tonone-ai

Summary

Engineering + Product Teams — 23 Claude Code agents covering engineering and product functions

457 plugins · page 4 of 12

#PluginStars

121drift-monitorDesign a drift monitoring system for a production ML model50122drift-reconAudit existing ML monitoring50123echoUser researcher — interviews, personas, Jobs-to-Be-Done, and customer feedback synthesis50124echo-feedbackFeedback synthesis — cluster support tickets, NPS verbatims, app store reviews, and churn surveys by theme, separate signal from noise, and produce an actionable insight report. Use when asked to "synthesize this feedback", "analyze support tickets", "what are users complaining about", "NPS analysis", "churn feedback synthesis", or "what's the feedback telling us".50125echo-interviewRun a user interview — produce an interview guide and synthesize the output into an actionable insight report. Use when asked to "run a user interview", "synthesize these interview notes", "what do users actually want", "build a persona from this feedback", "find the JTBD in these transcripts", or "analyze this interview data".50126echo-jobsJobs-to-Be-Done analysis — given a product, user descriptions, transcripts, or tickets, produce a JTBD job map with switching forces analysis and opportunity ranking. Use when asked to "find the JTBD", "what jobs are users hiring us for", "job mapping", "what are users really trying to do", "JTBD framework", or "why are users switching".50127echo-reconUser research reconnaissance — survey existing personas, research docs, interview notes, and feedback artifacts to establish what is already known about users. Use when asked to "what research exists", "review existing personas", "what do we know about our users", or before starting new research or synthesis work.50128echo-segmentUser segmentation and persona creation from mixed data sources — analytics, CRM, support tickets, reviews, or any combination. Use when asked to "build personas", "who are our users", "segment our users", "create user profiles", "define user archetypes", or "who is the target user".50129edgeInfrastructure Specialist Team — Edge: Edge computing and CDN — global distribution, cache strategy, edge functions, latency optimization50130edge-cdnEdge skill: edge-cdn50131edge-reconEdge skill: edge-recon50132edge-routeEdge skill: edge-route50133embedAI Operations Team — Embed: Embedding model selection, vector pipeline design, similarity search, and production index management.50134embed-designDesign embedding pipelines — model selection, batching, normalization, index refresh strategy.50135embed-reconAudit embedding infrastructure — model drift, index freshness, query latency, coverage gaps.50136embed-searchOptimize similarity search — ANN index tuning, hybrid search, reranking, query expansion.50137engineering-teamInstall all 15 Engineering Team agents at once50138evalExperiment Design Engineer — Experiment design — A/B testing, statistical power, experiment tracking, causal inference50139eval-analyzeAnalyze A/B test results50140eval-designDesign an A/B test50141eval-harnessDesign eval harnesses — task schemas, metrics, dataset versioning, eval-as-code patterns.50142eval-reconAudit existing experimentation infrastructure50143eval-regressBuild automated regression suites — golden sets, threshold alerting, CI integration for model changes.50144evalsAI Operations Team — Evals: Eval harness design, benchmark suites, automated regression, human eval orchestration.50145featFeature Engineer — Feature engineering — transformations, encodings, feature stores, pipeline design50146feat-engineerDesign a feature engineering pipeline for a ML problem50147feat-reconAudit feature engineering code for leakage and quality issues50148feat-storeDesign or audit a feature store50149finopInfrastructure Specialist Team — Finop: Cloud cost optimization — FinOps practices, rightsizing, reservation strategy, cost attribution50150finop-auditFinop skill: finop-audit50151finop-reconFinop skill: finop-recon50152finop-reserveFinop skill: finop-reserve50153fitModel Training Engineer — Model training — algorithm selection, hyperparameter tuning, training infrastructure50154fit-reconAudit existing model training code50155fit-trainDesign a model training pipeline50156fit-tuneDesign a hyperparameter tuning strategy50157fluxData engineer — databases, migrations, pipelines, data modeling50158flux-healthData quality and pipeline health check — freshness, schema drift, null rates, orphaned records, pipeline status. Use when asked about "data quality check", "pipeline health", "is our data fresh", or "schema drift".50159flux-migrateBuild zero-downtime database migrations — forward SQL, rollback SQL, deployment sequence. Use when asked to "write migration", "schema change", "add column", "rename table", "drop column", or "migrate safely".50160flux-pipelineBuild a data pipeline — ETL/ELT with extraction, transformation, loading, error handling, and scheduling. Use when asked to "build ETL", "data pipeline", "move data from X to Y", or "sync data".50