Agent skill

Resource Analysis

by openshift-eng in openshift-eng/ai-helpers

Analyze system resource usage data from sosreport archives, extracting memory statistics, CPU load averages, disk space utilization, and process information from the sosreport directory structure to…

Apache-2.0Auto-check passedData & Analytics

Install Resource Analysis

skills CLI
$ npx skills add openshift-eng/ai-helpers --skill resource-analysis -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install openshift-eng/ai-helpers resource-analysis --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/openshift-eng/ai-helpers.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/sosreport/skills/resource-analysis .claude/skills/resource-analysis && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
resource-analysis
GitHub stars
120
Token cost
~3.4k tokens
SKILL.md length
898 words
Files
1
Skills in repo
118
Repo updated
First seen
Licence
Apache-2.0

At a glance

Analyze system resource usage data from sosreport archives, extracting memory statistics, CPU load averages, disk space utilization, and process information from the sosreport directory structure to…

  • Works in 6 steps: Analyze Memory Usage → Analyze CPU Usage → Analyze Disk Usage → …
  • Tasks that involve Statistics
  • SKILL.md covers When to Use This Skill, Prerequisites, Key Resource Data Locations in… and Implementation Steps, plus 7 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Resource Analysis is an agent skill from openshift-eng/ai-helpers. Analyze system resource usage data from sosreport archives, extracting memory statistics, CPU load averages, disk space utilization, and process information from the sosreport directory structure to diagnose resource exhaustion, performance bottlenecks, and capacity issues

Its SKILL.md is about 3.4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Data & Analytics, covering Statistics. The repository describes itself as: Developer productivity tools for Claude Code & other AI assistants. The licence is Apache-2.0.

When your agent uses it

  • Tasks that involve Statistics

Example prompts

  • “/resource-analysis”

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Analyze Memory Usage
  2. Analyze CPU Usage
  3. Analyze Disk Usage
  4. Analyze Process Information
  5. Correlate Resource Usage with Issues
  6. Generate Resource Analysis Summary

What it can do on your machine

Read from SKILL.md and the folder at commit a627176. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are bash).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Resource Analysis loads about 3.4k tokens when it runs. Until then it costs about 73 tokens; SKILL.md has 898 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~73
When it runs · the whole SKILL.md, loaded when a task matches
~3.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from openshift-eng/ai-helpers at commit a627176, republished under its Apache-2.0 licence (© openshift-eng). 898 words, ~3,438 tokens.

Download SKILL.mdSave it as .claude/skills/resource-analysis/SKILL.md (or your agent's skills folder).
name
resource-analysis
description
Analyze system resource usage data from sosreport archives, extracting memory statistics, CPU load averages, disk space utilization, and process information from the sosreport directory structure to diagnose resource exhaustion, performance bottlenecks, and capacity issues

Resource Analysis Skill

This skill provides detailed guidance for analyzing system resource usage from sosreport archives, including memory, CPU, disk space, and process information.

When to Use This Skill

Use this skill when:

  • Analyzing the /sosreport:analyze command's resource analysis phase
  • Investigating performance issues or resource bottlenecks
  • Identifying resource exhaustion problems
  • Correlating resource usage with system failures

Prerequisites

  • Sosreport archive must be extracted to a working directory
  • Path to the sosreport root directory must be known
  • Understanding of Linux resource management

Key Resource Data Locations in Sosreport

  1. Memory Information:

    • sos_commands/memory/free - Memory usage snapshot
    • proc/meminfo - Detailed memory statistics
    • sos_commands/memory/swapon_-s - Swap usage
    • proc/buddyinfo - Memory fragmentation
  2. CPU Information:

    • sos_commands/processor/lscpu - CPU architecture and features
    • proc/cpuinfo - Detailed CPU information
    • sos_commands/processor/turbostat - CPU frequency and power states (if available)
    • uptime - Load averages
  3. Disk Information:

    • sos_commands/filesys/df_-al - Filesystem usage
    • sos_commands/block/lsblk - Block device information
    • sos_commands/filesys/mount - Mounted filesystems
    • proc/diskstats - Disk I/O statistics
  4. Process Information:

