from sgo75
Optimize a product, resume, pitch, or profile against simulated evaluator populations using LLM scoring and counterfactual probes to find changes that improve r
Runs a pipeline that builds an entity (what you're optimizing), constructs or samples an evaluator cohort, scores the entity with LLM-based evaluators, probes counterfactual changes, and returns a semantic gradient of which edits help or hurt across segments. Outputs include average scores, segment breakdowns, top attractions and concerns, and ranked change recommendations.
Use when you want to evaluate how a resume, product pitch, profile, or other artifact will be perceived by a target audience and to discover which edits will most improve acceptance. Useful for marketers, product managers, job-seekers, and growth teams.
Agents that can run or orchestrate local scripts and interact with LLM APIs (CLI-capable assistants, developer-focused agents).
This skill has not been reviewed by our automated audit pipeline yet.
