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spike-bakeofflisted

Use when the user wants to try 2–4 different strategies for the same goal in parallel and pick a winner — a bakeoff, arena, tournament, gauntlet, shootout, or head-to-head where each contender is built for real in its own git worktree, adversarially judged, and none of it lands as commits. Triggers on phrases like "bakeoff", "bake off two approaches", "arena them", "run the gauntlet", "hold a tournament", "fan out different approaches", "try it three different ways", "competing implementations", "head-to-head", or "which approach wins". Varies the strategy, not the model — for running one prompt across different AI models (Claude, Antigravity, GPT), use the weave plugin instead. Ends with every contender stashed with a recovery SHA, a judged verdict, and a commit-by-commit plan to land the winner through the project's quality gates.
tony/ai-workflow-plugins · ★ 2 · AI & Automation · score 71
Install: claude install-skill tony/ai-workflow-plugins
# this skill Multi-strategy spike harness. Where the `spike-probe` skill sends one instrument down one path, a bakeoff enters 2–4 **deliberately different strategies** for the same goal, builds each one for real in its own git worktree, judges them adversarially, and hands back a verdict plus a commit-by-commit plan for the winner. Every contender **mutates its worktree freely** — real code, real gates, full bakes. What no contender ever touches is history: the kitchens are torn down after judging, and only stashes (all recoverable by SHA) and the verdict survive. This is a slash command, not a model-invocable skill: it creates worktrees, mutates them, and (in `--replay`) creates commits, so it must be user-explicit, not router-inferred. ## Core thesis One spike answers "does this path work?" — it cannot answer "which path is best?". When the approach is genuinely uncertain, arguing about strategies in the abstract is slower and less reliable than building each one small and comparing **real, judgeable evidence**: diffs, gate results, blast radius, idiom fit. A bakeoff is N probes plus a judgment. Each contender follows the probe discipline (cheapest verification, `SPIKE:` markers, stay in goal); the bakeoff adds isolation (worktrees), blindness (contenders do not see each other), and adversarial judging. **Bakeoff vs weave**: a bakeoff varies the *strategy* with one model; the weave plugin varies the *model* with one prompt. Reaching for "three models, one approach" →