🦸 Superman Splunk Skill

A comprehensive AI skill that turns any LLM into a Splunk expert — covering architecture, SPL, development, administration, Enterprise Security, and operations at production scale.

What Is This?

Superman Splunk is an AI skill — a structured knowledge file that activates when you need Splunk expertise inside an AI coding assistant (Claude Code, GitHub Copilot CLI, Gemini CLI, or any Superpowers-compatible agent).

When the skill activates, the AI gains:

• Deep knowledge of the 3-tier Splunk architecture (forwarders → indexers → search heads)
• Expert-level SPL (Search Processing Language) covering every major command and best practice
• Complete app and add-on development guidance including custom commands, modular inputs, REST API, and Python SDK
• Production administration knowledge: all key conf files, config precedence, deployment server, license management
• Enterprise Security (ES), SOAR, ITSI, RBAC, TLS hardening, and Risk-Based Alerting

This skill was built by running deep automated research across 300+ web sources in Google NotebookLM, synthesizing those findings, and packaging them into a structured reference format.

📊 Benchmark Results

Evaluated across 5 real-world Splunk scenarios comparing AI with vs without the skill:

Key differentiators:

• ✅ Transparent Huge Pages (THP) tuning — the skill surfaces this critical Linux OS setting that the base model misses
• ✅ Risk-Based Alerting implementation steps — the skill provides concrete from-scratch workflows vs theory-only baseline
• ✅ TA directory structure + secure credential handling — complete patterns, not partial examples
• ⚠️ V2 custom command protocol — improvement opportunity identified (see roadmap)

Full benchmark methodology and eval prompts are in evals/evals.json.

🗂️ Knowledge Coverage

🚀 Installation

Option 1 — Clone directly into your skills directory

# Claude Code / Copilot CLI / Gemini CLI (Superpowers)
git clone https://github.com/ishayvilroel/superman-splunk \
  ~/.agents/skills/superman-splunk

That's it. The skill auto-discovers from the skills directory.

Option 2 — Manual copy

Download or clone this repo, then copy the folder to your AI platform's skills directory:

Option 3 — Packaged .skill file

Download superman-splunk.skill from the Releases page and import it via your skill manager.

⚡ Usage

Once installed, the skill activates automatically when Splunk is mentioned in your conversation. No explicit invocation needed.

Example Prompts

"How do I set up indexer clustering with RF=3 and SF=2?"

"Write me an SPL query to detect credential stuffing attacks from firewall logs"

"Build me a modular input TA that polls a REST API every 5 minutes and handles credential storage securely"

"My indexing pipeline is saturating typingQueue — walk me through diagnosing and fixing it"

"Design an RBA (Risk-Based Alerting) workflow in Enterprise Security from scratch"

The AI will automatically read the relevant reference files (architecture, SPL, development, etc.) before responding — giving you expert-level answers grounded in deep Splunk knowledge.

📁 Repository Structure

superman-splunk/
├── README.md                     # This file
├── SKILL.md                      # Skill entry point — the AI reads this first
├── CHANGELOG.md                  # Version history
├── LICENSE                       # MIT License
│
├── references/                   # Deep knowledge files (AI reads on demand)
│   ├── architecture.md           # 3-tier arch, buckets, clustering, SmartStore
│   ├── spl-guide.md              # Full SPL reference & knowledge objects
│   ├── development.md            # App dev, custom commands, REST API, SDK
│   ├── administration.md         # All conf files, deployment, licensing
│   └── security-es.md            # ES, SOAR, RBAC, TLS, RBA, ITSI
│
├── evals/
│   └── evals.json                # Benchmark eval prompts and assertions
│
└── docs/
    ├── how-it-works.md           # Architecture of the skill itself
    ├── splunk-quick-reference.md # Ports, conf files, commands cheatsheet
    └── benchmark.md              # Full benchmark methodology and results

🧠 How It Works

The skill uses a lazy-loading reference pattern:

1. SKILL.md — the entry point — loads first and gives the AI a domain routing table.
2. The AI reads only the reference files it needs for the current question (e.g., an SPL question → reads spl-guide.md, not all 5 files).
3. For broad questions, the AI reads multiple references before composing an answer.

This keeps token usage efficient while giving the AI access to the full knowledge base on demand.

User asks about Splunk
         │
         ▼
    SKILL.md loads
    (domain routing table)
         │
    ┌────┴────────────────────────────┐
    │                                 │
SPL question?                 Architecture question?
    │                                 │
reads spl-guide.md            reads architecture.md
    │                                 │
    └────────────────┬────────────────┘
                     │
              Expert response

🗺️ Roadmap

• V2 custom command protocol — more prominent coverage; current skill version has it in development.md but agents don't surface it reliably
• Splunk Cloud vs Enterprise decision matrix with migration guide
• ITSI deep-dive module (Episode pages, Glass Tables, KPI baselines)
• Splunk Observability (OTel, APM, RUM, Synthetics) extension
• SPL2 / Federated Search coverage

🤝 Contributing

Contributions are welcome. If you:

• Find a Splunk fact that's wrong or outdated — open an issue or PR
• Have a new eval scenario to add — add it to evals/evals.json
• Want to extend a reference file — PRs to references/ are the right place

Please keep reference files factual, precise, and actionable — the goal is expert-level accuracy, not high-level summaries.

📄 License

MIT © ishayvilroel

This skill was created with Superpowers AI skill-creator tooling.
Research was conducted using Google NotebookLM across 300+ web sources covering the full Splunk documentation surface.

"No question is too advanced or too basic — from 'how do I search logs' to 'design a multi-site indexer cluster with SmartStore.'"