Codely Plan Create Github logo

Codely Plan Create Github

Organization
CodelyTV
codely-plan-create-github

Create a plan for the specified task and store it as GitHub issues in the repository of the current working directory. Given the URL of a GitHub issue, it turns that issue into the parent "plan" issue (Goal, Context and a checklist of phases) and creates one child issue per phase, linking every phase as a native GitHub sub-issue of the parent. Stops for user approval before creating any issue. After creation, the plan is meant to be implemented with the codely-plan-phase-implement-github skill.

Overview

PublisherCodelyTV
Repositoryagent-harness
Skill namecodely-plan-create-github
Stars
287
Forks
13
Bundled files
1
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 CodelyTV on GitHub. Read the source before you install it.

Installation

Install the Codely Plan Create Github 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/CodelyTV/agent-harness/tree/main/.agents/skills/codely-plan-create-github
  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/CodelyTV/agent-harness.git /tmp/agent-harness
mkdir -p .claude/skills
cp -r /tmp/agent-harness/.agents/skills/codely-plan-create-github .claude/skills/codely-plan-create-github
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Codely Plan Create Github 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 Codely Plan Create Github 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 Codely Plan Create Github 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.

🧠 How to create a plan

[!CRITICAL] Do NOT create or edit any GitHub issue until the user has agreed on the specific public contracts to be considered and the implementation phases. Propose first, get approval, then create the issues.

The structure of a plan, its sections and the rules to shape them are defined in resources/plan-guidelines.md. Read it before proposing anything.

🎯 Input

This skill is invoked as /codely-plan-create-github <github-issue-url>.

  • <github-issue-url> is the URL of the GitHub issue describing the task to plan. This issue becomes the parent "plan" issue.
  • If no URL is provided, ask the user for it before doing anything else.

🗂️ Repository

All the plan lives as GitHub issues in the repository of the current working directory. Never hardcode a repository: let gh resolve it from the local Git remote.

bash
gh issue view <number>
gh issue create --title "..." --body "..."
gh issue edit <number> --body "..."

Derive <number> from the provided URL. Confirm the repository gh resolves with gh repo view --json nameWithOwner --jq .nameWithOwner, and check that the provided URL belongs to it. If it does not, stop and ask the user to run the skill from the clone of that repository.

To link a phase as a native GitHub sub-issue of the parent, use the sub-issues REST API. It expects the child's numeric database id (not its issue number), so resolve it first. The {owner} and {repo} placeholders are substituted by gh with the current repository:

bash
# Resolve the child issue database id from its number
child_id=$(gh api repos/{owner}/{repo}/issues/<child> --jq .id)

# Attach the child as a native sub-issue of the parent
gh api --method POST repos/{owner}/{repo}/issues/<parent>/sub_issues -F sub_issue_id="$child_id"

🧱 Issue structure

A plan is stored as a tree of issues, using GitHub's native sub-issues feature:

  • Parent "plan" issue: the issue whose URL was passed in. It holds the Goal, the Context, the agreed design decisions, and a checklist of the phases, each item linking its child issue. The parent depends on its children: when every child issue is closed, the parent closes too.
  • One child issue per phase: each holds the phase description and its to-do actions as a checkbox list, plus a reference back to the parent (Part of #<parent>). Every child is attached to the parent as a native GitHub sub-issue (via the sub-issues API), not only as a checklist link.
#12 Product Bundles (parent plan)
     Goal / Context / design decisions
     - [ ] #13 Phase 1: ...
     - [ ] #14 Phase 2: ...
#13 Phase 1: ...   (child, to-do checkboxes)
#14 Phase 2: ...   (child, to-do checkboxes)

🪜 Steps to create a plan

  1. Read the task from the parent issue with gh issue view <parent>.

  2. Define task phases, letting the user choose the amount of phases as described in the guidelines.

  3. Specify the public contracts to be created/modified/deleted on each phase task, as described in the guidelines.

  4. Propose the plan to the user for approval. IMPORTANT: Do not create any issue until the user has agreed on the specific contracts to be considered and the implementation phases.

  5. Create one child issue per phase with gh issue create. Each child issue body must contain:

    • The phase description.
    • The phase to-do actions as a checkbox list (- [ ] ...).
    • The public contracts for that phase.
    • A Part of #<parent> reference line.

    Capture the number of every child issue returned by gh.

  6. Attach every child as a native sub-issue of the parent. For each child, resolve its database id and link it to the parent with the sub-issues API (see the snippet in the Repository section above):

    bash
    child_id=$(gh api repos/{owner}/{repo}/issues/<child> --jq .id)
    gh api --method POST repos/{owner}/{repo}/issues/<parent>/sub_issues -F sub_issue_id="$child_id"

    Do this for every phase, in order, so the parent lists all phases as native sub-issues.

  7. Update the parent issue with gh issue edit <parent> --body ... so its body contains the Goal, Context, agreed design decisions and a Phases checklist that links every child issue (- [ ] #<child> Phase N: <title>). Preserve the original task description from the parent issue: keep it as-is and append the new plan content below it, separated by a --- line (do not lose or rewrite the original text).

  8. Suggest next steps. Ask the user what do they want to do:

    • Do not do anything else.

    • Implement the plan by executing the /codely-plan-phase-implement-github <parent-issue-url> skill (implements Phase 1 only).

    • Implement a specific phase by executing the /codely-plan-phase-implement-github <child-issue-url> skill.

    [!IMPORTANT] /codely-plan-phase-implement-github handles one phase per invocation. Never implement all phases at once.

🗃️ Plan metadata

GitHub issues have no YAML frontmatter, so add the plan metadata as a footer at the end of the parent issue body:

markdown
---

<sub>Created by { tool } · { model.name } { model.version } (reasoning effort: { model.reasoning_effort }) · { current_date }</sub>
  • current_date: The current date in the format ISO 8601 RFC 3339 (YYYY-MM-DDTHH:MM:SSZ).
  • tool: The AI coding tool used (e.g. Claude Code, Cursor, Copilot, Codex)
  • model.name: The name of the model used to make the change (e.g. Claude Opus, Cursor Composer, OpenAI GPT)
  • model.version: The version of the model used to make the change (e.g. 4.6, 1.5, 5.4)
  • model.reasoning_effort: The reasoning effort of the model used to make the change (e.g. low, medium, high)

🗂️ Where each plan section lives

The parent issue holds the Goal, the Context, the Phases checklist and the Next step sections described in resources/plan-guidelines.md. Each child issue holds the description and the to-do actions list of its own phase.

As the phases are implemented through pull requests, the last to-do action of every phase asks for pull request titles instead of commit messages.

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 Codely Plan Create Github AI skill do?

Create a plan for the specified task and store it as GitHub issues in the repository of the current working directory. Given the URL of a GitHub issue, it turns that issue into the parent "plan" issue (Goal, Context and a checklist of phases) and creates one child issue per phase, linking every phase as a native GitHub sub-issue of the parent. Stops for user approval before creating any issue. After creation, the plan is meant to be implemented with the codely-plan-phase-implement-github skill.

Why use Codely Plan Create Github on TypingMind?

Because you install it once and use it with any model. Codely Plan Create Github 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 Codely Plan Create Github in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/CodelyTV/agent-harness/tree/main/.agents/skills/codely-plan-create-github. 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 Codely Plan Create Github?

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 Codely Plan Create Github?

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

Is the Codely Plan Create Github AI skill free?

It is published on GitHub by CodelyTV. 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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