name: agent-loops displayName: Agent Loops | OpenClaw Skill description: Multi-agent workflow orchestrator. Use when the user asks to build, create, make, ship, develop, or launch any software (apps, webapps, websites, mobile apps, APIs, tools, bots, dashboards, SaaS, MVPs); fix or debug bugs; review or audit code; research topics; refactor code; or publish skills. version: 2.1.0

Agent Loops

Prebuilt multi-agent workflows that chain sequential and parallel steps, with real output passing between agents.

Description

Agent Loops orchestrates multi-step agent pipelines. Each workflow defines a sequence of steps; each step runs via claude -p with a role-specific system prompt and agent-swarm model routing. Outputs chain between steps so each agent builds on the previous one's work.

Use when the user wants to:

• Build, create, or ship anything — apps, webapps, websites, mobile apps (iPhone/Android), desktop apps, APIs, CLIs, bots, dashboards, landing pages, SaaS products, MVPs, prototypes, plugins, extensions, microservices
• Fix, debug, troubleshoot, or diagnose bugs, errors, crashes, or failing tests
• Review, audit, or inspect code for bugs, security vulnerabilities, or quality issues
• Research, investigate, compare, or write reports on any topic
• Refactor, restructure, clean up, optimize, or modernize code
• Test, review, and publish a skill to ClawHub

Installation

clawhub install agent-loops

Or clone into your skills directory:

git clone https://github.com/OpenClaw/agent-loops.git workspace/skills/agent-loops

Requires PyYAML for YAML workflows:

pip install pyyaml

Usage

Workflow selection — match the user's intent to a workflow:

Command pattern:

python3 workspace/skills/agent-loops/scripts/run_workflow.py <workflow> "<user's request>" --apply

Pass the user's natural language request as the input. The workflow handles scoping, delegation, and output chaining automatically.

Examples

Example 1: Ship a feature Scenario: You want to scope, implement, and document a new feature. Action: python3 workspace/skills/agent-loops/scripts/run_workflow.py ship_feature "Add dark mode toggle to settings" --apply Outcome: PM scopes tasks, Dev implements with tests, Editor writes docs and changelog.

Example 2: Fix a bug Scenario: A bug needs diagnosis, a fix, and regression tests. Action: python3 workspace/skills/agent-loops/scripts/run_workflow.py bug_fix "Login page crashes on empty password" --apply Outcome: Dev diagnoses root cause, Dev implements fix, Tester writes regression test, Editor documents the change.

Example 3: Parallel code review Scenario: You want a code review and security audit run simultaneously. Action: python3 workspace/skills/agent-loops/scripts/run_workflow.py code_review "Review the auth module" --apply Outcome: Reviewer and Security auditor run in parallel, then Editor synthesizes a unified summary.

Example 4: Override model Scenario: You want all steps to use a specific model. Action: python3 workspace/skills/agent-loops/scripts/run_workflow.py research_report "Compare REST vs GraphQL" --apply --model sonnet Outcome: All steps run with the specified model instead of agent-swarm routing.

Commands

python3 workspace/skills/agent-loops/scripts/run_workflow.py <workflow> "<input>"              # Dry-run a workflow
python3 workspace/skills/agent-loops/scripts/run_workflow.py <workflow> "<input>" --apply       # Run agents for real
python3 workspace/skills/agent-loops/scripts/run_workflow.py --list                             # List available workflows
python3 workspace/skills/agent-loops/scripts/run_workflow.py --list --json                      # List workflows as JSON
python3 workspace/skills/agent-loops/scripts/run_workflow.py <workflow> "<input>" --apply --json # Output results as JSON
python3 workspace/skills/agent-loops/scripts/run_workflow.py <workflow> "<input>" --model sonnet # Override model for all steps
python3 workspace/skills/agent-loops/scripts/run_workflow.py <workflow> "<input>" --timeout 900  # Set per-step timeout (seconds)
python3 workspace/skills/agent-loops/scripts/run_workflow.py <workflow> "<input>" -v             # Verbose output

• <workflow> — Workflow id: ship_feature, bug_fix, code_review, research_report, refactor, skill_publish
• --apply — Actually spawn agents (default is dry-run)
• --list — List all available workflows
• --json — Output results as JSON for programmatic use
• --model — Override agent-swarm routing with a specific model
• --timeout — Per-step timeout in seconds (default: 600)
• -v — Show full task text per step