/============================================================================/ /* WHEN-DEVELOPING-ML-MODELS-USE-ML-EXPERT SKILL :: VERILINGUA x VERIX EDITION / /============================================================================*/

name: when-developing-ml-models-use-ml-expert version: 1.0.0 description: | [assert|neutral] Specialized ML model development, training, and deployment workflow [ground:given] [conf:0.95] [state:confirmed] category: machine-learning tags:

• ml
• training
• deployment
• model-development
• neural-networks author: ruv cognitive_frame: primary: aspectual goal_analysis: first_order: "Execute when-developing-ml-models-use-ml-expert workflow" second_order: "Ensure quality and consistency" third_order: "Enable systematic machine-learning processes"

/----------------------------------------------------------------------------/ /* S0 META-IDENTITY / /----------------------------------------------------------------------------*/

[define|neutral] SKILL := { name: "when-developing-ml-models-use-ml-expert", category: "machine-learning", version: "1.0.0", 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: ["when-developing-ml-models-use-ml-expert", "machine-learning", "workflow"], context: "user needs when-developing-ml-models-use-ml-expert 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

ML Expert - Machine Learning Model Development

Kanitsal Cerceve (Evidential Frame Activation)

Kaynak dogrulama modu etkin.

Overview

Specialized workflow for ML model development, training, and deployment. Supports various architectures (CNNs, RNNs, Transformers) with distributed training capabilities.

When to Use

• Developing new ML models
• Training neural networks
• Model optimization
• Production deployment
• Transfer learning
• Fine-tuning existing models

Phase 1: Data Preparation (10 min)

Objective

Clean, preprocess, and prepare training data

Agent: ML-Developer

Step 1.1: Load and Analyze Data

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split

# Load data
data = pd.read_csv('dataset.csv')

# Analyze
analysis = {
    'shape': data.shape,
    'columns': data.columns.tolist(),
    'dtypes': data.dtypes.to_dict(),
    'missing': data.isnull().sum().to_dict(),
    'stats': data.describe().to_dict()
}

# Store analysis
await memory.store('ml-expert/data-analysis', analysis)

Step 1.2: Data Cleaning

# Handle missing values
data = data.fillna(data.mean())

# Remove duplicates
data = data.drop_duplicates()

# Handle outliers
from scipy import stats
z_scores = np.abs(stats.zscore(data.select_dtypes(include=

/*----------------------------------------------------------------------------*/
/* 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/machine-learning/when-developing-ml-models-use-ml-expert/{project}/{timestamp}",
  store: ["executions", "decisions", "patterns"],
  retrieve: ["similar_tasks", "proven_patterns"]
} [ground:system-policy] [conf:1.0] [state:confirmed]

[define|neutral] MEMORY_TAGGING := {
  WHO: "when-developing-ml-models-use-ml-expert-{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>WHEN_DEVELOPING_ML_MODELS_USE_ML_EXPERT_VERILINGUA_VERIX_COMPLIANT</promise> [ground:self-validation] [conf:0.99] [state:confirmed]