    • sos_commands/process/ps_auxwww - Process list with details
    • sos_commands/process/top - Process snapshot (if available)
    • proc/[pid]/ - Per-process information

Implementation Steps

Step 1: Analyze Memory Usage
  1. Parse free command output:

    bash
    # Check if free output exists
    if [ -f sos_commands/memory/free ]; then
      cat sos_commands/memory/free
    fi
  2. Extract memory metrics:

    bash
    # Parse /proc/meminfo for detailed stats
    if [ -f proc/meminfo ]; then
      grep -E "^(MemTotal|MemFree|MemAvailable|Buffers|Cached|SwapTotal|SwapFree|Dirty|Slab):" proc/meminfo
    fi
  3. Calculate memory usage percentage:

    • Total memory = MemTotal
    • Used memory = MemTotal - MemAvailable
    • Usage percentage = (Used / Total) * 100
    • Parse from free output or calculate from meminfo
  4. Check for memory pressure indicators:

    bash
    # Look for OOM events in logs
    grep -i "out of memory\|oom killer" sos_commands/logs/journalctl_--no-pager 2>/dev/null
    
    # Check swap usage
    if [ -f sos_commands/memory/swapon_-s ]; then
      cat sos_commands/memory/swapon_-s
    fi
  5. Identify memory issues:

    • Memory usage > 90% → Critical
    • Memory usage > 80% → Warning
    • Heavy swap usage (>50% swap used) → Performance issue
    • OOM killer events → Critical memory exhaustion
Step 2: Analyze CPU Usage
  1. Extract CPU information:

    bash
    # Get CPU count and model
    if [ -f sos_commands/processor/lscpu ]; then
      grep -E "^(CPU\(s\)|Model name|Thread|Core|Socket|CPU MHz):" sos_commands/processor/lscpu
    fi
  2. Check load averages:

    bash
    # Parse uptime for load averages
    if [ -f uptime ]; then
      cat uptime
    fi
    
    # Or from proc/loadavg
    if [ -f proc/loadavg ]; then
      cat proc/loadavg
    fi
  3. Interpret load averages:

    • Load average format: 1-min, 5-min, 15-min
    • Compare with CPU count from lscpu
    • Load > CPU count → System overloaded
    • Load >> CPU count (2x or more) → Critical overload
  4. Check for CPU throttling:

    bash
    # Look for thermal throttling in logs
    grep -i "throttl\|temperature\|thermal" sos_commands/logs/journalctl_--no-pager 2>/dev/null | head -20
  5. Identify CPU issues:

    • 1-min load > 2x CPU count → Critical
    • 5-min load > CPU count → Warning
    • Thermal throttling present → Hardware/cooling issue
Step 3: Analyze Disk Usage
  1. Parse df output for filesystem usage:

    bash
    if [ -f sos_commands/filesys/df_-al ]; then
      # Skip header and special filesystems, show only regular filesystems
      grep -v "^Filesystem\|tmpfs\|devtmpfs\|overlay" sos_commands/filesys/df_-al | grep -v "^$"
    fi
  2. Identify full or nearly-full filesystems:

    bash
    # Extract filesystems with usage > 85%
    if [ -f sos_commands/filesys/df_-al ]; then
      awk 'NR>1 && $5+0 >= 85 {print $5, $6, $1}' sos_commands/filesys/df_-al | grep -v "tmpfs\|devtmpfs"
    fi
  3. Check disk I/O errors:

    bash
    # Look for I/O errors in logs
    grep -i "i/o error\|read error\|write error\|bad sector" var/log/dmesg 2>/dev/null
    grep -i "i/o error\|read error\|write error" sos_commands/logs/journalctl_--no-pager 2>/dev/null | head -20
  4. Analyze block devices:

    bash
    if [ -f sos_commands/block/lsblk ]; then
      cat sos_commands/block/lsblk
    fi
  5. Identify disk issues:

