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Omh Plan

CommunityPopular
rlaope
omh-plan

[omh] Software feature or bugfix not yet planned: structured planning before execution. Use when the user says: plan, implementation plan, make a plan, write a plan, write the plan, task breakdown, safe feature, safely add a feature.

Overview

Publisherrlaope
Repositoryoh-my-hermes
Skill nameomh-plan
Stars
3.6K
Forks
269
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 Plan 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.
    https://github.com/rlaope/oh-my-hermes/tree/main/agent-skills/omh-plan
  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-plan .claude/skills/omh-plan
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Omh Plan 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 Plan 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 Plan 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.

Plan

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

Why This Exists

plan exists so a feature or a fix gets its goals, non-goals, risks, acceptance criteria, and verification shape written down and accepted by the user before any code moves, with every step small enough to check and no implementation hidden inside the plan text.

Do Not Use When

  • The request is casual chat, a status-only acknowledgement, or another workflow has stronger routing evidence.
  • The user needs implementation, review, CI, merge, or external publishing evidence that has not been delegated or observed.

Examples

Good example:

  • Prompt: plan: handle a planning request that needs explicit evidence boundaries and a clear stop condition.
  • Expected behavior: Run plan only after naming the target, evidence boundary, and stop condition.
  • Why: The request matches the catalog use case and keeps observed evidence separate from prepared guidance.

Bad example:

  • Prompt: plan: 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 plan.
  • Why: The request lacks the required inputs or would overclaim work that Hermes did not observe.

Completion Checklist

  • The plan names goals, non-goals, assumptions, acceptance criteria, and verification shape.
  • Draft recommendations, accepted decisions, and executor handoffs are separate states.
  • Rejected options or unresolved tradeoffs are recorded before handoff.
  • When the plan describes code changes, it has a Review Focus section with at most 5 uncovered inputs and their owning tasks, or one stating the check found none.

Recovery Notes

  • If acceptance criteria or verification are missing, route back to clarification before handoff.
  • If assumptions materially affect the plan, keep them visible and avoid treating the plan as accepted.

When to Use

Use for structured planning when implementation is not ready to start safely, including feature work that needs a safe plan before handoff.

Strong routing signals: `plan`, `$plan`, `implementation plan`, `make a plan`, `write a plan`, `write the plan`, `task breakdown`, `safe feature`, `safely add a feature`, `add a feature`, `feature request`, `new feature`, `product triage`, `bug triage`, `issue triage`, `reproduction plan`, `workflow hub`, `coding handoff`, `project template`, `github pr workflow`, `実装計画`, `タスク分解`, `安全に機能追加`, `機能追加の計画`, `답할 차례`, `준비할 차례`, `재현 계획`, `요구사항 정리`, `작업 허브`, `작업 허브가 필요`, `상태와 다음 행동`, `프로젝트별 운영`, `实现计划`, `任务拆解`, `安全地新增功能`, `新增功能计划`, `susun rencana implementasi`, `pecah fitur ini jadi langkah pengerjaan`, `bantu rencanakan penambahan fitur`, `tolong bikin rencana perbaikan bug`, `susun tahapan implementasi sebelum mulai coding`, `lập kế hoạch thực hiện`, `chia tính năng này thành các bước thực hiện`, `giúp lên kế hoạch thêm tính năng`, `hãy lập kế hoạch sửa lỗi`, `xác định các giai đoạn thực hiện trước khi code`

Catalog Metadata

Category: planning Phase: plan Quality tier: acceptance-gated Reasoning demand: standard

Quality bar:

  • Make goals, non-goals, risks, acceptance criteria, and verification shape explicit.
  • Where the repository declares non-negotiable principles, load references/project-constitution.md and record the check: a plan conflicting with a MUST is resolved by changing the plan, never by reinterpreting the principle.
  • Keep draft plans unapproved until the user accepts; a wrapper must carry their answer.
  • Only prepare coding handoff guidance after the plan is accepted.
  • When the plan describes code changes, add a Review Focus section listing at most 5 inputs or failure modes the requirements imply that no planned test covers, likeliest first, each with its expected behavior and the owning task that will add the test; an input the requirements do not mention must still leave the result intact, and when the check turns up nothing the section says it found none.
  • When the plan describes code changes, keep each step to a single action whose outcome can be checked - a named test and what it asserts, an exact signature and its file, or a command and the output that counts as passing; a step that leaves the choice open (TBD, make it robust) is a gap.
  • When the plan describes code changes, run a proportion check before acceptance: if the plan text outgrows the code it leads to, or is mostly function bodies, implementation has leaked into it; reduce bodies to test names, assertions, and signatures.
  • Take plan acceptance only from the answer the user gave — the response status and value the question tool returns, or the user's own reply — never from the wording of the question you asked: when the acceptance question times out, is dismissed, or returns with no answer, the plan stays unaccepted, so record no acceptance, prepare no handoff, and start nothing; a missing answer is not a refusal either, so ask again later or report the plan as awaiting acceptance.
  • Plan acceptance approves the plan content, not execution: after acceptance, recommend the follow-on path that fits the work's shape — ultrawork durable checkpoints for progress that must survive sessions as a checkpointed ledger, ultrawork coordinated lanes for an accepted plan split into disjoint parallel lanes, ultrawork single-owner persistence for one already-scoped task with a single owner, ultrawork for one bounded delivery cycle, or a direct selected executor/runtime handoff for a single prepared coding change — state the fit reason in one line, and start it only after the user's explicit go-ahead.

Required inputs:

  • requirements
  • constraints
  • known facts
  • non-goals

Expected outputs:

  • plan
  • acceptance criteria
  • verification strategy

Artifact expectations:

  • plan artifact when durable execution will follow

Safety rules:

  • Do not imply hidden Hermes runtime behavior.
  • Use the smallest verification that can prove the claim.

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. Record actual tool results, or not_observed / not_available, in the record; 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. Reply in the user's own words and the host's own voice: the host's persona owns reply language, tone, speech level, and sentence endings, progress updates included (where it sets no language, use the one the user wrote in), and OMH shapes structure and content only; OMH's record terms (surface, lane, wrapper, handoff, evidence boundary, not_observed) stay in records and tool calls, never in the sentence the user reads unless they ask about one; and when a real unresolved user decision or a useful briefing discussion remains, offer one specific, content-linked question. Completed work needs no question; omit unrelated non-action footers and generic offers to continue. 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 Plan AI skill do?

[omh] Software feature or bugfix not yet planned: structured planning before execution. Use when the user says: plan, implementation plan, make a plan, write a plan, write the plan, task breakdown, safe feature, safely add a feature.

Why use Omh Plan on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rlaope/oh-my-hermes/tree/main/agent-skills/omh-plan. 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 Plan?

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 Plan?

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

Is the Omh Plan 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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