| name | control-metalayer-loop |
| description | Create and maintain a control-system metalayer for autonomous code-agent development in any repository. Use when you need explicit control primitives (setpoints, sensors, controller policy, actuators, feedback loop, stability and entropy controls), repo command/rule governance, and a scalable folder topology that lets agents operate safely and keep improving over time. |
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.mdMakefile.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.yamldocs/control/CONTROL_LOOP.mdevals/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.shscripts/control/web_e2e.shscripts/control/cli_e2e.sh.github/workflows/web-e2e.yml.github/workflows/cli-e2e.ymltests/e2e/web/* + playwright.config.tstests/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.
Broomva Stack Position
Layer 1 (Foundation) — part of the 24-skill Broomva Stack.
Related Skills
agent-consciousness (L2) — Architectural synthesis of how the control metalayer, knowledge graph, and conversation logs form a persistent consciousness for agents.knowledge-graph-memory (L2) — Bridge script that transforms Claude Code conversation logs into Obsidian-compatible session documents, creating episodic memory for the knowledge graph.drift-check (L7) — Reads control-metalayer setpoints to detect priority misalignment against actual effort.harness-engineering-playbook (L1) — Agent-first workflow that builds on control primitives for deterministic harness commands.
