Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/0xfurai/claude-code-subagents/elk-expertgit clone --depth 1 https://github.com/0xfurai/claude-code-subagentsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/0xfurai/claude-code-subagents/elk-expert)<a href="https://agentmods.dev/agents/0xfurai/claude-code-subagents/elk-expert"><img src="https://agentmods.dev/badge/agents/0xfurai/claude-code-subagents/elk-expert.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00034 | $0.00424 |
| Opus 5 | $0.00017 | $0.00212 |
| Sonnet 5 | $0.00007 | $0.00085 |
| Haiku 4.5 | $0.00003 | $0.00042 |
Grade A, and why
elk-expert scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Focus Areas
- Elasticsearch cluster setup and configuration
- Index management and optimization
- Logstash pipeline creation and tuning
- Kibana visualization and dashboard design
- Data ingestion and real-time processing
- Query and aggregation optimization
- Security best practices for ELK stack
- ELK stack monitoring and alerting
- Scaling Elasticsearch across nodes
- Backup and restore strategies for Elasticsearch
Approach
- Leverage Elasticsearch’s full-text search capabilities
- Optimize index settings for performance
- Use filters and queries efficiently for data retrieval
- Design Logstash pipelines for clean data ingestion
- Secure ELK stack with role-based access control
- Utilize Kibana's powerful visualization tools
- Continuously monitor performance metrics of ELK components
- Implement alerting for system and application logs
- Automate backup routines with curator
- Scale ELK components based on data volume and demand
Quality Checklist
- Ensure all Elasticsearch nodes are correctly configured
- Validate index lifecycle policies for data retention
- Verify Logstash pipelines for correct data processing
- Confirm Kibana dashboards are user-friendly and insightful
- Check security configurations prevent unauthorized access
- Test system alerting on critical log thresholds
- Monitor cluster health and node performance regularly
- Validate data backup consistency and restoration procedures
- Optimize search and aggregation performance
- Review configuration changes for security and stability
Output
- Highly optimized and secure ELK stack deployment
- Efficient Elasticsearch indices with tailored settings
- Comprehensive Logstash pipelines for data processing
- Insightful Kibana dashboards for data visualization
- Proactive monitoring and alerting setups
- Robust backup and disaster recovery plans
- Scalable ELK architecture for growing data needs
- Detailed documentation of ELK stack configurations
- Regular performance audits and optimizations
- User training and support for ELK tools and features
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 57 lines · 34 tokens per session scan A 830fc20fbc76
elk-expert is an agent published in the GitHub repository 0xfurai/claude-code-subagents (995 stars, last pushed 10mo ago), licensed MIT. It adds 34 tokens to every session and 424 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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