    • Filesystem > 95% full → Critical
    • Filesystem > 85% full → Warning
    • I/O errors present → Hardware issue
    • Root filesystem full → System stability risk
Step 4: Analyze Process Information
  1. Parse ps output:

    bash
    if [ -f sos_commands/process/ps_auxwww ]; then
      # Show header
      head -1 sos_commands/process/ps_auxwww
    fi
  2. Find top CPU consumers:

    bash
    # Sort by CPU usage (column 3), show top 10
    if [ -f sos_commands/process/ps_auxwww ]; then
      tail -n +2 sos_commands/process/ps_auxwww | sort -k3 -rn | head -10
    fi
  3. Find top memory consumers:

    bash
    # Sort by memory usage (column 4), show top 10
    if [ -f sos_commands/process/ps_auxwww ]; then
      tail -n +2 sos_commands/process/ps_auxwww | sort -k4 -rn | head -10
    fi
  4. Check for zombie processes:

    bash
    # Look for processes in Z state
    if [ -f sos_commands/process/ps_auxwww ]; then
      grep " Z " sos_commands/process/ps_auxwww || echo "No zombie processes found"
    fi
  5. Count processes by state:

    bash
    # Count processes by state (R=running, S=sleeping, D=uninterruptible, Z=zombie, T=stopped)
    if [ -f sos_commands/process/ps_auxwww ]; then
      tail -n +2 sos_commands/process/ps_auxwww | awk '{print $8}' | cut -c1 | sort | uniq -c
    fi
  6. Identify process issues:

    • Zombie processes present → Parent process not reaping children
    • Many processes in D state → I/O bottleneck
    • Single process using >80% memory → Memory leak or expected behavior
    • Many processes using high CPU → CPU contention
Show full SKILL.md (484 more words)Show less
Step 5: Correlate Resource Usage with Issues
  1. Cross-reference with logs:

    • If high memory usage, check for OOM events in logs
    • If high disk usage, check for disk full errors
    • If high load, check for performance-related errors
  2. Identify resource exhaustion patterns:

    • Memory exhaustion → OOM killer → Service crashes
    • Disk full → Write failures → Application errors
    • CPU overload → Timeouts → Request failures
  3. Build timeline:

    • When did resource issues start?
    • Correlate with log timestamps
    • Identify triggering event when log entries or metric changes indicate a clear cause
Step 6: Generate Resource Analysis Summary

Create a structured summary with the following sections:

  1. Memory Summary:

    • Total memory
    • Used memory (GB and %)
    • Available memory
    • Swap usage (GB and %)
    • Memory pressure indicators (OOM events)
  2. CPU Summary:

    • CPU count and model
    • Load averages (1-min, 5-min, 15-min)
    • Load per CPU
    • CPU issues (throttling, overload)
  3. Disk Summary:

    • Filesystems and usage percentages
    • Full or nearly-full filesystems
    • I/O errors count
    • Most full filesystem
  4. Process Summary:

    • Total process count
    • Top CPU consumers (top 5)
    • Top memory consumers (top 5)
    • Zombie process count
    • Processes in uninterruptible sleep (D state)
  5. Critical Resource Issues:

    • List issues by severity
    • Provide evidence (file paths, metrics)
    • Suggest remediation

Error Handling

  1. Missing resource files:

    • If free is missing, parse proc/meminfo directly
    • If ps is missing, check proc/ for process information
    • Document missing data in summary
  2. Parsing errors:

    • Handle different output formats (free -h vs free -m)
    • Account for locale differences in number formats
    • Validate data before calculations
  3. Incomplete data:

    • Some sosreports may not include all resource files
    • Indicate which metrics are unavailable
    • Work with available data only

Output Format

The resource analysis should produce:

bash
RESOURCE USAGE SUMMARY
======================

MEMORY
------
Total:      {total_gb} GB
Used:       {used_gb} GB ({used_pct}%)
Available:  {available_gb} GB ({available_pct}%)
Buffers:    {buffers_gb} GB
Cached:     {cached_gb} GB
Swap Total: {swap_total_gb} GB
Swap Used:  {swap_used_gb} GB ({swap_used_pct}%)

