Complete Synthesized Claude Code Plugin Architecture with skill-systems-complete Integration

Complete Synthesized Claude Code Plugin with skill-systems-complete Integration

Plugin Architecture with Actual Implementation Files

claude-code-exemplar-plugin/
├── .claude-plugin/
│   └── plugin.json
│
├── commands/                               # User-triggered workflows
│   ├── skillforge.sh                      # FROM: skill-architect/commands/skillforge.sh
│   ├── skillwhisper.sh                    # FROM: skill-integration-auditor/commands/skillwhisper.sh
│   ├── create-skill.md                    # Wrapper: delegates to skill-genesis skill
│   ├── audit-component.md                 # Wrapper: delegates to skill-integration-optimizer
│   └── analyze-activation.md              # Wrapper: uses skillwhisper.sh + semantic_analyzer.py
│
├── skills/                                # Claude-invoked capabilities
│   ├── skill-genesis/                     # FROM: skill-architect/skills/skill-genesis/
│   │   ├── SKILL.md
│   │   ├── scripts/
│   │   │   ├── activation_optimizer.py   # ACTUAL Python module
│   │   │   └── semantic_analyzer.py      # ACTUAL Python module
│   │   └── references/
│   │       ├── patterns.md
│   │       └── activation-theory.md
│   │
│   └── skill-integration-optimizer/       # FROM: skill-integration-auditor/skills/
│       ├── SKILL.md
│       ├── scripts/
│       │   └── integration_analyzer.py   # ACTUAL Python module
│       └── references/
│           └── integration-patterns.md
│
├── agents/                                # Autonomous specialists
│   ├── skill-architect.yaml              # FROM: skill-architect/agents/skill-architect.yaml
│   └── integration-auditor.yaml          # FROM: skill-integration-auditor/agents/skill-auditor.yaml
│
├── hooks/
│   └── hooks.json
│
└── .mcp.json

Implementation File Locations and Purposes

1. Python Modules (Executable Intelligence)

activation_optimizer.py (skills/skill-genesis/scripts/)

• Purpose: Optimizes skill descriptions for >90% activation rates
• Key Functions:
• calculate_activation_probability(description, prompt) - Returns 0.0-1.0 score
• optimize_description(initial_desc, test_prompts) - Iterative optimization
• find_missing_triggers(description, prompts) - Gap analysis
• test_activation(description, prompts) - Batch testing
• Invoked By:
• skillforge.sh bash script
• skill-genesis SKILL.md via Bash tool
• skill-architect agent for skill generation
• Data Flow:

```python
# Called from skill-genesis SKILL.md
from scripts.activation_optimizer import ActivationOptimizer

optimizer = ActivationOptimizer()
optimized_desc, score = optimizer.optimize_description(
initial_description,
test_prompts=['find security bugs', 'scan for vulnerabilities']
)
```

semantic_analyzer.py (skills/skill-genesis/scripts/)

• Purpose: Extracts semantic patterns from prompts for skill creation
• Key Functions:
• analyze_prompt(prompt) - Returns dict with actions, objects, domains, clarity
• extract_actions(text) - Returns list of action verbs
• identify_domains(text) - Returns list of technical domains
• calculate_clarity(text) - Returns 0-100 clarity score
• generate_triggers(semantic_components) - Returns ranked trigger phrases
• Invoked By:
• skillforge.sh for intent crystallization
• skill-architect agent for semantic analysis
• activation_optimizer.py for trigger extraction
• Data Flow:

```python
from scripts.semantic_analyzer import SemanticAnalyzer

analyzer = SemanticAnalyzer()
analysis = analyzer.analyze_prompt("find SQL injection vulnerabilities")
# Returns: {
# 'actions': ['find', 'identify'],
# 'objects': ['vulnerability', 'injection'],
# 'domains': ['security'],
# 'clarity_score': 85.0
# }
```

integration_analyzer.py (skills/skill-integration-optimizer/scripts/)

