majiayu000 /============================================================================/ /* MACHINE-LEARNING SKILL :: VERILINGUA x VERIX EDITION / /============================================================================*/
name: machine-learning version: 2.0.0 description: | [assert|neutral] Comprehensive machine learning development with training, evaluation, and deployment capabilities. Use when training models, developing ML pipelines, or deploying machine learning systems. [ground:given] [conf:0.95] [state:confirmed] category: platforms tags:
- ml
- deep-learning
- training
- evaluation
- deployment author: SPARC System cognitive_frame: primary: aspectual goal_analysis: first_order: "Execute machine-learning workflow" second_order: "Ensure quality and consistency" third_order: "Enable systematic platforms processes"
/----------------------------------------------------------------------------/ /* S0 META-IDENTITY / /----------------------------------------------------------------------------*/
[define|neutral] SKILL := { name: "machine-learning", category: "platforms", version: "2.0.0", layer: L1 } [ground:given] [conf:1.0] [state:confirmed]
/----------------------------------------------------------------------------/ /* S1 COGNITIVE FRAME / /----------------------------------------------------------------------------*/
[define|neutral] COGNITIVE_FRAME := { frame: "Aspectual", source: "Russian", force: "Complete or ongoing?" } [ground:cognitive-science] [conf:0.92] [state:confirmed]
Kanitsal Cerceve (Evidential Frame Activation)
Kaynak dogrulama modu etkin.
/----------------------------------------------------------------------------/ /* S2 TRIGGER CONDITIONS / /----------------------------------------------------------------------------*/
[define|neutral] TRIGGER_POSITIVE := { keywords: ["machine-learning", "platforms", "workflow"], context: "user needs machine-learning capability" } [ground:given] [conf:1.0] [state:confirmed]
/----------------------------------------------------------------------------/ /* S3 CORE CONTENT / /----------------------------------------------------------------------------*/
When NOT to Use This Skill
- Simple data preprocessing without model training
- Statistical analysis that does not require ML models
- Rule-based systems without learning components
- Operations that do not involve model training or inference
Success Criteria
- [assert|neutral] Model training convergence: Loss decreasing consistently [ground:acceptance-criteria] [conf:0.90] [state:provisional]
- [assert|neutral] Validation accuracy: Meeting or exceeding baseline targets [ground:acceptance-criteria] [conf:0.90] [state:provisional]
- [assert|neutral] Training time: Within expected bounds for dataset size [ground:acceptance-criteria] [conf:0.90] [state:provisional]
- [assert|neutral] GPU utilization: >80% during training [ground:acceptance-criteria] [conf:0.90] [state:provisional]
- [assert|neutral] Model export success: 100% successful saves [ground:acceptance-criteria] [conf:0.90] [state:provisional]
- [assert|neutral] Inference latency: <100ms for real-time applications [ground:acceptance-criteria] [conf:0.90] [state:provisional]
Edge Cases & Error Handling
- GPU Memory Overflow: Reduce batch size, use gradient accumulation, or mixed precision
- Divergent Training: Implement learning rate scheduling, gradient clipping
- Data Pipeline Failures: Validate data integrity, handle missing/corrupted files
- Version Mismatches: Lock dependency versions, use containerization
- Checkpoint Corruption: Save multiple checkpoints, validate before loading
- Distributed Training Failures: Handle node failures, implement fault tolerance
Guardrails & Safety
- [assert|emphatic] NEVER: train on unvalidated or uncleaned data [ground:policy] [conf:0.98] [state:confirmed]
- [assert|neutral] ALWAYS: validate model outputs before deployment [ground:policy] [conf:0.98] [state:confirmed]
- [assert|neutral] ALWAYS: implement reproducibility (random seeds, version pinning) [ground:policy] [conf:0.98] [state:confirmed]
- [assert|emphatic] NEVER: expose training data in model artifacts or logs [ground:policy] [conf:0.98] [state:confirmed]
- [assert|neutral] ALWAYS: monitor for bias and fairness issues [ground:policy] [conf:0.98] [state:confirmed]
- [assert|neutral] ALWAYS: implement model versioning and rollback capabilities [ground:policy] [conf:0.98] [state:confirmed]
Evidence-Based Validation
- Verify hardware availability: Check GPU/TPU status before training
- Validate data quality: Run data integrity checks and statistics
- Monitor training: Track loss curves, gradients, and metrics
- Test model performance: Evaluate on held-out test set
- Benchmark inference: Measure latency and throughput under load
Machine Learning Development Skill
Kanitsal Cerceve (Evidential Frame Activation)
Kaynak dogrulama modu etkin.
