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Omh Codebase Onboarding

CommunityPopular
rlaope
omh-codebase-onboarding

[omh] Hermes Codebase Onboarding workflow: create a repo map, reading path, glossary, risk map, and first-task runway for unfamiliar codebases. Use when the user says: codebase-onboarding, codebase onboarding, repo onboarding, repository onboarding, codebase tour, code tour, new repo orientation, understand this repo.

Overview

Publisherrlaope
Repositoryoh-my-hermes
Skill nameomh-codebase-onboarding
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 Codebase Onboarding 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-codebase-onboarding .claude/skills/omh-codebase-onboarding
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Omh Codebase Onboarding 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 Codebase Onboarding 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 Codebase Onboarding 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.

Codebase Onboarding

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

Why This Exists

codebase-onboarding adapts ECC's code-tour and onboarding surfaces into an OMH-native first-read workflow so unfamiliar repos become navigable before implementation pressure starts.

Do Not Use When

  • The user already named a concrete implementation task and acceptance criteria; use ultrawork or idea-to-deploy.
  • The user needs a whole-workspace capability inventory; use workspace-audit.
  • The user wants a code diff review; use code-review.

Examples

Good example:

  • Prompt: codebase-onboarding 처음 보는 레포라서 구조, 주요 모듈, 테스트, 첫 작업 후보를 잡아줘.
  • Expected behavior: Prepare repo_map/v1, reading_path/v1, domain_glossary/v1, risk map, and first_task_runway/v1 from observed files.
  • Why: The request is repo orientation before implementation.

Bad example:

  • Prompt: codebase-onboarding 파일 안 읽고 이 레포 아키텍처를 확정해줘.
  • Expected behavior: Mark architecture as unobserved and inspect source evidence before making claims.
  • Why: Onboarding is only useful when grounded in current repo evidence.

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.

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.

Use When

Use when Hermes should help an operator or coding executor understand an unfamiliar repository before planning implementation.

Strong routing signals: `codebase-onboarding`, `codebase onboarding`, `repo onboarding`, `repository onboarding`, `codebase tour`, `code tour`, `new repo orientation`, `understand this repo`, `how this repo works`, `first task runway`, `개발자 온보딩`, `레포 온보딩`, `코드베이스 온보딩`, `처음 보는 레포`, `레포 구조 설명`

Catalog Metadata

Category: planning Phase: codebase-onboarding Quality tier: onboarding-gated Reasoning demand: standard

Quality bar:

  • Name the audience, depth, repo root, read-only boundary, and stop condition.
  • Separate observed files and commands from inferred architecture and unknowns.
  • Produce a practical reading path and first-task runway rather than a flat file tour.
  • Route follow-up implementation to plan, ultrawork, verification-gate, or workspace-audit as needed.

Required inputs:

  • repo root or supplied source context
  • target audience: operator, new contributor, maintainer, or executor
  • desired depth: quick map, architecture tour, first issue, or handoff pack
  • known constraints such as no network, no secrets, or read-only mode

Expected outputs:

  • codebase_onboarding_plan/v1
  • repo_map/v1
  • reading_path/v1
  • domain_glossary/v1
  • risk_and_unknowns_map/v1
  • first_task_runway/v1
  • not-evidence boundary

Artifact expectations:

  • repo_map/v1 with observed directories, entrypoints, generated surfaces, tests, docs, scripts, and runtime artifacts
  • reading_path/v1 ordered from product direction to architecture, core modules, tests, and operational docs
  • domain_glossary/v1 with repo-specific terms, owners, artifacts, and evidence references
  • first_task_runway/v1 with low-risk starter tasks, verification commands, and handoff readiness

Safety rules:

  • Do not invent architecture, ownership, maturity, or runtime behavior without observed repo evidence.
  • Do not mutate files, run setup, install dependencies, or dispatch an executor from onboarding alone.
  • Keep onboarding findings, inferred risks, first-task suggestions, and implementation handoffs separate.
  • Never expose secrets from config or environment files; record only redacted paths and risk categories.

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 Codebase Onboarding AI skill do?

[omh] Hermes Codebase Onboarding workflow: create a repo map, reading path, glossary, risk map, and first-task runway for unfamiliar codebases. Use when the user says: codebase-onboarding, codebase onboarding, repo onboarding, repository onboarding, codebase tour, code tour, new repo orientation, understand this repo.

Why use Omh Codebase Onboarding on TypingMind?

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

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

Which AI models can use Omh Codebase Onboarding?

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 Codebase Onboarding?

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

Is the Omh Codebase Onboarding 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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