context-guru
Reduces token consumption and API latency for AI-assisted development workflows, directly lowering operational costs while maintaining code quality and sugge…
Context optimization for AI coding agents — lower token cost and latency without sacrificing accuracy.
- Ask Claude to analyze large codebases and extract only the most relevant files for a specific bug fix.
- Generate optimized code context summaries to reduce token usage when debugging multi-service architectures.
- Automate the process of trimming unnecessary code snippets before sending requests to AI coding assistants.
Reduces token consumption and API latency for AI-assisted development workflows, directly lowering operational costs while maintaining code quality and suggestion accuracy.
Engineering teams integrating AI coding agents into CI/CD pipelines or daily development practices seeking cost efficiency.
https://github.com/rossoctl/context-guru
By rossoctl
How to Get It
claude plugins install rossoctl/context-guru
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 large codebases and extract only the most relevant files for a specific bug fix
Trust Signals Auto-scanned
Reviewer notes
Auto-scanned review. These are observations, not a security certification.
Scored from trust signals (evidence-eval-v1): 56 GitHub stars; contributors unknown; last commit 0d ago; license Apache-2.0.
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.
How to evaluate tools before deploying →
Data shown here comes from public APIs and automated scanning. Reviewer notes reflect one person's experience. This is not a security certification or legal recommendation. Always evaluate tools according to your own organization's policies.
Evaluation
Scored from trust signals (evidence-eval-v1): 56 GitHub stars; contributors unknown; last commit 0d ago; license Apache-2.0.