Generate SDK-based Python evaluators or JSON specs from production traces.
npx skills add https://github.com/datadog-labs/agent-skills --skill eval-bootstrap
Please help me install this Agent Skill. Skill: eval-bootstrap Source: https://github.com/datadog-labs/agent-skills/tree/main/dd-llmo/eval-bootstrap Command: npx skills add https://github.com/datadog-labs/agent-skills --skill eval-bootstrap
Bootstrap evaluators from production traces to rapidly generate ready-to-use evaluation code for ML apps.
Run eval-bootstrap to generate evaluator code from production traces for your ml_app.
High-intent search queries and answers about installing and using this skill.
You can bootstrap SDK-based evaluators from production traces to generate ready-to-run Python code. This Skill targets ml_app contexts and uses optional RCA reports to seed a failure taxonomy for the generated evaluators.
Yes, you can generate LLMJudge-based code or structured JSON specs from Datadog production traces. These outputs are designed to be used directly as ready-to-run evaluators for offline LLM experiments.
Seeding an evaluation taxonomy from failure hypotheses involves targeting ml_app contexts with optional RCA reports. This process grounds the generation of BaseEvaluator prompts directly in known production failure patterns.
The bootstrap process produces both SDK-based Python evaluators and structured JSON specs. You can choose the format that best fits your offline experiment workflow and evaluation framework requirements.
RCA reports provide ground truth context to seed a failure taxonomy from LLM traces. This context guides the generation of BaseEvaluator and LLMJudge prompts tailored to your specific production failures.
You should not use trace-generated evaluators when you lack sufficient production traces or RCA reports to seed a meaningful failure taxonomy. Generating evaluators without ground truth context from your ml_app may produce ineffective prompts.
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