oktkLLM Token Optimizer - Reduce AI API costs by 60-90%. Compresses CLI outputs (git, docker, kubectl) before sending to GPT-4/Claude. AI auto-learning included. By Buba Draugelis 🇱🇹
Install via ClawdBot CLI:
clawdbot install satnamra/oktkGrade Fair — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
Contains telemetry, tracking, or analytics calls not mentioned in documentation
analytics.track(Calls external URL not in known-safe list
https://github.com/satnamra/openclaw-workspace/tree/main/skills/oktkAI Analysis
The skill's primary function is local CLI output compression to reduce token usage, which aligns with its stated purpose. The signal for an external URL points to the skill's own public GitHub repository homepage, which is documented and not a data exfiltration endpoint. The telemetry signal appears to be a false positive from a generic pattern match on common method names like 'analytics.track()' with no evidence of actual data transmission.
Audited Apr 18, 2026 · audit v1.0
Generated Mar 22, 2026
Teams using Git, Docker, and Kubernetes in daily workflows can integrate oktk to compress verbose CLI outputs before sending them to AI assistants for debugging or code reviews. This reduces token usage by 60-90%, cutting costs on AI API calls while maintaining essential information for troubleshooting and collaboration.
DevOps engineers managing infrastructure with commands like kubectl get pods or docker logs can use oktk to filter outputs, focusing on critical statuses and errors. This optimizes AI-assisted monitoring and incident response, saving tokens and costs without compromising operational insights.
Quality assurance teams running automated tests with npm test or grep can leverage oktk to compress results, highlighting only pass/fail statuses and key matches. This streamlines AI analysis of test reports, reducing token consumption and accelerating feedback loops in CI/CD pipelines.
Researchers using CLI tools for data processing or system management can integrate oktk to compress outputs like curl responses or ls listings before AI analysis. This cuts API costs for AI-assisted data interpretation, making resource-intensive computations more affordable in academic settings.
Independent developers handling multiple client projects can use oktk to optimize AI usage across varied commands, from git diff to docker ps, reducing overhead costs. The auto-learning feature adapts to custom workflows, ensuring efficient token management for cost-sensitive solo operations.
Offer a free tier with basic compression for common commands like git status and docker ps, while premium plans include advanced features such as AI auto-learning for custom commands and detailed analytics. Revenue comes from monthly subscriptions, targeting small teams and enterprises seeking scalable cost savings.
Sell annual licenses to large organizations for integration into internal AI tools and development environments, with custom filters for proprietary CLI tools and dedicated support. This model focuses on high-volume token reduction, offering bulk discounts and tailored solutions for corporate workflows.
Provide oktk as a cloud API where users send CLI outputs for compression, paying per token saved or on a usage-based tier. This allows seamless integration into existing AI platforms and automation scripts, generating revenue from microtransactions and high-usage clients in tech-heavy industries.
💬 Integration Tip
Source the provided aliases file in your shell to automatically filter commands with short aliases like gst for git status, or manually pipe outputs using oktk <command> for quick setup without modifying existing workflows.
Scored May 17, 2026
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