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Sub-skill of automation: Testing Automation.
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.
| name | automation-testing-automation |
| description | Sub-skill of automation: Testing Automation. |
| version | 1.0.0 |
| category | development |
| type | reference |
| scripts_exempt | true |
# Test workflow logic in isolation
def test_transform_step():
input_data = {"raw": "value"}
expected = {"processed": "VALUE"}
result = transform_step(input_data)
assert result == expected
# Integration tests with mocked services
def test_workflow_end_to_end(mock_api):
mock_api.return_value = {"status": "ok"}
result = run_workflow("test-workflow")
assert result.success
assert mock_api.called