run

name	run
description	Use the local Autodialectics MCP server and CLI to compile tasks, execute anti-slop runs, inspect artifacts, replay runs, benchmark policies, and evolve champions in this repository.

Autodialectics

Use this skill when the user wants to work through the Autodialectics harness instead of asking the model to freestyle a task.

Repository facts:

• Repo root: the repository root for the active checkout
• Primary config: autodialectics.yaml
• MCP entrypoint: uv run autodialectics-mcp
• CLI entrypoint: uv run autodialectics
• Python fallback: .venv/bin/python -m autodialectics.cli.main

Preferred MCP workflow:

1. health
2. init_runtime
3. compile_task
4. run_task
5. inspect_run or read_artifact
6. benchmark, evolve_policy, promote_policy, rollback_policy, or replay_run

CLI fallback command forms:

uv run autodialectics init
uv run autodialectics compile examples/code_fix/task.json
uv run autodialectics run examples/code_fix/task.json
uv run autodialectics benchmark
uv run autodialectics inspect <run_id>
uv run autodialectics replay <run_id>
uv run autodialectics evolve
uv run autodialectics promote <policy_id>
uv run autodialectics rollback

Working rules:

• Prefer the MCP server when it is available through the plugin.
• Run CLI commands from the repo root when the MCP server is unavailable.
• Prefer uv run autodialectics ... over ad hoc module invocation.
• Use --config autodialectics.yaml if config resolution looks ambiguous.
• When a run finishes, inspect the generated artifacts/run_* directory and summarize the gate decision, score, slop composite, and unresolved risks.
• For benchmark work, report aggregate results and call out canary failures explicitly.
• For policy work, do not promote a challenger unless the benchmark output supports it.

Expected examples:

• Compile before a risky run if the task contract seems underspecified.
• Use inspect after run or benchmark instead of guessing from stdout.
• Use replay when the user wants the same task rerun under another policy.