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Simple Issue Description

OrganizationPopular
every-app
simple-issue-description

Turn a rough bug report, feature request, support note, or pull request into a short, plain-language issue focused on the problem and desired behavior. Use when a contributor asks to simplify an issue, explain what a PR is for, create the corresponding issue for a PR, remove implementation detail from a report, or invokes /simple-issue-description.

Overview

Publisherevery-app
Repositoryopen-seo
Skill namesimple-issue-description
Stars
22K
Forks
2.8K
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 every-app on GitHub. Read the source before you install it.

Installation

Install the Simple Issue Description 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/every-app/open-seo/tree/main/.agents/skills/simple-issue-description
  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/every-app/open-seo.git /tmp/open-seo
mkdir -p .claude/skills
cp -r /tmp/open-seo/.agents/skills/simple-issue-description .claude/skills/simple-issue-description
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Simple Issue Description 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 Simple Issue Description 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 Simple Issue Description 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.

Simple Issue Description

Write an issue a maintainer can understand in under a minute. Focus on what someone experiences and what should happen instead.

Workflow

  1. Read the supplied notes, conversation, issue, PR description, or diff. In a PR, also check for spec, design doc, or README changes — they often state the intent better than the description does.
  2. Identify the concrete problem. State who or what is affected when the source makes that clear.
  3. Describe the desired behavior without prescribing an implementation.
  4. Keep only context that helps someone understand or reproduce the problem.
  5. If the source is a PR or diff, describe the problem the change tries to solve, not the files or code it changes.
  6. Draft the issue immediately unless the problem and desired behavior cannot be determined. In that case, ask one short clarifying question.

One issue per problem. If the source bundles unrelated problems, draft the issue for the most significant one, list the others in a line each, and tell the contributor to split them into separate issues and separate pull requests.

If the source adds a capability instead of fixing a misbehavior, do not stage the absence as a bug. Describe what a user cannot do today and the full user experience of the feature: who uses it, from where, and what they see.

Do not invent user impact, reproduction steps, or certainty that the source does not support. If the source only shows cleanup, refactoring, or a possible code smell, say that no concrete problem is clear instead of manufacturing an issue — this rule wins over step 6. Reply with two or three sentences: what the source shows, and what evidence would make it issue-worthy.

If the source fixes a security weakness that is not already public, do not draft a public issue describing it. Suggest reporting it privately to the maintainer instead.

Writing rules

  • Use plain language and short sentences.
  • Keep the issue under 200 words unless it is clear that more is necessary to describe the reproduction steps or a large feature.
  • Lead with behavior, not code, architecture, or the proposed fix. A one-sentence plain-language cause is fine when the symptom cannot be understood without it.
  • Keep technical details only when they are necessary to reproduce or understand the problem. Limits and defaults that make the symptom make sense (batch sizes, quotas, caps) count as necessary.
  • Preserve useful evidence such as error messages, screenshots, links, and documentation references.
  • Do not mention that AI wrote or reviewed the issue.
  • Avoid filler, praise, roadmap language, and exhaustive edge cases.
  • Use the contributor's level of certainty. Do not present a guess as a confirmed bug; write "can" or "appears to" when the source describes a risk rather than an observed failure.

Output format

The first line is the issue title — when filing on GitHub, put it in the title field instead of repeating it in the body. For a missing capability, title the outcome ("Flag pages with no structured data") rather than a fake bug ("Audits never mention structured data").

Omit the Extra context section when there is nothing useful to add. Constraints and scope notes belong there, including what is not affected when a maintainer would reasonably worry that it is.

markdown
# <Short title describing the problem or desired outcome>

## TL;DR

<In one or two sentences, explain what happens now and what should happen instead.>

## What is happening?

<Describe the current behavior in plain language. Include a concrete example when available.>

## What should happen?

<Describe the desired behavior without proposing how to build it.>

## Extra context

<Optional reproduction steps, error text, screenshots, links, or constraints.>

Example

markdown
# The chat jumps away from messages I am reading

## TL;DR

When I scroll up to read an older message, a new response moves me back to the bottom of the chat. The chat should stay where I left it until I choose to return to the latest message.

## What is happening?

New responses automatically scroll the chat to the bottom, even when I am reading earlier messages.

## What should happen?

Keep my current scroll position and show that a new message is available.

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 Simple Issue Description AI skill do?

Turn a rough bug report, feature request, support note, or pull request into a short, plain-language issue focused on the problem and desired behavior. Use when a contributor asks to simplify an issue, explain what a PR is for, create the corresponding issue for a PR, remove implementation detail from a report, or invokes /simple-issue-description.

Why use Simple Issue Description on TypingMind?

Because you install it once and use it with any model. Simple Issue Description 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 Simple Issue Description in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/every-app/open-seo/tree/main/.agents/skills/simple-issue-description. 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 Simple Issue Description?

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 Simple Issue Description?

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

Is the Simple Issue Description AI skill free?

Yes. It is published on GitHub by every-app 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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