Control Metalayer Loop

Use this skill to initialize or upgrade a repository into a control-loop driven agentic development system.

What To Load

• references/control-primitives.md for the control model and minimal control law.

• references/rules-and-commands.md for policy/rules and command governance.

• references/topology-growth.md for repository topology and scale path.

• references/wizard-cli.md for command usage.

Primary Entry Point

Use the Typer wizard:

python3 scripts/control_wizard.py init <repo-path> --profile governed

Profiles:

• baseline : minimal harness and command surface.

• governed : baseline + policy/commands/topology + control loop + metrics + git hooks.

• autonomous : governed + recovery/nightly controls + web and CLI E2E primitives.

Workflow

• Baseline current repo workflows and constraints.

• Initialize baseline metalayer artifacts.

• Add control primitives and governance rules.

• Audit and close gaps.

• Iterate based on run outcomes and metric drift.

Step 1: Baseline

• Identify canonical test/lint/typecheck/build commands.

• Identify high-risk actions requiring policy gates.

• Identify required observability IDs for agent runs.

Step 2: Initialize Metalayer

Run:

python3 scripts/control_wizard.py init <repo-path> --profile baseline

This creates stable operational interfaces:

• AGENTS.md , PLANS.md , METALAYER.md

• Makefile.control and scripts/control/*

• docs/control/ARCHITECTURE.md and docs/control/OBSERVABILITY.md

• CI workflow for control checks

Step 3: Add Control Primitives

Run:

python3 scripts/control_wizard.py init <repo-path> --profile governed

This adds the core control plane:

• .control/policy.yaml

• .control/commands.yaml

• .control/topology.yaml

• docs/control/CONTROL_LOOP.md

• evals/control-metrics.yaml

For a fully self-sustaining loop:

python3 scripts/control_wizard.py init <repo-path> --profile autonomous

Adds:

• scripts/control/install_hooks.sh

• .githooks/*

• scripts/control/recover.sh

• scripts/control/web_e2e.sh

• scripts/control/cli_e2e.sh

• .github/workflows/web-e2e.yml

• .github/workflows/cli-e2e.yml

• tests/e2e/web/*

• playwright.config.ts

• tests/e2e/cli/smoke.sh

• .control/state.json

• .github/workflows/control-nightly.yml

Step 4: Validate

Run:

python3 scripts/control_wizard.py audit <repo-path> python3 scripts/control_wizard.py audit <repo-path> --strict

Treat audit failures as blocking until corrected.

Step 5: Operate And Grow

• Keep command names stable (smoke , check , test , recover ).

• Keep E2E command names stable (web-e2e , cli-e2e ).

• Keep policy and command catalog synchronized with actual behavior.

• Track control metrics and adjust setpoints deliberately.

• Prune stale rules/scripts/docs to prevent entropy growth.

Adaptation Rules

• Do not overwrite existing project conventions without explicit reason.

• Prefer wrappers and policy files over ad-hoc command execution.

• Make every major behavior observable and auditable.

• Keep human escalation rules explicit and easy to trigger.