feat(ce-optimize): Auto-research loop for tuning system prompts / vector clustering / evaluating different code solution / etc (#446)

Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

plugins/compound-engineering/skills/ce-optimize/README.md

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+# `ce-optimize`
+
+Run iterative optimization loops for problems where you can try multiple variants and score them with the same measurement setup.
+
+## When To Use It
+
+Use `/ce-optimize` when:
+
+- The right change is not obvious up front
+- You can generate several plausible variants
+- You have a repeatable measurement harness
+- "Better" can be expressed as a hard metric or an LLM-as-judge evaluation
+
+Good fits:
+
+- Tuning memory, timeout, concurrency, or batch-size settings where you can measure crashes, latency, throughput, or error rate
+- Improving clustering, ranking, search, or recommendation quality where hard metrics alone can be gamed
+- Optimizing prompts where both output quality and token cost matter
+
+Usually not a good fit:
+
+- One-shot bug fixes with an obvious root cause
+- Changes without a repeatable measurement harness
+- Problems where "better" cannot be measured or judged consistently
+
+## Quick Start
+
+- Start with [`references/example-hard-spec.yaml`](./references/example-hard-spec.yaml) for objective targets
+- Start with [`references/example-judge-spec.yaml`](./references/example-judge-spec.yaml) when semantics matter and you need LLM-as-judge
+- Keep the first run serial, small, and cheap until the harness is trustworthy
+- Avoid introducing new dependencies until the baseline and evaluation loop are stable
+
+## Docs
+
+- [`SKILL.md`](./SKILL.md): full orchestration workflow and runtime rules
+- [`references/usage-guide.md`](./references/usage-guide.md): example prompts and practical "when/how to use this skill" guidance
+- [`references/optimize-spec-schema.yaml`](./references/optimize-spec-schema.yaml): optimization spec schema
+- [`references/experiment-log-schema.yaml`](./references/experiment-log-schema.yaml): experiment log schema