Drive objective-defined experiments and record results for metric-driven optimization.
npx skills add https://github.com/jgabor/agentera --skill optimera
Please help me install this Agent Skill. Skill: optimera Source: https://github.com/jgabor/agentera/tree/main/skills/optimera Command: npx skills add https://github.com/jgabor/agentera --skill optimera
Optimera enables metric-driven optimization by driving objective-defined experiments and recording results for evidence-based decisions. It coordinates iterative evaluation harnesses, keeps decision trails, and enforces safety rails to prevent regressions. On activation, it consumes per-objective artifacts under .agentera/optimera and reads references to guide experiments.
Initiate a brainstorm to define the objective, then launch the first experiment with the optimera trigger.
High-intent search queries and answers about installing and using this skill.
Metric-driven optimization experiments require an iterative evaluation harness to drive objective-defined tests and record results for evidence-based decisions. Optimera coordinates these harnesses and maintains per-objective artifacts to track progress and prevent regressions.
Preventing regressions during iterative code optimization requires enforcing safety rails and plateau detection. Optimera applies these guardrails alongside keep or discard decisions to focus exploration and ensure measurable metrics do not degrade during experiments.
Tracking software optimization experiments requires storing artifacts in a dedicated directory with objective and experiment files. Optimera maintains per-objective artifacts including objective definitions and experiment logs to keep decision trails for evidence-based analysis.
You can use natural language triggers to start an optimization workflow. Optimera interprets triggers like optimize or run experiments to initiate the process, which begins with brainstorming to define the objective before launching the first experiment.
Defining an objective before running optimization experiments is required. Optimera consumes per-objective artifacts to guide the iterative evaluation harnesses, ensuring that experiments are driven by clearly defined measurable goals from the start.
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