• Purpose: Finds skill integration opportunities in existing code
• Key Functions:
• analyze_component(component_path) - Full integration analysis
• extract_capabilities(content) - Parse code for capabilities
• find_redundancies(content) - Detect duplicate implementations
• calculate_skill_relevance(capabilities, skill_pattern) - 0.0-1.0 score
• generate_replacement(pattern, skill, code) - Refactoring specs
• Invoked By:
• skillaudit.sh bash script
• integration-auditor agent
• skill-integration-optimizer SKILL.md
• Data Flow:

```python
from scripts.integration_analyzer import IntegrationAnalyzer

analyzer = IntegrationAnalyzer()
analysis = analyzer.analyze_component(Path('~/.claude/agents/code-reviewer.md'))
# Returns: {
# 'opportunities': [IntegrationOpportunity(...)],
# 'redundancies': [RedundancyFinding(...)],
# 'metrics': {...}
# }
```

2. Bash Scripts (CLI Entry Points)

skillforge.sh (commands/)

• Purpose: CLI for creating activation-optimized skills
• Workflow:

1. Parses user capability description + optional test prompts
2. Calls semantic_analyzer.py to evaluate intent clarity (0-100%)
3. If clarity < 40%: generates clarification dialogue
4. If clarity >= 40%: calls activation_optimizer.py for optimization
5. Generates SKILL.md, activation-map.json, test suite, semantic fingerprint
6. Writes to /tmp/generated-skills/{skill-name}/

• Usage:

``bash
/skillforge "analyze Python code for security issues" \
--test-prompts "find vulnerabilities" "check for SQL injection"
``

• Integration Points:
• Uses Python scripts via subprocess
• Writes structured output files
• Returns skill package for installation

skillwhisper.sh (commands/)

• Purpose: Optimize prompts for maximum skill activation
• Workflow:

1. Locates target skill in ~/.claude/skills/
2. Extracts skill description from SKILL.md YAML
3. Calculates activation probability via token overlap
4. Generates optimized prompt variants
5. Displays activation heatmap with scores

• Modes:
• --mode optimize: Generate optimal prompt
• --mode test: Test activation probability
• --mode explain: Show activation mechanics
• Usage:

``bash
/skillwhisper security-scanner "check my code"
# Returns: "identify security vulnerabilities" (89% activation)
``

• Integration Points:
• Reads ~/.claude/skills/*/SKILL.md files
• Pure bash (no Python dependencies)
• Returns activation scores and optimized prompts

3. Agent Configurations

skill-architect.yaml (agents/)

• System Prompt: Specialized for activation-first skill creation
• Tools: read, write, bash, create, analyze
• Environment:

``yaml
SKILL_OUTPUT_DIR: "/tmp/generated-skills"
ACTIVATION_THRESHOLD: "80"
``

• Invoked By: /create-skill command via Task tool
• Workflow: Uses activation_optimizer.py + semantic_analyzer.py to generate skills

integration-auditor.yaml (agents/)

• System Prompt: Specialized for finding integration opportunities
• Tools: read, write, analyze, diff, test
• Environment:

``yaml
AUDIT_OUTPUT_DIR: "/tmp/skill-audit-results"
MIN_ACTIVATION_TARGET: "80"
``

• Invoked By: /audit-component command via Task tool
• Workflow: Uses integration_analyzer.py to analyze components

4. Command Wrappers (Delegation Pattern)

create-skill.md (commands/)

---
description: Create activation-optimized skill with intent crystallization
argument-hint: [capability description]
allowed-tools: [Task, Read(/tmp/generated-skills/**)]
---

<objective>
Invoke skill-architect agent to create new skill with >90% activation rate.
Agent uses activation_optimizer.py and semantic_analyzer.py for optimization.
</objective>

<subagent_contract>
skill-architect will:
1. Analyze capability description using semantic_analyzer.py (clarity scoring)
2. Optimize description using activation_optimizer.py (iterative improvement)
3. Generate SKILL.md, activation-map.json, tests, semantic fingerprint
4. Write to /tmp/generated-skills/{skill-name}/
5. Return: "Skill created at: [path]" with activation score
</subagent_contract>

<process>
1. REQUIRED: Invoke skill-architect agent with: $ARGUMENTS
2. REQUIRED: Read generated skill files from returned path
3. REQUIRED: Present installation options to user via AskUserQuestion
</process>

analyze-activation.md (commands/)

---
description: Analyze activation patterns using semantic analysis tools
argument-hint: [skill-name] [test-prompt]
allowed-tools: [Bash, Read]
---

<objective>
Use skillwhisper.sh and semantic_analyzer.py to analyze activation.
</objective>

