| name | benchmark |
| description | Run benchmark suites and manage policy evolution — create challengers, compare against champions, promote or rollback policies. |
Autodialectics Benchmark & Evolve
Use this skill to benchmark policies and drive champion/challenger evolution.
Prerequisites
autodialectics-mcp must be on PATH (pip install autodialectics).
MCP Workflow
Benchmarking
benchmark(suite_dir?, policy_id?) — run the benchmark suite against a policy. Returns case-by-case results with scores and decisions.
Policy Evolution
evolve_policy(use_gepa?) — analyze recent benchmark reports and create a challenger policy. Set use_gepa: false to skip the GEPA optimizer (simpler heuristic fallback).
promote_policy(policy_id) — promote a challenger to champion if comparison rules allow.
rollback_policy() — revert to the previous champion if the current one regresses.
CLI Fallback
autodialectics benchmark
autodialectics evolve
autodialectics promote <policy_id>
autodialectics rollback
Typical Evolution Cycle
benchmark → evolve_policy → benchmark (with challenger) → compare → promote or rollback
- Run benchmarks with the current champion to establish a baseline.
- Evolve a challenger from the benchmark reports.
- Run the same benchmarks with the challenger's policy ID.
- Compare results. Promote if the challenger wins; rollback if it regresses.
Guidance
- Never claim a policy is better without benchmark evidence from the same suite.
- When reporting benchmark results, include: total cases, pass/fail/revise counts, mean overall score, mean slop composite.
- If
evolve_policy returns no_reports, run benchmarks first to generate data.
- Promotion can be denied by comparison rules — check the response status.
Arguments
If the user passes a suite directory after /autodialectics:benchmark, use it as the benchmark suite path.