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Omh Cto Loop

CommunityPopular
rlaope
omh-cto-loop

[omh] Hermes CTO Loop workflow: roadmap, PM, technical tradeoffs, risk, delivery, release, and follow-up operating cadence. Use when the user says: cto-loop, cto loop, cto, cto pm, pm dev qa security ops, roadmap technical tradeoffs, technical tradeoff, delivery risk.

Overview

Publisherrlaope
Repositoryoh-my-hermes
Skill nameomh-cto-loop
Stars
2.7K
Forks
194
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Omh Cto Loop 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-cto-loop .claude/skills/omh-cto-loop
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Omh Cto Loop 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 Cto Loop 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 Cto Loop 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.

Cto Loop

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

Why This Exists

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

Do Not Use When

  • 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 or use strategy-brief for a decision brief instead of starting a leadership operating loop.

Examples

Good example:

  • Prompt: cto-loop: run the PM, dev, QA, security, and ops loop for this risky billing launch.
  • Expected behavior: Prepare the CTO operating model with role responsibilities, gates, blockers, and status boundaries.
  • Why: The request needs a leadership operating loop, not just a generic plan.

Bad example:

  • Prompt: cto-loop: 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 cto-loop.
  • 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 run a leadership-style operating loop that turns signals into roadmap decisions, technical tradeoffs, delivery risk, release readiness, and explicit follow-up handoffs.

Strong routing signals: `cto-loop`, `cto loop`, `cto`, `cto pm`, `pm dev qa security ops`, `roadmap technical tradeoffs`, `technical tradeoff`, `delivery risk`, `release readiness`, `technical leadership loop`, `leadership operating loop`, `engineering leadership`, `CTO 구조`, `PM 구조`, `로드맵`, `아키텍처 트레이드오프`, `기술 리더십`, `출시 준비`

Catalog Metadata

Category: leadership Phase: operating-loop Quality tier: decision-gated Reasoning demand: heavy

Quality bar:

  • Separate product priority, architecture tradeoff, delivery risk, release risk, and follow-up owner.
  • Tie recommendations to observed signals or mark assumptions.
  • Record accepted decisions separately from draft recommendations.
  • Prepare executor handoffs only for accepted implementation follow-ups.

Required inputs:

  • operating signals
  • roadmap or release scope
  • known risks
  • decision owner

Expected outputs:

  • priority frame
  • architecture tradeoffs
  • delivery risks
  • decision note
  • follow-up handoff candidates

Artifact expectations:

  • leadership loop record or status summary when a wrapper captures decisions and follow-ups

Safety rules:

  • Do not treat a CTO loop recommendation as an accepted roadmap decision.
  • Do not imply CTO, PM, QA, Security, or Ops runtime agents exist without observed wrapper evidence.
  • Separate strategy decisions from implementation handoffs and release evidence.

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.

Frequently asked questions

What does the Omh Cto Loop AI skill do?

[omh] Hermes CTO Loop workflow: roadmap, PM, technical tradeoffs, risk, delivery, release, and follow-up operating cadence. Use when the user says: cto-loop, cto loop, cto, cto pm, pm dev qa security ops, roadmap technical tradeoffs, technical tradeoff, delivery risk.

Why use Omh Cto Loop on TypingMind?

Because you install it once and use it with any model. Omh Cto Loop 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 Cto Loop in TypingMind?

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

Which AI models can use Omh Cto Loop?

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 Cto Loop?

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

Is the Omh Cto Loop 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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