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Skill(command: "Claude_dms3")
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```
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**SKILL Activation**:
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- Description triggers match: "modifying" + "workflow management"
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- System loads comprehensive documentation (Level 2-3)
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- Ready for workflow modification tasks
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### Example 3: Activate Multi-Physics Network SKILL
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**User Request**: "学习电磁域组件设计模式"
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**Command Execution**:
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```javascript
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// Step 1: Validate
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bash(test -f .claude/skills/multiphysics_network/SKILL.md && echo "exists")
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// Output: exists
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// Step 2: Activate
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Skill(command: "multiphysics_network")
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**Output**:
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```
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✅ Matched and activated SKILL: Claude_dms3
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🎯 Match reason: Keywords ["workflow", "docs"] + Action ["modifying"]
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📦 Location: D:\Claude_dms3
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💡 Context loaded for: workflow orchestration and documentation
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```
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**SKILL Activation**:
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- Description triggers match: "learning" + "multi-physics"
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- System provides overview and core module docs (Level 0-1)
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- Context available for exploration
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## 6. SKILL Trigger Mechanism
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