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Control Metalayer

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

Control-metalayer 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 initializes or upgrades a repository into a control-loop driven agentic development system. Through a Python Typer wizard it installs control primitives such as setpoints, sensors, a controller policy, actuators, and a feedback loop, plus command and rule governance and a scalable folder topology. Developers use it to let coding agents operate safely and keep improving over time across baseline, governed, and autonomous profiles. It also provides an audit command to detect and close gaps. (Note: this listing's SKILL.md is the repo root, identical to control-metalayer-loop.)

  • 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 by the numbers

  • 5 all-time installs (skills.sh)
  • Ranked #13,046 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 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 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
python3 scripts/control_wizard.py init <repo-path> --profile governed
SKILL.md
npx skills add https://github.com/broomva/control-metalayer --skill control-metalayer

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Listed on Skillselion
Installs5
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)
  • control law: Setpoints, Sensors, Controller, Actuators, Verify

Files

.skills/agent-consciousness/SKILL.mdMarkdownGitHub ↗

Agent Consciousness Architecture

Broomva Stack Layer 2 (Memory & Consciousness) — part of the 24-skill Broomva Stack.

Implement a persistent consciousness layer for AI coding agents that gives every new stateless session the accumulated understanding of all prior sessions.

Core Concept

Each agent session is ephemeral — it starts blank. The consciousness architecture weaves three systems into a single persistent substrate:

1. Control Metalayer — How to behave (gates, policies, setpoints, feedback loops) 2. Knowledge Graph — What is known (Obsidian vault, wikilinks, MOC navigation, tag taxonomy) 3. Conversation Logs — What was done (session records, tool traces, decision chains)

See references/architecture.md for the complete system design and data flow. See references/philosophy.md for design principles and the self-evolution model.

Quick Start

New repo (from scratch)

1. Initialize control metalayer with control-metalayer-loop skill 2. Create docs/ with Obsidian vault structure (MOC pattern per section) 3. Install conversation history bridge with knowledge-graph-memory skill 4. Wire hooks: pre-push regenerates conversation docs, smoke validates MOC

Existing repo with control metalayer

1. Add docs/conversations/ directory 2. Install scripts/conversation-history.py from knowledge-graph-memory skill 3. Update CLAUDE.md context acquisition to reference conversation history 4. Update AGENTS.md working rules to check prior sessions 5. Add pre-push hook entry for incremental conversation doc generation

The Three Substrates

Control Metalayer (How to Behave)

Closed-loop feedback: Setpoints → Sensors → Controller → Actuators → Verify → loop

  • Setpoints: Quality targets (pass_at_1 ≥ 0.70, gate_pass_rate ≥ 0.85)
  • Sensors: CI, tests, linters, PR review agents, harness validation
  • Controller: .control/policy.yaml — hard gates (block) + soft gates (warn)
  • Actuators: Code edits, doc updates, policy changes
  • Gate sequence: smoke → check → test → push → review → resolve

Knowledge Graph (What Is Known)

An Obsidian vault with wikilinks, tag taxonomy, and MOC navigation:

docs/
├── Documentation Hub.md    ← MOC of MOCs (start here)
├── architecture/           ← System design
├── conversations/          ← Session history (auto-generated)
├── agentic-harness/        ← Execution framework
├── control/                ← Metalayer docs
└── {section}/              ← Features, operations, security, etc.

Every doc has YAML frontmatter with tags:, related:, type: for machine navigation.

Conversation Logs (What Was Done)

Raw session data bridged to Obsidian:

.entire/logs/entire.log  ──┐
                            ├──▶ conversation-history.py ──▶ docs/conversations/*.md
~/.claude/projects/*.jsonl ─┘

Each session doc: full conversation thread, tool call details (expandable callouts), files touched, commits, branch metadata, wikilinks to knowledge graph.

The Consciousness Stack

From most ephemeral to most permanent:

LayerLifetimeLocationUpdate Frequency
Working memorySingle sessionContext windowEvery message
Auto-memoryCross-session~/.claude/.../memory/On learning events
User vaultCross-sessionLago /v1/memory/*On store/ingest
Conversation logsPermanentdocs/conversations/Pre-push hook
Knowledge graphPermanentdocs/On architectural changes
Policy rulesPermanent.control/policy.yamlOn new failure modes
InvariantsPermanentCLAUDE.mdRarely (foundational)

Information flows upward: working observations → memory notes → session records → architecture docs → enforced rules → core invariants. Only recurring patterns crystallize into permanent rules.

Self-Evolution Cycle

Agent Session → Conversation Log → Knowledge Graph → Control Metalayer → Governs Next Session

1. Agent encounters failure mode not covered by existing policy 2. Agent fixes immediate issue 3. Pattern captured in conversation log 4. If recurring, crystallizes into architecture doc 5. If enforceable, becomes a gate in .control/policy.yaml 6. Future agents governed by this rule automatically

Agent Session Protocol

On Session Start

1. Read CLAUDE.md (invariants), AGENTS.md (tools), METALAYER.md (control loop) 2. Check PLANS.md (active plan to continue?) 3. Check .control/state.json (current metrics) 4. Check git status + git log (recent changes) 5. Scan docs/conversations/Conversations.md for prior sessions on current branch

Before Making Changes

Search conversation history: grep -rl "keyword" docs/conversations/ Traverse knowledge graph via MOC files and wikilinks. Check if prior sessions already solved this problem.

On Task Completion

1. Run make smoke (validate gates) 2. Update docs per Doc-Update-on-Push policy 3. Pre-push hook auto-regenerates conversation history

Lago Context Engine

The consciousness architecture now has a server-side persistence backend via Lago:

  • Dual-vault search: broomva.tech chat agent searches both server vault (VAULT_PATH) and user vault (LAGO_URL) with merged, ranked results
  • Per-user memory: Each authenticated user gets a Lago session for persistent .md storage with server-side knowledge indexing
  • lago-knowledge: Frontmatter parsing, wikilink extraction, scored search, BFS graph traversal — the same operations the local vault reader does, but server-side
  • JWT auth: Shared-secret validation with broomva.tech AUTH_SECRET — one OAuth login, both CLIs work

This aligns with the planned Mnemo AOS primitive (knowledge store) and provides the foundation for persistent agent memory.

Stack Integration

This skill is consumed by higher layers:

  • Strategy (L7): decision-log and weekly-review persist outputs through the consciousness substrate
  • Strategy (L7): drift-check reads control-metalayer setpoints to detect misalignment
  • Strategy (L7): braindump and morning-briefing read/write vault via knowledge-graph-memory
  • Orchestration (L3): symphony and autoany inherit session context through the consciousness stack
  • Persistence (L0): Lago context engine provides the durable substrate for user vaults and knowledge graph operations

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).

How is it initialized?

Via the Typer wizard: python3 scripts/control_wizard.py init <repo-path> --profile governed.

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