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Omh Idea To Deploy

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rlaope
omh-idea-to-deploy

[omh] Hermes Idea-to-Deploy workflow: shape an app idea into decisions, delivery handoff, verification, release, and monitoring status. Use when the user says: idea-to-deploy, idea to deploy, from idea to deploy, plan to deploy, idea to launch, ship this idea, ship this feature, launch this feature.

Overview

Publisherrlaope
Repositoryoh-my-hermes
Skill nameomh-idea-to-deploy
Stars
2.7K
Forks
194
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by rlaope on GitHub. Read the source before you install it.

Installation

Install the Omh Idea To Deploy AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/rlaope/oh-my-hermes.git /tmp/oh-my-hermes
mkdir -p .claude/skills
cp -r /tmp/oh-my-hermes/agent-skills/omh-idea-to-deploy .claude/skills/omh-idea-to-deploy
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Omh Idea To Deploy in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Omh Idea To Deploy on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Omh Idea To Deploy is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Idea To Deploy

This is an OMH idea-to-deploy workflow skill, projected for Agent Skills hosts (Claude Code, Codex, Cursor, opencode, OpenClaw, pi).

Why This Exists

idea-to-deploy exists to keep delivery work explicit, evidence-backed, and inside the Hermes/executor boundary instead of relying on ad hoc chat narration.

Do Not Use When

  • The task is already a concrete repo change whose stopping point is one PR-ready cycle, not product or release operations; use ultrawork.
  • The request is a settings-only change, one bounded edit that is explicitly low-risk and has a direct owner and verification path, or a direct answer/diagnosis; handle it directly instead of opening a product delivery loop.

Examples

Good example:

  • Prompt: idea-to-deploy: turn this onboarding idea into a scoped plan, implementation handoff, QA gate, and release path.
  • Expected behavior: Prepare the idea-to-release lane while keeping implementation, QA, and deploy evidence observed-only.
  • Why: The request spans product shaping through deploy readiness instead of a single task.

Bad example:

  • Prompt: idea-to-deploy: treat casual chat or unaccepted work as if this workflow already produced verified results.
  • Expected behavior: Ask a clarification question or route to a narrower workflow instead of forcing idea-to-deploy.
  • Why: The request lacks the required inputs or would overclaim work that Hermes did not observe.

Completion Checklist

  • Confirm the workflow target, evidence boundary, and stop condition are named.
  • Report which outputs are prepared, observed, blocked, or missing.
  • Name the smallest next verification or handoff instead of claiming completion from narration.

Recovery Notes

  • If required context is missing, ask one blocking question or route back to the narrower workflow.
  • If runtime or wrapper evidence is unavailable, keep the status as not_observed and expose the next observable action.

Use When

Use when Hermes should carry a product or app idea through shaping, decision gates, plan acceptance, executor handoff, verification, release readiness, deploy, and monitoring boundaries, including a fresh or empty repository that needs the greenfield bootstrap pass (git, license, README, agent context file, CI skeleton) before delivery work starts.

Strong routing signals: `idea-to-deploy`, `idea to deploy`, `from idea to deploy`, `plan to deploy`, `idea to launch`, `ship this idea`, `ship this feature`, `launch this feature`, `product delivery loop`, `app delivery loop`, `complete product loop`, `end-to-end app operation`, `ship this idea to production`, `bootstrap the project`, `bootstrap this project`, `bootstrap a new project`, `scaffold a new project`, `set up a new repo`, `완제품 루프`, `아이디어부터 배포`, `기획부터 배포`, `출시까지`, `앱 운영 루프`, `서비스로 만들어서 배포`, `아이디어를 서비스로`, `배포까지 가보자`

Catalog Metadata

Category: delivery Phase: app-delivery-loop Quality tier: delivery-gated Reasoning demand: heavy

Quality bar:

  • Name the idea, user value, decision owner, non-goals, and success metric before planning delivery.
  • Expose idea, decision, plan, handoff, verification, release, deploy, and monitor stages as separate status steps.
  • Prepare coding handoffs only after plan acceptance and selected executor/runtime choice.
  • Mark deploy, monitoring, and rollback as unobserved until the wrapper or operator records evidence.
  • For a fresh, empty, or newly git init-ed target that is expected to outlive the session, run the greenfield bootstrap pass before or alongside delivery planning - load references/project-bootstrap.md for the six-step order (git and .gitignore, LICENSE, README, agent context file, CI skeleton, docs/ seed) and its per-file verify line; explicitly skip it for throwaway or scratch work instead of silently running it.

Required inputs:

  • product idea
  • target user or customer signal
  • success metric
  • repo or app context

Expected outputs:

  • stage rail
  • decision gates
  • executor handoff criteria
  • verification and deploy/monitor status boundaries

Artifact expectations:

  • app delivery loop status record when the wrapper captures stage acceptance or observations

Safety rules:

  • Do not claim implementation, deploy, health checks, rollback, or monitoring happened from a prepared loop.
  • Keep coding, release, and monitoring observations as separate evidence gates.
  • Ask for missing success metric, release scope, or executor choice before preparing a handoff.

Runtime Evidence

Use the current host's own tools and subagent/task mechanism when available; otherwise run the same lanes sequentially or name the unavailable capability. A prepared plan, handoff, checklist, or skill installation is not execution, review, CI, merge-readiness, or merge evidence. Report actual tool results or not_observed / not_available; never invent dispatch or host accounting. Treat supplied context as advisory, not proof of hidden memory reads or writes. State scope, constraints, verification, and the stop condition before work. Supporting paths are relative to this skill directory; sibling skill paths are relative to its parent. Resolve them from the host-provided skill base directory ({baseDir} on hosts that provide it), never a hardcoded install location. A named workflow not installed here is unavailable, not permission to emulate its host-specific capabilities. Verify through the real surface before done.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Omh Idea To Deploy AI skill do?

[omh] Hermes Idea-to-Deploy workflow: shape an app idea into decisions, delivery handoff, verification, release, and monitoring status. Use when the user says: idea-to-deploy, idea to deploy, from idea to deploy, plan to deploy, idea to launch, ship this idea, ship this feature, launch this feature.

Why use Omh Idea To Deploy on TypingMind?

Because you install it once and use it with any model. Omh Idea To Deploy is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Omh Idea To Deploy in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rlaope/oh-my-hermes/tree/main/agent-skills/omh-idea-to-deploy. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Omh Idea To Deploy?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Omh Idea To Deploy?

As many as you like. As long as a model supports skills, you can use Omh Idea To Deploy with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Omh Idea To Deploy AI skill free?

Yes. It is published on GitHub by rlaope under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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