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jbaham2

@jbaham2 source repo

5 published skills

  1. ▌
    Langfuse · jbaham2 bundle
    Interact with Langfuse and access its documentation. Use when needing to (1) query or modify Langfuse data programmatically via the CLI — traces, prompts, datasets, scores, sessions, and any other API resource, (2) look up Langfuse documentation, concepts, integration guides, or SDK usage, or (3) understand how any Langfuse feature works. This skill covers CLI-based API access (via npx) and multiple documentation retrieval methods.
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    Langfuse Setup · jbaham2 bundle
    Orchestrates Langfuse adoption decisions and production-readiness — the planning the official `langfuse` skill doesn't cover. Use whenever the user is deciding HOW to adopt Langfuse or whether their setup is ready: "set up Langfuse", "Langfuse Cloud or self-host", "which Langfuse region", "configure Langfuse keys/env", "is my Langfuse setup production ready", "Langfuse prod checklist", "my traces aren't showing up", or planning a Langfuse rollout. Defers instrumentation CODE to the vendored `langfuse` skill — this skill owns the decisions, order, and verification around it.
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    Langfuse Deployment · jbaham2 bundle
    Operating a self-hosted Langfuse deployment — architecture, sizing, scaling, backups, upgrades, and security. Use whenever the user is running or planning to run Langfuse on their own infrastructure: "operate / run self-hosted Langfuse", "deploy Langfuse on Kubernetes / Docker / AWS / GCP / Azure", "Langfuse sizing / resource requirements", "scale Langfuse / ingestion throughput", "back up Langfuse", "upgrade Langfuse / background migrations", "Langfuse SSO / encryption / VPC / air-gapped", or "Langfuse production deployment". Owns HOW to run self-hosted Langfuse well; the self-host-vs-Cloud and tier decision lives in the `langfuse-setup` skill, and exact configs in live docs.
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    Langfuse Evaluation · jbaham2 bundle
    Designs and runs LLM evaluation with Langfuse — the strategy and workflow layer for scoring quality, building datasets, and running experiments. Use whenever the user is evaluating LLM output quality with Langfuse: "evaluate my LLM app", "which eval method should I use", "set up LLM-as-a-judge", "create a dataset / run an experiment", "score my traces", "offline vs online evaluation", "test prompt changes before deploying", "build a regression test set", or interpreting experiment results. Owns eval STRATEGY and the datasets/experiments/scores workflow; defers judge calibration and CI/CD experiment code to the vendored `langfuse` skill, and exact SDK code to live docs.
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    Langfuse Monitoring · jbaham2 bundle
    Monitors and analyzes LLM application data already in Langfuse — dashboards, metrics, and alerting for cost, latency, quality, and volume. Use whenever the user wants to observe or report on production Langfuse data: "monitor my LLM app", "build a Langfuse dashboard", "track cost / latency / quality over time", "Langfuse metrics API", "score analytics", "set up a spend alert", "alert me when costs spike", "dashboard for production monitoring", or interpreting usage/cost/quality trends. Owns operating-the-data (dashboards/metrics/alerting); defers instrumentation to the vendored `langfuse` skill and score/evaluator design to the `langfuse-evaluation` skill.
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