ziya
Reduces context-switching overhead and tribal knowledge loss by centralizing technical analysis, architecture decisions, and operational insights in a persis…
Self-hosted AI technical workbench. Visual, persistent, multi-stream. Development, architecture, and operational analytics without losing context.
- Ask Claude to analyze application logs across multiple data streams simultaneously without losing session context.
- Generate architecture diagrams and technical documentation while referencing live metrics and code changes in parallel.
- Find performance bottlenecks by correlating operational metrics, logs, and development metrics in one persistent workspace.
Reduces context-switching overhead and tribal knowledge loss by centralizing technical analysis, architecture decisions, and operational insights in a persistent, visual workspace that teams can reference and build upon.
Engineering teams managing complex system architecture or operational analytics who need shared context across development, design, and incident review cycles.
https://github.com/ziya-ai/ziya
By ziya-ai
How to Get It
claude plugins install ziya-ai/ziya
Tip: Paste this into a Claude Code conversation. Verify command matches your Claude Code version.
Auto-generated from the tool's public listing — not hands-on verified. Cross-check against the source repo's README before running.
After installing, paste this into Claude:
Analyze application logs across multiple data streams simultaneously without losing session context
Trust Signals Auto-scanned
Community Pulse New
No community discussions found yet. This doesn't mean the tool isn't good — it may be new or serve a niche use case.
Reviewer notes
Auto-scanned review. These are observations, not a security certification.
Scored from trust signals (evidence-eval-v1): 38 GitHub stars; contributors unknown; last commit 0d ago; license MIT.
Things to check
- Scanned, not hands-on tested — this entry was auto-scanned from public metadata (GitHub metrics, license, security flags). No reviewer has run it, and no tool-specific limitations have been documented yet.
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Evaluation
Scored from trust signals (evidence-eval-v1): 38 GitHub stars; contributors unknown; last commit 0d ago; license MIT.