Menu
Workflow automation, CI/CD pipelines, and task orchestration patterns across platforms like n8n, GitHub Actions, and Make.
| name | automation |
| version | 1.0.0 |
| category | development |
| description | Workflow automation, CI/CD pipelines, and task orchestration patterns across platforms like n8n, GitHub Actions, and Make. |
| type | reference |
| tags | [] |
This library contains 5 production-ready automation skills for building workflows, pipelines, and orchestration systems. Each skill covers a specific automation platform with patterns for integration, error handling, and scaling. Skills follow the Anthropic Skills format with practical examples from real-world automation scenarios.
# Browse available skills
ls skills/automation/
# Read a skill
cat skills/automation/n8n/SKILL.md
# Skills are documentation - implement patterns in your automation tools
These skills represent patterns refined across production automation systems handling thousands of workflow executions daily.
Karpathy's LLM Wiki — build and maintain a persistent, interlinked markdown knowledge base. Ingest sources, query compiled knowledge, and lint for consistency.
Operate ace-linux-1 as the continuous AI-agent control surface for overnight and continuous batches that keep GTM material moving toward client outreach.
Canonical GitHub issue planning route — issue intake, strengthened resource intelligence, repo-tracked plan artifact, adversarial review, GitHub progress posting, future-issue capture, explicit approval gate before execution, machine-dispatch readiness checks, and execution-ready delegation packaging for Claude agent teams.
Build a docs-only execution handoff bundle after a completed implementation wave — follow-up issue drafts, scoped authorization note, deploy checklist, operator note, copy/paste command bundle, and incremental commit hygiene.
Audit and dispose of session residue (orphan files, scratch dirs, sibling-repo state, locks, trash-stages) BEFORE claiming a task complete. Required gate before any agent says "all done", "task complete", or hands work back to user/orchestrator.
Plan and operate a Hermes-led control plane that routes AI provider work across workstations using quota urgency, machine readiness, GitHub issue gates, and a dispatch ledger.