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To Issues

Community
elianiva
to-issues

Break a plan, spec, or PRD into independently-grabbable issues on the project issue tracker using tracer-bullet vertical slices. Use when user wants to convert a plan into issues, create implementation tickets, or break down work into issues.

Overview

Publisherelianiva
Repositorydotfiles
Skill nameto-issues
Stars
204
Forks
9
Bundled files
Instructions only
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 elianiva on GitHub. Read the source before you install it.

Installation

Install the To Issues 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/elianiva/dotfiles.git /tmp/dotfiles
mkdir -p .claude/skills
cp -r /tmp/dotfiles/agents/skills/to-issues .claude/skills/to-issues
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable To Issues 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 To Issues 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 To Issues 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.

To Issues

Break a plan into independently-grabbable issues using vertical slices (tracer bullets).

The issue tracker and triage label vocabulary should have been provided to you — run /setup-matt-pocock-skills if not.

Process

1. Gather context

Work from whatever is already in the conversation context. If the user passes an issue reference (issue number, URL, or path) as an argument, fetch it from the issue tracker and read its full body and comments.

2. Explore the codebase (optional)

If you have not already explored the codebase, do so to understand the current state of the code. Issue titles and descriptions should use the project's domain glossary vocabulary, and respect ADRs in the area you're touching.

3. Draft vertical slices

Break the plan into tracer bullet issues. Each issue is a thin vertical slice that cuts through ALL integration layers end-to-end, NOT a horizontal slice of one layer.

Slices may be 'HITL' or 'AFK'. HITL slices require human interaction, such as an architectural decision or a design review. AFK slices can be implemented and merged without human interaction. Prefer AFK over HITL where possible.

4. Quiz the user

Present the proposed breakdown as a numbered list. For each slice, show:

  • Title: short descriptive name
  • Type: HITL / AFK
  • Blocked by: which other slices (if any) must complete first
  • User stories covered: which user stories this addresses (if the source material has them)

Ask the user:

  • Does the granularity feel right? (too coarse / too fine)
  • Are the dependency relationships correct?
  • Should any slices be merged or split further?
  • Are the correct slices marked as HITL and AFK?

Iterate until the user approves the breakdown.

5. Publish the issues to the issue tracker

For each approved slice, publish a new issue to the issue tracker. Use the issue body template below. These issues are considered ready for AFK agents, so publish them with the correct triage label unless instructed otherwise.

Publish issues in dependency order (blockers first) so you can reference real issue identifiers in the "Blocked by" field.

A reference to the parent issue on the issue tracker (if the source was an existing issue, otherwise omit this section).

What to build

A concise description of this vertical slice. Describe the end-to-end behavior, not layer-by-layer implementation.

Avoid specific file paths or code snippets — they go stale fast. Exception: if a prototype produced a snippet that encodes a decision more precisely than prose can (state machine, reducer, schema, type shape), inline it here and note briefly that it came from a prototype. Trim to the decision-rich parts — not a working demo, just the important bits.

Acceptance criteria

  • Criterion 1
  • Criterion 2
  • Criterion 3

Blocked by

  • A reference to the blocking ticket (if any)

Or "None - can start immediately" if no blockers.

Do NOT close or modify any parent issue.

Frequently asked questions

What does the To Issues AI skill do?

Break a plan, spec, or PRD into independently-grabbable issues on the project issue tracker using tracer-bullet vertical slices. Use when user wants to convert a plan into issues, create implementation tickets, or break down work into issues.

Why use To Issues on TypingMind?

Because you install it once and use it with any model. To Issues 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 To Issues in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/elianiva/dotfiles/tree/master/agents/skills/to-issues. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use To Issues?

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 To Issues?

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

Is the To Issues AI skill free?

It is published on GitHub by elianiva. Check the repository for licensing terms. 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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