Status: {OK|WARNING|CRITICAL}
Issues:
  - {memory_issue_description}

CPU
---
Model:        {cpu_model}
CPU Count:    {cpu_count}
Threads/Core: {threads_per_core}

Load Averages: {load_1m}, {load_5m}, {load_15m}
Load per CPU:  {load_1m_per_cpu}, {load_5m_per_cpu}, {load_15m_per_cpu}

Status: {OK|WARNING|CRITICAL}
Issues:
  - {cpu_issue_description}

DISK USAGE
----------
Filesystem                    Size  Used  Avail  Use%  Mounted on
{filesystem}                 {size} {used} {avail} {pct}% {mount}

Nearly Full Filesystems (>85%):
  - {mount}: {pct}% full ({available} available)

I/O Errors: {count} errors found in logs

Status: {OK|WARNING|CRITICAL}
Issues:
  - {disk_issue_description}

PROCESSES
---------
Total Processes: {total}
Running:         {running}
Sleeping:        {sleeping}
Zombie:          {zombie}
Uninterruptible: {uninterruptible}

Top CPU Consumers:
  1. {process_name} (PID {pid}): {cpu}% CPU, {mem}% MEM
  2. {process_name} (PID {pid}): {cpu}% CPU, {mem}% MEM
  3. {process_name} (PID {pid}): {cpu}% CPU, {mem}% MEM

Top Memory Consumers:
  1. {process_name} (PID {pid}): {mem}% MEM, {cpu}% CPU
  2. {process_name} (PID {pid}): {mem}% MEM, {cpu}% CPU
  3. {process_name} (PID {pid}): {mem}% MEM, {cpu}% CPU

Status: {OK|WARNING|CRITICAL}
Issues:
  - {process_issue_description}

CRITICAL RESOURCE ISSUES
------------------------
{severity}: {issue_description}
  Evidence: {file_path}
  Impact: {impact_description}
  Recommendation: {remediation_action}

RECOMMENDATIONS
---------------
1. {actionable_recommendation}
2. {actionable_recommendation}

DATA SOURCES
------------
- Memory: {sosreport_path}/sos_commands/memory/free
- Memory: {sosreport_path}/proc/meminfo
- CPU: {sosreport_path}/sos_commands/processor/lscpu
- Load: {sosreport_path}/uptime
- Disk: {sosreport_path}/sos_commands/filesys/df_-al
- Processes: {sosreport_path}/sos_commands/process/ps_auxwww

Examples

Example 1: Memory Analysis
bash
# Parse free command output
$ cat sos_commands/memory/free
              total        used        free      shared  buff/cache   available
Mem:       16277396     8123456     2145678      123456     6008262     7654321
Swap:       8388604      512000     7876604

# Interpretation:
# - Total RAM: ~16 GB
# - Used: ~8 GB (50%)
# - Available: ~7.6 GB (47%)
# - Swap used: ~500 MB (6%)
# Status: OK - healthy memory usage
Example 2: Disk Full Detection
bash
# Find filesystems > 85% full
$ awk 'NR>1 && $5+0 >= 85' sos_commands/filesys/df_-al
/dev/sda1      50G   45G   5G   90%  /
/dev/sdb1      100G  96G   4G   96%  /var/log

# Critical: Root filesystem at 90%, /var/log at 96%
# Action required: Clean up disk space
Example 3: High Load Investigation
bash
# Check load averages
$ cat uptime
14:23:45 up 10 days, 3:42, 2 users, load average: 8.45, 7.23, 6.12

# With lscpu showing 4 CPUs:
# Load per CPU: 2.1, 1.8, 1.5
# System is overloaded (load > 2x CPU count)

Tips for Effective Analysis

  1. Context matters: High resource usage isn't always bad - consider the workload
  2. Look for trends: Compare 1-min, 5-min, 15-min loads to see if issues are growing
  3. Correlate metrics: High load + high memory + disk full = multiple issues
  4. Check ratios: Usage percentages are more meaningful than absolute values
  5. Validate findings: Cross-reference with log analysis for confirmation
  6. Consider capacity: Does the system have enough CPU, memory, and disk for its workload?

