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Control Metalayer Loop

  • 10 installs
  • 1 repo stars
  • Updated June 28, 2026
  • broomva/agent-control-metalayer-skill

control-metalayer-loop is a skill that initializes a repository into a control-loop driven system with control primitives, policy governance, and CI harnesses for autonomous code agents.

About

A skill that turns a repository into a control-system metalayer for autonomous code agents. A developer uses it to install control primitives, policy and command governance, git hooks, and CI harnesses so agents can operate safely and keep improving. It runs a Typer CLI wizard that scaffolds artifacts in baseline, governed, or autonomous profiles and audits for gaps.

  • Initializes a repo into a control-loop driven agentic development system
  • Typer wizard with baseline, governed, and autonomous profiles
  • Generates control policy, commands, topology, git hooks, and E2E harness

Control Metalayer Loop by the numbers

  • 10 all-time installs (skills.sh)
  • Ranked #11,937 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
orchestration
Works with
github · playwright
Use cases
ci cd · orchestration · testing
From the docs

What control-metalayer-loop says it does

Use this skill to initialize or upgrade a repository into a control-loop driven agentic development system.
SKILL.md
python3 scripts/control_wizard.py init <repo-path> --profile governed
SKILL.md
Keep command names stable (`smoke`, `check`, `test`, `recover`).
SKILL.md
npx skills add https://github.com/broomva/agent-control-metalayer-skill --skill control-metalayer-loop

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Listed on Skillselion
Installs10
repo stars1
Last updatedJune 28, 2026
Repositorybroomva/agent-control-metalayer-skill

What it does

Initialize or upgrade a repo with control primitives, policy governance, and harness so code agents operate safely.

Who is it for?

Bootstrapping repo-level control primitives, policy gates, and CI harnesses for code agents

When should I use this skill?

You need explicit control primitives, command/rule governance, and a scalable agent-safe topology

What you get

A repo scaffolded with control policy, commands, topology, git hooks, and control-loop CI

  • AGENTS.md, PLANS.md, METALAYER.md, .control policy/commands/topology, git hooks, CI workflows

By the numbers

  • 3 profiles (baseline, governed, autonomous)
  • 5-step workflow

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 the wizard support?

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

How do you initialize it?

Run python3 scripts/control_wizard.py init <repo-path> --profile governed, then audit with the same script.

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