# Design Agent Automation Workflow > Design framework-neutral agent and automation workflows before implementation. Use when choosing between Codex app automations, codex exec, Codex subagents, OpenAI Agents SDK services, LangGraph graphs, Hermes-specific workflows, full-auto execution, auto-with-escalation, or no automation yet, and when the user wants a planning/scaffolding pass that delegates stack-specific implementation to the owning plugin or official docs. - Skill: `gaelic-ghost/design-agent-automation-workflow` (Agent Skill, multi-file: 5 files) - Install (CLI): `npx skillmds@latest add gaelic-ghost/design-agent-automation-workflow` - Raw SKILL.md: https://api.skillmd.com/api/skills/gaelic-ghost/design-agent-automation-workflow/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: gaelic-ghost (https://skillmd.com/u/gaelic-ghost) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/gaelic-ghost/design-agent-automation-workflow --- # Design Agent Automation Workflow Design agent and automation workflows before implementation. This skill is a framework-neutral planning surface. It helps choose the smallest defensible automation shape, name the safety and state boundaries, decide whether the task is fit for full automation or needs exact escalation gates, and produce a scaffold another stack-owned skill or implementation pass can use. ## Inputs - Required: the automation goal or workflow idea - Useful: target repository, cadence, write surface, expected outputs, full-auto eligibility, escalation points, state needs, retry needs, observability needs, and preferred runtime constraints - Optional: known framework preference, deployment target, language stack, or existing scheduler/service ## Workflow 1. Restate the intended real-world outcome and the smallest useful first run. 2. Decide whether automation is appropriate yet. Prefer safe full automation when bounded scope, validation, rollback or draft behavior, and side-effect controls make it reasonably reliable. Use human review only for the exact decision that cannot be made safe through narrower scope, deterministic checks, retries, rollback, sandboxing, or an orchestration layer. 3. Classify the best-fit surface: - Codex app automation - `codex exec` or Codex GitHub Action - Codex subagent fan-out - OpenAI Agents SDK service - LangGraph graph - Hermes-specific workflow - full-auto execution - auto-with-escalation - no automation yet 4. Name the practical reason for the choice: schedule, isolation, state, approvals, retries, observability, deployment, or integration with an existing runtime. 5. Identify ownership: - prefer existing agent skills, plugins, scripts, and official workflow surfaces before adding new automation code, so business process knowledge has one maintained source of truth - prompt or skill-only work stays in this planning skill - Python implementation belongs in `python-skills` - web or TypeScript implementation belongs in the Build Web Apps plugin or the repo's owning JavaScript/TypeScript workflow - Swift or Apple-platform implementation belongs in Apple/Swift-owned skills - Hermes-specific work belongs in Hermes docs or a Hermes-owned skill if one exists later 6. When the request compares agent frameworks or local-first agent development, use `references/local-agent-frameworks.md` to separate the orchestration framework from the inference server, model capability, document/RAG, and integration choices. Keep the recommendation tied to a concrete workflow; do not recommend a framework merely because it is popular. 7. Produce a scaffold with the chosen surface, guardrails, validation plan, output contract, and next implementation handoff. 8. Link official docs for every framework or runtime named in the recommendation. ## Decision Rules - Prefer reusing an existing skill, plugin, repo script, or official tool when one already owns the task. Add a new agent/service only when the existing workflow cannot express the needed state, approval, scheduling, or integration boundary. - Prefer full automation when the workflow has bounded inputs, explicit write scope, deterministic or reviewable validation, durable failure reporting, bounded retries, and rollback, no-op, or draft behavior for unsafe outcomes. - Prefer auto-with-escalation when one specific decision remains unsafe or ambiguous. Name the exact escalation trigger instead of making the whole workflow human-reviewed. - Prefer Codex app automations for recurring check-ins, reminders, inbox reports, and skill-backed background tasks where Codex should stay the user interface. - Prefer `codex exec` for deterministic one-repo CLI jobs with explicit sandbox settings, structured output, CI integration, or PR-producing workflows. - Prefer Codex subagents only when the user or applicable workflow explicitly asks for parallel agent work and the jobs can be split into bounded mostly independent read, review, test, or implementation slices. - Prefer the OpenAI Agents SDK when application code should own agent orchestration, tools, handoffs, guardrails, approvals, state, tracing, or server integration. - Prefer LangGraph when the workflow is a durable graph with persisted state, explicit transitions, long-running execution, human-in-the-loop pauses, streaming, and resume behavior. - Prefer Hermes-specific workflows only when the work intentionally targets the Hermes Agent runtime, Hermes memory/skills/automation model, messaging gateways, or Hermes provider configuration. - Prefer no automation yet only when the goal, validation, owner, write scope, or rollback/escalation boundary is still unclear after trying to narrow the workflow. - Treat a framework's advertised local-model integration as an adapter capability, not proof that every local model can safely use tools, structured output, long context, or multi-step planning. Plan an explicit model capability check before granting write-capable tools. ## Output Contract Return a concise plan with these sections: - `Recommendation`: one chosen surface and one sentence explaining why - `Not Chosen`: short reasons the other plausible surfaces are not first - `State And Safety`: state, automation target, escalation gates, secrets, permissions, write scope, and rollback/no-op behavior - `Scaffold`: prompt, job shape, graph shape, or service outline - `Validation`: how the first run should prove it worked - `Handoff`: which skill, plugin, repo, or official docs should own implementation - `Sources`: official docs checked, with links Use `references/automation-plan-template.md` when the user asks for a reusable prompt, issue body, project note, or implementation brief. ## Guardrails - Do not implement framework runtime code unless the user asks for that as a second step and the owning stack/plugin guidance has been loaded. - Do not wrap OpenAI Agents SDK, LangGraph, Hermes, or Codex runtimes inside this skill. - Do not invent scheduler, queue, auth, deployment, or observability features that are not grounded in the target repo or official docs. - Do not duplicate business-process logic that already belongs to an existing agent skill, plugin, repo script, or official workflow surface. Delegate, invoke, or extend the owner instead. - Do not suggest unattended write automation unless the validation path, rollback/no-op path, and escalation boundary are explicit. - Do not use human review as a blanket fallback when a narrower automation, validation gate, rollback path, or orchestration agent would make the task reasonably safe. - Do not use Codex subagents unless the user requested them or applicable workflow guidance tells the agent to ask and receive permission first. - Keep the first version small enough to review: one workflow, one repo, or one disjoint batch before fleet-scale rollout. ## References - `agents/openai.yaml` - `references/framework-selection.md` - `references/local-agent-frameworks.md` - `references/automation-plan-template.md`