/============================================================================/
/* ML SKILL :: VERILINGUA x VERIX EDITION /
/============================================================================*/
name: ml
version: 2.0.0
description: |
[assert|neutral] Machine Learning development workflow with experiment tracking, hyperparameter optimization, and MLOps integration [ground:given] [conf:0.95] [state:confirmed]
category: specialized-development
tags:
- machine-learning
- mlops
- experiment-tracking
- hyperparameter-tuning
- model-registry
author: ruv
cognitive_frame:
primary: aspectual
goal_analysis:
first_order: "Execute ml workflow"
second_order: "Ensure quality and consistency"
third_order: "Enable systematic specialized-development processes"
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/* S0 META-IDENTITY /
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[define|neutral] SKILL := {
name: "ml",
category: "specialized-development",
version: "2.0.0",
layer: L1
} [ground:given] [conf:1.0] [state:confirmed]
/----------------------------------------------------------------------------/
/* S1 COGNITIVE FRAME /
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[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.
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/* S2 TRIGGER CONDITIONS /
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[define|neutral] TRIGGER_POSITIVE := {
keywords: ["ml", "specialized-development", "workflow"],
context: "user needs ml capability"
} [ground:given] [conf:1.0] [state:confirmed]
/----------------------------------------------------------------------------/
/* S3 CORE CONTENT /
/----------------------------------------------------------------------------*/
ML Development Skill
Kanitsal Cerceve (Evidential Frame Activation)
Kaynak dogrulama modu etkin.
When to Use This Skill
- Model Training: Training neural networks or ML models
- Hyperparameter Tuning: Optimizing model performance
- Model Debugging: Diagnosing training issues (overfitting, vanishing gradients)
- Data Pipeline: Building training/validation data pipelines
- Experiment Tracking: Managing ML experiments and metrics
- Model Deployment: Serving models in production
When NOT to Use This Skill
- Data Analysis: Exploratory data analysis or statistics (use data scientist)
- Data Engineering: Large-scale ETL or data warehouse (use data engineer)
- Research: Novel algorithm development (use research specialist)
- Simple Rules: Heuristic-based logic without ML
Success Criteria
Edge Cases to Handle
- Class Imbalance: Unequal class distribution requiring resampling
- Data Leakage: Information from validation/test leaking into training
- Catastrophic Forgetting: Model forgetting old tasks when learning new ones
- Adversarial Examples: Model vulnerable to adversarial attacks
- Distribution Shift: Training data differs from production data
- Hardware Constraints: GPU memory limitations or mixed precision training
Guardrails
- NEVER evaluate on training data
- ALWAYS use separate train/validation/test splits
- NEVER touch test set until final evaluation
- ALWAYS version datasets and models
- NEVER deploy without monitoring for data drift
- ALWAYS document model assumptions and limitations
- NEVER train on biased or unrepresentative data
Evidence-Based Validation
Comprehensive machine learning development workflow with enterprise-grade experiment tracking, automated hyperparameter optimization, model registry management, and production MLOps pipelines.
Overview
This Gold-tier skill provides a complete ML development lifecycle with:
- Experiment Tracking: MLflow/W&B integration for reproducible experiments
- Hyperparameter Optimization: Optuna/Ray Tune for automated tuning
- Model Registry: Centralized model versioning and deployment
- MLOps Pipeline: Production-ready model serving and monitoring
Quick Start
# Initialize ML project
npx claude-flow sparc run ml "Create ML project for image classification"
# Track experiment
python resources/scripts/experiment-tracker.py --config experiment-config.yaml
# Optimize hyperparameters
node resources/scripts/hyperparameter-tuner.js --space hyperparameter-space.json
# Deploy model
bash resources/scripts/model-registry.sh deploy production latest
Workflow Phases
1. Experiment Design
- Define hypothesis and metrics
- Configure experiment tracking
- Set up data pipelines
- Validate data quality
2. Model Development
- Implement model architecture
- Configure training pipeline
- Set up validation strategy
- Enable experiment logging
3. Hyperparameter Optimization
- Define search space
- Select optimization algorithm
- Run distributed trials
- Analyze results
4. Model Evaluation
- Comprehensive metrics analysis
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/* S4 SUCCESS CRITERIA /
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[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]
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/* S6 MEMORY NAMESPACE /
/----------------------------------------------------------------------------*/
[define|neutral] MEMORY_NAMESPACE := {
pattern: "skills/specialized-development/ml/{project}/{timestamp}",
store: ["executions", "decisions", "patterns"],
retrieve: ["similar_tasks", "proven_patterns"]
} [ground:system-policy] [conf:1.0] [state:confirmed]
[define|neutral] MEMORY_TAGGING := {
WHO: "ml-{session_id}",
WHEN: "ISO8601_timestamp",
PROJECT: "{project_name}",
WHY: "skill-execution"
} [ground:system-policy] [conf:1.0] [state:confirmed]
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/* 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]
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/* S8 ABSOLUTE RULES /
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[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]
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/* PROMISE /
/----------------------------------------------------------------------------*/
[commit|confident] <promise>ML_VERILINGUA_VERIX_COMPLIANT</promise> [ground:self-validation] [conf:0.99] [state:confirmed]