model-detection

Primary Detection Methods

1. System Context Analysis:

```javascript

// Check for model indicators in system context

const modelIndicators = {

'claude-sonnet-4.5': { pattern: /sonnet.4\.5|4\.5.sonnet/i, confidence: 0.9 },

'claude-haiku-4.5': { pattern: /haiku.4\.5|4\.5.haiku/i, confidence: 0.9 },

'claude-opus-4.1': { pattern: /opus.4\.1|4\.1.opus/i, confidence: 0.9 },

'glm-4.6': { pattern: /glm|4\.6/i, confidence: 0.9 },

'claude-haiku': { pattern: /haiku(?!\.*4\.5)/i, confidence: 0.8 }

}

```

1. Performance Pattern Recognition:

```javascript

// Analyze execution patterns to identify model

const performanceSignatures = {

'claude-sonnet-4.5': { reasoning: 'nuanced', speed: 'fast', adaptability: 'high' },

'claude-haiku-4.5': { reasoning: 'focused', speed: 'very_fast', adaptability: 'high' },

'claude-opus-4.1': { reasoning: 'enhanced', speed: 'very_fast', adaptability: 'very_high' },

'glm-4.6': { reasoning: 'structured', speed: 'moderate', adaptability: 'medium' }

}

```

1. Capability Assessment:

```javascript

// Test specific capabilities

const capabilityTests = {

nuanced_reasoning: testAmbiguousScenario,

structured_execution: testLiteralInterpretation,

context_switching: testMultiTaskContext,

adaptive_learning: testPatternRecognition

}

```

Claude Sonnet 4.5 Configuration

```json

{

"model_type": "claude-sonnet-4.5",

"capabilities": {

"reasoning_style": "nuanced",

"context_management": "adaptive",

"skill_loading": "progressive_disclosure",

"error_handling": "pattern_based",

"communication_style": "natural_flow"

},

"performance_targets": {

"execution_time_multiplier": 1.0,

"quality_score_target": 90,

"autonomy_level": "high",

"delegation_style": "parallel_context_merge"

},

"optimizations": {

"use_context_switching": true,

"apply_improvisation": true,

"weight_based_decisions": true,

"predictive_delegation": true

}

}

```

Claude Haiku 4.5 Configuration

```json

{

"model_type": "claude-haiku-4.5",

"capabilities": {

"reasoning_style": "focused",

"context_management": "efficient",

"skill_loading": "selective_disclosure",

"error_handling": "fast_prevention",

"communication_style": "concise"

},

"performance_targets": {

"execution_time_multiplier": 0.8,

"quality_score_target": 88,

"autonomy_level": "medium",

"delegation_style": "focused_parallel"

},

"optimizations": {

"use_fast_execution": true,

"apply_focused_reasoning": true,

"efficient_delegation": true,

"streamlined_processing": true

}

}

```

Claude Opus 4.1 Configuration

```json

{

"model_type": "claude-opus-4.1",

"capabilities": {

"reasoning_style": "enhanced",

"context_management": "predictive",

"skill_loading": "intelligent_progressive",

"error_handling": "predictive_prevention",

"communication_style": "insightful"

},

"performance_targets": {

"execution_time_multiplier": 0.9,

"quality_score_target": 95,

"autonomy_level": "very_high",

"delegation_style": "predictive_parallel"

},

"optimizations": {

"use_context_switching": true,

"apply_improvisation": true,

"anticipatory_actions": true,

"enhanced_pattern_learning": true

}

}

```

GLM-4.6 Configuration

```json

{

"model_type": "glm-4.6",

"capabilities": {

"reasoning_style": "structured",

"context_management": "sequential",

"skill_loading": "complete_loading",

"error_handling": "rule_based",

"communication_style": "structured_explicit"

},

"performance_targets": {

"execution_time_multiplier": 1.25,

"quality_score_target": 88,

"autonomy_level": "medium",

"delegation_style": "sequential_clear"

},

"optimizations": {

"use_structured_decisions": true,

"explicit_instructions": true,

"sequential_processing": true,

"clear_handoffs": true

}

}

```

Skill Loading Adaptation

Claude Models:

```javascript

function loadSkillsForClaude(skills) {

// Progressive disclosure with context merging

return skills.map(skill => ({

...skill,

loading_strategy: 'progressive',

context_aware: true,

weight_based: true

}));

}

```

GLM Models:

```javascript

function loadSkillsForGLM(skills) {

// Complete upfront loading with clear structure

return skills.map(skill => ({

...skill,

loading_strategy: 'complete',

explicit_criteria: true,

priority_sequenced: true

}));

}

```

Communication Style Adaptation

Output Formatting by Model:

| Model | Terminal Style | File Report Style | Reasoning |

|-------|----------------|-------------------|-----------|

| Claude Sonnet | Natural flow | Insightful analysis | Nuanced communication |

| Claude 4.5 | Concise insights | Enhanced context | Predictive communication |

| GLM-4.6 | Structured lists | Detailed procedures | Explicit communication |

Error Recovery Adaptation

Claude Models: Pattern-based prediction and contextual prevention

GLM Models: Rule-based detection and structured recovery protocols

Nuanced Reasoning Test

```javascript

function testNuancedReasoning() {

// Present ambiguous scenario requiring subtle judgment

// Evaluate response quality and contextual awareness

return score >= 0.8; // True for Claude models

}

```

Structured Execution Test

```javascript

function testStructuredExecution() {

// Present clear, sequential task

// Evaluate adherence to structured approach

return score >= 0.8; // True for GLM models

}

```

Auto-Detection Function

```javascript

function detectModel() {

// Step 1: Check system context indicators

const contextResult = analyzeSystemContext();

// Step 2: Test capability patterns

const capabilityResult = testCapabilities();

// Step 3: Analyze performance signature

const performanceResult = analyzePerformancePattern();

// Step 4: Combine results with confidence scoring

return combineDetections(contextResult, capabilityResult, performanceResult);

}

```

Configuration Loading

```javascript

function loadModelConfiguration(detectedModel) {

const baseConfig = getBaseModelConfig(detectedModel);

const adaptiveConfig = generateAdaptiveConfig(detectedModel);

return mergeConfigurations(baseConfig, adaptiveConfig);

}

```

When to Apply Model Detection

1. Plugin Initialization: First load of any agent
2. Agent Delegation: Before delegating to specialized agents
3. Skill Loading: Before loading any skill package
4. Error Recovery: When selecting recovery strategy
5. Performance Optimization: When setting execution targets

Integration Points

• Orchestrator Agent: Use for autonomous decision-making adaptation
• All Specialized Agents: Use for model-specific behavior
• Skill System: Use for loading strategy selection
• Quality Controller: Use for model-appropriate quality targets

If model detection fails:

1. Default to Conservative Settings: Use structured, explicit approach
2. Basic Capability Tests: Run simplified detection tests
3. Universal Configuration: Apply cross-model compatible settings
4. Performance Monitoring: Continuously assess and adapt

Detection Accuracy

• Target: >95% correct model identification
• Measurement: Compare detected vs actual model capabilities
• Validation: Test across all supported models

Performance Improvement

• Target: >10% improvement for GLM models
• Target: >2% improvement for Claude models
• Measurement: Compare pre/post optimization performance

Adaptation Success

• Target: >90% successful adaptation scenarios
• Measurement: Monitor successful autonomous operations
• Validation: Test with diverse task types

This skill ensures the Autonomous Agent Plugin performs optimally across all supported LLM models while maintaining backward compatibility and future-proofing for new models.