loop-engineering-skills

Use when running or extending the fmhub formal-verification benchmarks — driving LLMs (codex / DeepSeek V4 Flash & Pro / GLM) to add ACSL/Dafny/Verus specs that a verifier proves, and lifting weaker/cheaper models toward codex quality via loop engineering. Covers the harness toolkit, the proven workflow levers, the Flash+Pro cascade, model/endpoint gotchas, and how to add a solver. Trigger on tasks about FMBench, spec generation, multi-turn verifier loops, hybrid model cascades, or improving a weak model's pass rate on these benchmarks.

Use when integrating or benchmarking an LLM API (especially reasoning models like GLM-5.2, DeepSeek V4, or any new provider/endpoint), or when a model "looks weak" and you need to rule out integration artifacts before concluding. Covers anti-hang call layers, reasoning-model handling (max_tokens, empty content), endpoint comparison, prefix caching, cheat-proof evaluation (faithfulness), and variance-aware measurement. Generalizable beyond fmhub.

Use when shifting from manual prompting of coding agents to designing automated loops. Apply the 4-condition test, 5 building blocks, and MVP construction order for repetitive machine-verifiable tasks like CI triage, dependency bumps, or lint fixes. Trigger on automation, loop design, or agent orchestration requests.