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DAG-based workflow execution engine with parallel steps, retries, and conditional branching
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Based on SOC occupation classification
Onboard a new client/company onto this platform's real Agentic OS: create the company record, scan its websites/repos, auto-provision specialist agents, activate its 24x7 agency runtime, and know exactly which "OS" building blocks (memory, integrations, dashboard) already exist versus which are roadmap gaps. ADAPTED FROM: a third-party giveaway skill ("agentic-os-installer" by Gennaro Santoro / Operations Heroes) that described a generic vault + Google-suite + skill-pack installer. That skill's product (Obsidian vault, Gmail/Calendar/ Drive wiring, "skill packs") does not exist in this repo and its promotional content (Skool community link) does not belong here. This is a clean-room rewrite that keeps the useful idea — "stand up a working agency OS for a client from a short checklist" — and maps every step to the real module that already implements it in this codebase, per CLAUDE.md architecture rules.
Agile sprint planning, velocity tracking, and burndown metrics for agent-managed projects
Initiative-level portfolio management with dependency tracking and milestone coordination
AI-assisted engineering impact analysis — productivity metrics and code quality insights
Cross-harness agent patterns — standardize agent execution across different coding assistants
Temporal context graph for agent memory — track entity relationships and state changes over time
| name | workflow-engine |
| description | DAG-based workflow execution engine with parallel steps, retries, and conditional branching |
DAG-based workflow execution engine (agents/workflow_engine.py) with topological ordering,
cycle detection, and dependency resolution.
from agents.workflow_engine import WorkflowEngine, Workflow, Task
engine = WorkflowEngine()
wf = Workflow(workflow_id="deploy", name="Deploy Pipeline")
wf.add_task(Task(task_id="build", name="Build", action=lambda: "built"))
wf.add_task(Task(task_id="test", name="Test", action=lambda: "tested", depends_on=["build"]))
engine.register(wf)
results = engine.execute("deploy")
python -m pytest tests/test_workflow_engine.py -v