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Omh Github Issue Intake

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rlaope
omh-github-issue-intake

[omh] GitHub issue intake workflow: turn a public chat report into a confirmed, verified issue package. Use when the user says: github-issue-intake, github issue intake, issue intake, file this as an issue, file a github issue, open a github issue, create a github issue, submit a github issue.

Overview

Publisherrlaope
Repositoryoh-my-hermes
Skill nameomh-github-issue-intake
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 Github Issue Intake 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-github-issue-intake .claude/skills/omh-github-issue-intake
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Omh Github Issue Intake 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 Github Issue Intake 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 Github Issue Intake 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.

Github Issue Intake

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

Why This Exists

github-issue-intake exists so a public support-chat report can become a verified GitHub issue through one bounded, confirmation-gated lane instead of ad hoc chat narration or an unscoped bot write.

Do Not Use When

  • The report only wants classification or signal clustering; use feedback-triage instead.
  • The event concerns an already-existing issue, PR, review, or CI run; use github-event-ops instead.
  • The user wants implementation; coding stays a separate follow-up lane with its own maintainer authority.
  • The report describes a security vulnerability; redirect to the private SECURITY.md path.

Examples

Good example:

  • Prompt: please file this as an issue: omh setup fails on Windows
  • Expected behavior: Classify the report, run the bounded interview, search duplicates, present the direction check, and prepare github_issue_intake/v1 for confirmation-gated connector handoff.
  • Why: The request is an explicit pre-creation filing ask with a classifiable report and an explicit target.

Bad example:

  • Prompt: github-issue-intake prove the issue was filed and labelled.
  • Expected behavior: Report that creation, labeling, and any GitHub mutation stay unobserved until an authorized connector returns read-back evidence.
  • Why: A prepared package is not issue creation, label application, or any GitHub mutation evidence.

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 a public chat report should become a new GitHub issue: classify it, ask at most three decision-changing questions, search duplicates, confirm the direction, and hand the scoped creation to an authorized connector.

Strong routing signals: `github-issue-intake`, `github issue intake`, `issue intake`, `file this as an issue`, `file a github issue`, `open a github issue`, `create a github issue`, `submit a github issue`, `report a bug as an issue`, `new github issue`, `이슈로 올려줘`, `깃허브 이슈로 올려줘`, `이슈 등록해줘`, `깃허브 이슈 등록해줘`, `깃허브 이슈 만들어줘`, `깃허브 이슈 생성해줘`, `버그 리포트 올려줘`, `새 이슈 만들어줘`

Catalog Metadata

Category: github-ops Phase: issue-intake Quality tier: workflow-surface-gated Reasoning demand: standard

Quality bar:

  • Classify the report from supplied or observed facts and separate observation from inference.
  • Ask at most three unresolved, decision-changing questions; stop with a specific missing-evidence request instead of filing a vague issue.
  • Present the direction check, require confirmation, and keep prepared packages distinct from observed creation.

Required inputs:

  • public report or summary
  • source boundary
  • explicit target repository
  • desired outcome
  • scope boundary
  • missing evidence

Expert clarification questions:

  • desired outcome
    • English: What is the smallest user-visible outcome this issue should ask for?
    • Korean: 이 이슈가 요구해야 할 가장 작은 사용자 관점 결과는 무엇인가요?
  • scope boundary
    • English: What is explicitly included in this issue, and what is explicitly out of scope?
    • Korean: 이 이슈에 명시적으로 포함되는 범위와 명시적으로 제외되는 범위는 무엇인가요?
  • missing evidence
    • English: Which reproduction steps, versions, or logs are still missing and would change the issue direction?
    • Korean: 이슈 방향을 바꿀 수 있는 재현 단계, 버전, 로그 중 아직 없는 증거는 무엇인가요?

Expected outputs:

  • github_issue_intake/v1
  • direction check
  • duplicate status
  • issue package or connector handoff
  • read-back verification or explicit blocker

Artifact expectations:

  • github_issue_intake/v1 metadata-only wrapper card when recorded

Safety rules:

  • Investigation is read-only: repository and documentation exploration plus GitHub duplicate search; never mutate code, settings, branches, commits, PRs, releases, or deployments.
  • No external mutation before the direction check and an explicit confirmation; a maintainer file-now requires authenticated-wrapper actor/evidence identity and never bypasses duplicate, template, security, or read-back gates.
  • Confirmation requires a complete direction check - type, user-visible problem, source summary, smallest desired outcome, included and excluded scope, observed evidence versus inference, and duplicate status - plus a completed duplicate search; any blocker (security redirect, missing evidence, connector unavailable, or credentials missing) stops confirmation and handoff and cannot be cleared by a later observed result.
  • A public reporter authorizes exactly one scoped create_issue against an explicit repository; code, configuration, branch, commit, PR, merge, deployment, and coding-executor mutations stay in their own maintainer-gated lanes.
  • Security vulnerability reports redirect to the private SECURITY.md reporting path instead of a public issue.
  • Core OMH never calls GitHub; only a checked-in issue-form builder can produce an authorized create_issue request. An authorized connector receives one stable idempotency-keyed request, must enforce that key externally, and returns observed result evidence bound to that request; dispatch consumes the core handoff, so dispatched or observed artifacts cannot hand off again.
  • A prepared issue package is not creation evidence; only connector read-back of repository, author, title, body, labels, and URL is observed evidence.
  • The target repository must be explicit or safely configured; never infer a cross-repository target from context.
  • github_issue_intake/v1 persists bounded metadata, digests, and refs only: no raw title, body, transcript, platform event, credential, prompt, private log, or private content; the complete request remains transient for the connector.

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 Github Issue Intake AI skill do?

[omh] GitHub issue intake workflow: turn a public chat report into a confirmed, verified issue package. Use when the user says: github-issue-intake, github issue intake, issue intake, file this as an issue, file a github issue, open a github issue, create a github issue, submit a github issue.

Why use Omh Github Issue Intake on TypingMind?

Because you install it once and use it with any model. Omh Github Issue Intake 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 Github Issue Intake in TypingMind?

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

Which AI models can use Omh Github Issue Intake?

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 Github Issue Intake?

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

Is the Omh Github Issue Intake 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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