Code Plan logo

Code Plan

Organization
runxhq
code-plan

Compile a bounded objective or code-intake change set into a dependency-safe Runx execution plan, with deterministic checks for evidence preservation, mutation boundaries, catalog references, and skill-package ownership. Use before multi-step or cross-surface work; this skill plans but does not execute.

Overview

Publisherrunxhq
Repositoryrunx
Skill namecode-plan
Stars
105
Forks
109
Bundled files
6
LicenseApache-2.0
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.

  • 6 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by runxhq on GitHub. Read the source before you install it.

Installation

Install the Code Plan 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/runxhq/runx/tree/main/skills/code-plan
  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/runxhq/runx.git /tmp/runx
mkdir -p .claude/skills
cp -r /tmp/runx/skills/code-plan .claude/skills/code-plan
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Code Plan 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 Code Plan 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 Code Plan 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.

Code Plan

Turn one objective into a plan that downstream runners can consume without reinterpreting the source request.

Procedure

  1. Start from objective. When change_set is supplied by code-intake, preserve it exactly; do not rewrite its target surfaces, invariants, success criteria, or commencement decision.
  2. Classify the plan as workspace_change or skill_package. Skill-package authoring belongs to skill-build; do not route it through a generic repo-change step.
  3. Split work at authority, mutation, dependency, and review boundaries. Keep steps coarse enough to produce a meaningful artifact.
  4. Declare ordered phases and repo change requests. Parallel work must have no dependency or shared mutation target.
  5. Declare orchestration steps with exact skill references, scopes, mutation flags, inputs, and prior-step context references.
  6. Return blocked when a question must be answered before mutation. Never turn uncertainty into an executable plan.
  7. Native catalog inspection supplies the actual installed skill set. The domain validator then verifies source preservation, ordered DAGs, mutation declarations, context references, catalog ownership, and the skill-build boundary. Invalid candidates are withheld and returned as a blocked packet.

This skill does not approve or execute mutations. A mutating: true field describes authority a future step will need.

Output

yaml
decision: ready | blocked
plan_kind: workspace_change | skill_package
change_set: object
objective_summary: string
workspace_change_plan: object
orchestration_steps: array
required_skills: array
open_questions: array
evidence: object
validation:
  status: pass | hold
  findings: array

workspace_change_plan contains plan_id, change_set_id, objective_summary, shared invariants, success criteria, ordered phases, integration checks, and open questions. Each phase contains ordered repo change requests with dependencies, validation commands, and mutation declarations.

evidence.source_change_set_status is preserved, drifted, or not_supplied; source_change_set_preserved: false is never a successful preservation claim. When a caller supplies source context, drift or loss blocks the plan.

Inputs are objective, optional project_context, thread_locator, thread, change_set, and harness_context. harness_context is passed through from the caller; the agent does not reconstruct or advance it.

Agent task contracts

code-plan-draft

Return work_plan_draft with decision, plan_kind, change_set, objective_summary, workspace_change_plan, orchestration_steps, required_skills, and open_questions. Preserve a supplied change_set exactly. Split at authority, mutation, dependency, and review boundaries; do not split for cognitive convenience. Every phase, repo request, and orchestration step needs an id, ordered dependencies, and an explicit mutation flag. A ready plan has no blocking open questions. Use plan_kind skill_package for Runx skill authoring and route that work through skill-build. This runner plans only and never claims downstream execution.

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 Code Plan AI skill do?

Compile a bounded objective or code-intake change set into a dependency-safe Runx execution plan, with deterministic checks for evidence preservation, mutation boundaries, catalog references, and skill-package ownership. Use before multi-step or cross-surface work; this skill plans but does not execute.

Why use Code Plan on TypingMind?

Because you install it once and use it with any model. Code Plan 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 Code Plan in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/runxhq/runx/tree/main/skills/code-plan. 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 Code Plan?

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 Code Plan?

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

Is the Code Plan AI skill free?

Yes. It is published on GitHub by runxhq under the Apache-2.0 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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