Complete workflow for machine learning model development, training, evaluation, and deployment.
When to Use
Auto-trigger when detecting:
- "train model", "machine learning", "ML pipeline"
- "deep learning", "neural network", "model training"
- "data preprocessing", "feature engineering"
- "model evaluation", "hyperparameter tuning"
- "model deployment", "ML ops"
Capabilities
1. Data Pipeline
- Data preprocessing and cleaning
- Feature engineering and selection
- Data augmentation
- Train/validation/test splitting
- Data versioning with DVC
2. Model Training
- Neural network architectures
- Hyperparameter optimization
- Transfer learning
- Distributed training
- Training monitoring and logging
3. Model Evaluation
- Multi-metric evaluation
- Cross-validation
- Confusion matrices and ROC curves
- Fairness and bias detection
- Performance benchmarking
4. Model Deployment
- Model serialization and versioning
- API endpoint creation
- Containerization
- Monitoring and logging
- A/B testing support
Agent Workflow
// Auto-spawned agents for ML development
Task("ML Researcher", "Research SOTA models and best practices for [task]", "researcher")
Task("
/*----------------------------------------------------------------------------*/
/* S4 SUCCESS CRITERIA */
/*----------------------------------------------------------------------------*/
[define|neutral] SUCCESS_CRITERIA := {
primary: "Skill execution completes successfully",
quality: "Output meets quality thresholds",
verification: "Results validated against requirements"
} [ground:given] [conf:1.0] [state:confirmed]
/*----------------------------------------------------------------------------*/
/* S5 MCP INTEGRATION */
/*----------------------------------------------------------------------------*/
[define|neutral] MCP_INTEGRATION := {
memory_mcp: "Store execution results and patterns",
tools: ["mcp__memory-mcp__memory_store", "mcp__memory-mcp__vector_search"]
} [ground:witnessed:mcp-config] [conf:0.95] [state:confirmed]
/*----------------------------------------------------------------------------*/
/* S6 MEMORY NAMESPACE */
/*----------------------------------------------------------------------------*/
[define|neutral] MEMORY_NAMESPACE := {
pattern: "skills/platforms/machine-learning/{project}/{timestamp}",
store: ["executions", "decisions", "patterns"],
retrieve: ["similar_tasks", "proven_patterns"]
} [ground:system-policy] [conf:1.0] [state:confirmed]
[define|neutral] MEMORY_TAGGING := {
WHO: "machine-learning-{session_id}",
WHEN: "ISO8601_timestamp",
PROJECT: "{project_name}",
WHY: "skill-execution"
} [ground:system-policy] [conf:1.0] [state:confirmed]
/*----------------------------------------------------------------------------*/
/* S7 SKILL COMPLETION VERIFICATION */
/*----------------------------------------------------------------------------*/
[direct|emphatic] COMPLETION_CHECKLIST := {
agent_spawning: "Spawn agents via Task()",
registry_validation: "Use registry agents only",
todowrite_called: "Track progress with TodoWrite",
work_delegation: "Delegate to specialized agents"
} [ground:system-policy] [conf:1.0] [state:confirmed]
/*----------------------------------------------------------------------------*/
/* S8 ABSOLUTE RULES */
/*----------------------------------------------------------------------------*/
[direct|emphatic] RULE_NO_UNICODE := forall(output): NOT(unicode_outside_ascii) [ground:windows-compatibility] [conf:1.0] [state:confirmed]
[direct|emphatic] RULE_EVIDENCE := forall(claim): has(ground) AND has(confidence) [ground:verix-spec] [conf:1.0] [state:confirmed]
[direct|emphatic] RULE_REGISTRY := forall(agent): agent IN AGENT_REGISTRY [ground:system-policy] [conf:1.0] [state:confirmed]
/*----------------------------------------------------------------------------*/
/* PROMISE */
/*----------------------------------------------------------------------------*/
[commit|confident] <promise>MACHINE_LEARNING_VERILINGUA_VERIX_COMPLIANT</promise> [ground:self-validation] [conf:0.99] [state:confirmed]