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Treat any question as an optimization problem — pin the objective, find the binding constraint, weigh the real options, and make the single best call with the reasoning shown. Use this whenever the user is choosing between options, asking for a recommendation, comparing tools/products/approaches, doing a build-vs-buy or cost/quality/time tradeoff, designing or planning something, or asking "what's the best X" / "should I do A or B" — in ANY domain, from code and hardware to cooking, purchases, and trip planning, even if they don't explicitly ask for analysis. Apply it for any question where there's a genuinely best answer worth finding rather than a balanced survey of possibilities.
optimizer-mindset is an agent skill entry in ChatHome Skills, sourced from GitHub Skill Search and scored 58/100. # Optimizer Mindset Treat every question as an optimization problem: given the real objective and the real constraints, what is the *best* choice — and why is i. As an agent skill, it packages a reusable instruction set that AI coding agents such as Codex and Claude Code can load to perform this task without hand-written glue code. ChatHome Skills discovered this entry through its daily ingest of official registries and GitHub discovery queries, and re-verifies repository facts, freshness signals, and install metadata on every run. The quality score weighs official-registry presence, a working install command, license clarity, GitHub star trajectory, and repository freshness — the full formula is on the Sources page. The install command above is ready to copy. Review the last-pushed date and repository status before adding optimizer-mindset to a production agent workflow; the snapshot facts below are point-in-time values from the most recent ingest.
codex skill install codered/aiReview the upstream source before running this command.
Scan coverage: Install metadata and the indexed document body.
Automated signals from install metadata and a bounded skill body — not a full code security audit and not a recursive source or dependency review.
# Optimizer Mindset Treat every question as an optimization problem: given the real objective and the real constraints, what is the *best* choice — and why is it better than the al