<process>
1. Run skillwhisper.sh for token-based activation scoring
2. Use semantic_analyzer.py for deep semantic analysis
3. Compare results and generate recommendations
</process>

Actual Invocation Patterns

Pattern 1: Skill Creation Workflow

User: /create-skill "parse OpenAPI specs and generate Python clients"

create-skill.md (parent command)
    ↓ [Task tool]
skill-architect.yaml (agent)
    ↓ [Bash tool]
skillforge.sh (bash script)
    ↓ [subprocess calls]
semantic_analyzer.py → analyze_prompt() → clarity_score: 85%
    ↓
activation_optimizer.py → optimize_description() → optimized desc
    ↓ [writes files]
/tmp/generated-skills/openapi-python-generator/
    ├── SKILL.md
    ├── activation-map.json
    ├── tests/test_activation.sh
    └── semantic-fingerprint.md
    ↓ [returns to agent]
skill-architect agent → returns "Skill created at: /tmp/generated-skills/..."
    ↓ [returns to parent]
create-skill.md → reads files → presents install options to user

Pattern 2: Activation Optimization Workflow

User: /skillwhisper security-scanner "check my code for bugs"

skillwhisper.sh
    ↓ [reads]
~/.claude/skills/security-scanner/SKILL.md → extract description
    ↓ [calculates]
tokenize("check my code for bugs") ∩ tokenize(description)
    ↓
overlap_score: 42% (low activation)
    ↓ [optimizes]
add_missing_triggers(["identify", "security", "vulnerabilities"])
    ↓
optimized: "identify security vulnerabilities in code"
activation_score: 89%
    ↓ [returns]
Display: Original 42% → Optimized 89% (+47% improvement)

Pattern 3: Component Audit Workflow

User: /audit-component ~/.claude/agents/code-reviewer.md

audit-component.md (parent)
    ↓ [Task tool]
integration-auditor.yaml (agent)
    ↓ [Bash tool]
integration_analyzer.py
    ↓
analyze_component(Path("code-reviewer.md"))
    ├→ extract_capabilities() → {actions: ['review', 'analyze'], ...}
    ├→ calculate_skill_relevance() → security-scanner: 0.85
    ├→ find_redundancies() → lines 45-78 duplicate security checks
    └→ generate_replacement() → invoke_skill('security-scanner', ...)
    ↓ [writes]
/tmp/skill-audit-results/code-reviewer-audit-2025-11-22.md
    ↓ [returns to agent]
integration-auditor → returns "Report written to: [path]"
    ↓ [returns to parent]
audit-component.md → reads report → AskUserQuestion:
    - "Apply fixes now"
    - "Create GitHub issue"
    - "Review report only"

Complete File Manifest with Purposes

| File | Type | Purpose | Invokes | Invoked By |
|------|------|---------|---------|------------|
| activation_optimizer.py | Python | Iteratively optimize descriptions for activation | None | skillforge.sh, skill-architect agent |
| semantic_analyzer.py | Python | Extract semantic patterns from prompts | None | skillforge.sh, activation_optimizer.py |
| integration_analyzer.py | Python | Find integration opportunities in code | None | integration-auditor agent |
| skillforge.sh | Bash | CLI for skill creation | activation_optimizer.py, semantic_analyzer.py | create-skill.md, direct CLI |
| skillwhisper.sh | Bash | CLI for activation optimization | None (pure bash) | analyze-activation.md, direct CLI |
| skill-architect.yaml | Agent | Autonomous skill creation specialist | skillforge.sh via Bash tool | create-skill.md via Task tool |
| integration-auditor.yaml | Agent | Autonomous integration analyst | integration_analyzer.py via Bash tool | audit-component.md via Task tool |
| create-skill.md | Command | User-facing skill creation wrapper | skill-architect agent via Task tool | User types /create-skill |
| audit-component.md | Command | User-facing audit wrapper | integration-auditor agent via Task tool | User types /audit-component |
| skill-genesis/SKILL.md | Skill | Contains skill creation knowledge | activation_optimizer.py, semantic_analyzer.py | Claude automatically when relevant |
| skill-integration-optimizer/SKILL.md | Skill | Contains integration knowledge | integration_analyzer.py | Claude automatically when relevant |

This is the complete, concrete architecture showing exactly where each file from skill-systems-complete lives in the plugin and how they all interconnect.