By tonone-ai
Designs and evaluates retrieval reranking pipelines with relevance scoring, learning-to-rank models, and NDCG/MRR metrics for search and RAG systems.
Design ranking pipelines — reranker selection, score fusion, cross-encoder patterns, latency trade-offs.
Build ranking evaluation — NDCG/MRR measurement, human relevance labeling, offline eval harness.
Audit ranking quality — metric trends, failure modes, dataset coverage, reranker performance.
Uses power tools
Uses Bash, Write, or Edit tools
Based on adoption, maintenance, documentation, and repository signals. Not a security audit or endorsement.
Design and build networking infrastructure — VPCs, subnets, DNS, load balancers, firewall rules. Use when asked to "set up networking", "VPC design", "configure DNS", "load balancer setup", "network architecture", or "firewall rules".
Generate onboarding documentation — what this project does, how to set up locally, where things live, key decisions, how to deploy. Written for day-one engineers who know nothing. Use when asked for "onboarding docs", "new engineer guide", "how to get started", or "developer setup".
Implement a reusable, accessible, typed component from a design spec. Use when asked to "create a component", "build a widget", "implement this design", or "reusable UI element".
Verify observability posture — audit monitoring coverage, find blind spots, prioritize gaps. Use when asked "is monitoring sufficient", "observability review", "are we covered", or "pre-launch monitoring check".
ML reconnaissance — inventory all models, pipelines, data sources, and monitoring. Use when asked "what ML do we have", "model inventory", or "ML assessment".
npx claudepluginhub tonone-ai/tonone --plugin rankBuild ranking evaluation — NDCG/MRR measurement, human relevance labeling, offline eval harness.
(forwward) Lean agent skills for building, shipping, strategy, and growth — no context bloat.
AI Agent Team Operating System for Claude Code with 155 MCP tools (incl. 40+ ecosystem research tools), 25 agent templates, 14 hooks / 12 lifecycle events. Persistent team management, structured meetings, task wall with pipeline workflows, company loop engine, real-time React dashboard, and Ecosystem Research Platform v2 (progressive 4-stage deep-review funnel: shallow auto-summary → on-demand architecture → debate-based finalist evaluation → reference/integrate marking, with project-customizable thresholds, append-only history snapshots, and Failed self-learning).
Engineering process for solo founders and teams up to 50 engineers. Agents do architecture, code review, QA, and security. You make two decisions per feature.
Complete collection of battle-tested Claude Code configs from an Anthropic hackathon winner - agents, skills, hooks, and rules evolved over 10+ months of intensive daily use
Evidence-gated AI coding workflow: scan → analyze → plan → TDD → execute → fix → verify → review, powered by Codebase Memory MCP >= 0.9.0 with optional Serena LSP intelligence. Includes blast-radius planning, test/cycle gates, independent review, and Windows Git Bash hook auto-resolution.