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smf-analyzer

Analyze SMF records to profile MIPS usage and identify high-cost programs.

1|1|Updated Apr 6, 2026
Search Tags:#migration#profiling#dead code#workload#smf#mainframe#mips
One-click install
npx skills add https://github.com/duboc/mainframe-skills-mig --skill smf-analyzer
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: smf-analyzer
Source: https://github.com/duboc/mainframe-skills-mig/tree/main/skills/02-smf-analyzer
Command: npx skills add https://github.com/duboc/mainframe-skills-mig --skill smf-analyzer

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill addresses the challenge of identifying which mainframe programs consume the most resources and actually run in production, enabling targeted migration efforts that focus on high-impact workloads while safely retiring dead code.

Core Features & Use Cases

  • MIPS Profiling: Analyzes SMF records to calculate CPU consumption and establish billing baselines for programs and LPARs.
  • Workload Segregation: Separates CICS online transactions from batch jobs, providing SLA metrics and access patterns for VSAM datasets.
  • Dead Code Detection: Cross-references runtime data with inventory to flag unused programs, reducing migration scope.
  • Use Case: For a legacy banking system, use this Skill to process a year's SMF logs and generate a priority matrix that highlights the top 20% of programs driving 80% of MIPS costs, guiding the team to migrate critical transaction processors first.

Quick Start

Analyze the provided SMF Type 30 and 110 records to generate a MIPS baseline and migration priority matrix for our mainframe inventory.

Frequently Asked Questions about smf-analyzer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I identify high-cost mainframe programs for migration using SMF records?▼

Mainframe migration profiling analyzes SMF Type 30 and 110 records to calculate CPU consumption and MIPS baselines. By evaluating runtime telemetry, it identifies the top programs driving costs and generates a priority matrix to guide targeted migration efforts.

What is the best way to profile mainframe workloads and separate CICS transactions from batch jobs?▼

Workload segregation analyzes SMF records to separate CICS online transactions from batch jobs. This provides distinct SLA metrics and maps VSAM dataset access patterns, enabling precise MIPS calculation and targeted migration planning for high-impact workloads.

Can I process binary EBCDIC SMF logs with Python for MIPS calculation?▼

Yes, mainframe workload profiling supports data ingestion from both CSV and binary EBCDIC formats using Python with pandas. This allows you to aggregate runtime telemetry and calculate MIPS consumption for priority scoring without needing specialized mainframe reporting tools.

How do I map VSAM access patterns to prioritize mainframe migration?▼

Mapping VSAM access patterns involves analyzing SMF records to track dataset interactions during CICS and batch processing. This reveals high-frequency access programs, establishing a priority matrix that highlights critical transaction processors for early migration.

What are the limitations of using SMF records for mainframe migration planning?▼

SMF analysis requires Python with pandas for data ingestion and aggregation. It depends on having complete SMF Type 30 and 110 logs, meaning programs not generating runtime telemetry or lacking inventory cross-references cannot be accurately profiled for dead code detection.

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