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Control Metalayer Loop

  • 3 installs
  • 1 repo stars
  • Updated June 28, 2026
  • broomva/control-metalayer

Control-metalayer-loop is a Claude skill that initializes a repository into a control-loop driven agentic development system with setpoints, sensors, a controller policy, actuators, and a feedback loop.

About

Control-metalayer-loop initializes or upgrades a repository into a control-loop driven agentic development system. A Python Typer wizard installs control primitives such as setpoints, sensors, a controller policy, actuators, and a feedback loop, along with command and rule governance and a scalable folder topology. Developers use it to let coding agents operate safely and keep improving over time via baseline, governed, and autonomous profiles. It also ships an audit command to detect and close control gaps.

  • Initializes a repo into a control-loop driven agentic development system
  • Ships baseline, governed, and autonomous profiles via a Typer CLI wizard
  • Adds policy, commands, topology, git hooks, and control metrics

Control Metalayer Loop by the numbers

  • 3 all-time installs (skills.sh)
  • Ranked #13,677 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
  • Data as of Jul 28, 2026 (Skillselion catalog sync)
At a glance

control-metalayer-loop capabilities & compatibility

Capabilities
control loop init · policy governance · command governance · control audit
Works with
github
Use cases
orchestration
Pricing
Free
From the docs

What control-metalayer-loop says it does

Create and maintain a control-system metalayer for autonomous code-agent development in any repository.
SKILL.md
Use this skill to initialize or upgrade a repository into a control-loop driven agentic development system.
SKILL.md
npx skills add https://github.com/broomva/control-metalayer --skill control-metalayer-loop

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Listed on Skillselion
Installs3
repo stars1
Last updatedJune 28, 2026
Repositorybroomva/control-metalayer

What it does

Initialize a repo into a control-loop agentic system with setpoints, sensors, policy, actuators, and command governance.

Who is it for?

Developers who want coding agents to operate safely under explicit control primitives and governance.

Skip if: Small repos that do not need a formal control loop or policy gates.

When should I use this skill?

You need explicit control primitives, repo command and rule governance, and a scalable folder topology for safe agent operation.

What you get

A repository governed by a control loop with policy gates, stable command names, and auditable behavior.

  • .control/policy.yaml
  • commands.yaml and topology.yaml
  • git hooks and CI control workflows

By the numbers

  • 3 profiles (baseline, governed, autonomous)
  • 5-step workflow (baseline to operate-and-grow)

Files

SKILL.mdMarkdownGitHub ↗

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

1. Baseline current repo workflows and constraints. 2. Initialize baseline metalayer artifacts. 3. Add control primitives and governance rules. 4. Audit and close gaps. 5. 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.

Related Skills

  • agent-consciousness — Architectural synthesis of how the control metalayer, knowledge graph, and conversation logs form a persistent consciousness for agents.
  • knowledge-graph-memory — Bridge script that transforms Claude Code conversation logs into Obsidian-compatible session documents, creating episodic memory for the knowledge graph.

Related skills

FAQ

What profiles does it support?

baseline (minimal harness), governed (adds policy, commands, topology, control loop, metrics, git hooks), and autonomous (adds recovery, nightly controls, and web/CLI E2E primitives).

Which command names stay stable?

It keeps command names like smoke, check, test, and recover stable, plus E2E names web-e2e and cli-e2e.

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