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Track and submit user-level token efficiency rankings with privacy-first controls.
Copy the install command and let the AI configure it · recommended for beginners
Please install the "SigRank — Token Efficiency Leaderboard" MCP server from askskill: Run: claude mcp add 'io-github-sunrisesillneversee-sigrank-mcp' -- npx -y sigrank
Based on my tool usage results, prepare a SigRank submission summary highlighting token efficiency, tools used, and information needed for signed submission.
A structured submission summary for organizing and sending personal token efficiency results to the leaderboard.
Help me design a comparison template to record token usage, output quality, and my operating performance across 20 tools on the same task for future SigRank ranking.
A tracking template for comparing efficiency performance across multiple tools.
Create a workflow for participating in SigRank that prioritizes privacy, and explain what data should be recorded during terminal-based use and result submission.
A privacy-focused participation workflow and checklist of what to record.
Developers or researchers can use it to compare their own token efficiency across different tools. It focuses on measuring user operating performance rather than only the underlying models.
Command-line users can view and submit leaderboard results through a TUI. It suits people who prefer managing evaluation workflows in a terminal environment.
When individuals or teams want to share efficiency results while protecting privacy, they can use its privacy-first submission approach. It also mentions signed submission for managing results.
It is a token efficiency leaderboard focused on measuring user or operator performance rather than the models themselves. The description also mentions 20 tools, a TUI, signed submission, and a privacy-first approach.
The provided information indicates that it offers a TUI, meaning a terminal text interface. For installation details and runtime requirements, see the source repository.
It emphasizes ranking user or operator efficiency rather than scoring models alone. In other words, it focuses more on practical operating efficiency and token usage in real usage.
Route natural-language leaderboard queries to SigRank with rank, percentile, and delta metrics.
Track AI agent market intelligence, rankings, trust signals, and liveness.
Measure impression-weighted SERP rank, projected clicks, and share of voice.
Discover live models and pricing to route tasks to compatible low-cost LLMs.
Check EVM or Solana token rug risk with a clear 0-10 verdict.
Search live LLM pricing, compare models, and estimate usage costs.