Common Resource Patterns

  1. Memory leak: Steadily increasing memory usage, eventual OOM
  2. Disk full: Application writes failing, log rotation issues
  3. CPU spike: Load average spike, potentially from runaway process
  4. I/O bottleneck: High load but low CPU usage, many D-state processes
  5. Swap thrashing: High swap usage, poor performance
  6. Zombie accumulation: Parent process bug not reaping children

Severity Classification

MetricOKWarningCritical
Memory Usage< 80%80-90%> 90%
Swap Usage< 20%20-50%> 50%
Disk Usage< 85%85-95%> 95%
Load (per CPU)< 1.01.0-2.0> 2.0
Root FS Usage< 80%80-90%> 90%

See Also

  • Logs Analysis Skill: For finding resource-related errors in logs
  • System Configuration Analysis Skill: For investigating service resource limits
  • Network Analysis Skill: For network-related performance issues

© openshift-eng, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in plugins/sosreport/skills/resource-analysis of openshift-eng/ai-helpers.

Open the folder on GitHubat commit a627176

Compare with similar skills

Resource Analysis next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Resource Analysis compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Resource Analysis this skillopenshift-eng/ai-helpers120—~3.4kAutomated safety check: PassApache-2.0
Sandbox Benchvercel/next.js143k—~4.1kAutomated safety check: PassMIT
Statistical Analysisspacering-net/codeg3.8k4 repos~5kAutomated safety check: PassMIT
StatsmodelszLanqing/codex-claude-academic-skills4.6k16 repos~4.9kAutomated safety check: PassBSD-3-Clause
Statistical Powerspacering-net/codeg3.8k2 repos~3.6kAutomated safety check: NotesMIT
AI Daily DigestvigorX777/ai-daily-digest1.6k—~1.3kAutomated safety check: PassNone

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Questions about Resource Analysis

What does Resource Analysis do?

Analyze system resource usage data from sosreport archives, extracting memory statistics, CPU load averages, disk space utilization, and process information from the sosreport directory structure to…. Resource Analysis is an agent skill from openshift-eng/ai-helpers.

When should I use Resource Analysis?

Resource Analysis fits situations like: tasks that involve Statistics.

How do I install Resource Analysis in Claude Code?

Run `npx skills add openshift-eng/ai-helpers --skill resource-analysis -a claude-code`. Or copy the skill folder (plugins/sosreport/skills/resource-analysis in openshift-eng/ai-helpers) into .claude/skills/resource-analysis in your project. Claude Code loads it when a task matches its description.

How do I install Resource Analysis in Codex?

Run `npx skills add openshift-eng/ai-helpers --skill resource-analysis -a codex`. Or copy the skill folder (plugins/sosreport/skills/resource-analysis in openshift-eng/ai-helpers) into .agents/skills/resource-analysis in your project. Codex loads it when a task matches its description.

Can I use Resource Analysis in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add openshift-eng/ai-helpers --skill resource-analysis -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/resource-analysis, .gemini/skills/resource-analysis, .github/skills/resource-analysis and .opencode/skills/resource-analysis in your project.

What does Resource Analysis need to run?

SKILL.md names no scripts, command-line tools or credentials: Resource Analysis is instructions for the agent only.

Does Resource Analysis access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Resource Analysis safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Resource Analysis use?

Resource Analysis is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Resource Analysis use?

About 3.4k tokens (SKILL.md is roughly 14k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Resource Analysis?

Skills that share tags, products or a category with Resource Analysis: Sandbox Bench (vercel/next.js, 143k stars), Statistical Analysis (spacering-net/codeg, 3.8k stars), Statsmodels (zLanqing/codex-claude-academic-skills, 4.6k stars) and Statistical Power (spacering-net/codeg, 3.8k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Resource Analysis?

openshift-eng (a GitHub organization) maintains it in openshift-eng/ai-helpers, which has 120 GitHub stars. The repository holds 118 skills in this directory. The repository was last updated on October 6, 2026.

Source: openshift-eng/ai-helpers on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.