Coding logo

Coding

OrganizationPopular
leon-ai
coding

Investigate, implement and verify code changes directly in a repository using Leon's file, search and shell tools.

Overview

Publisherleon-ai
Repositoryleon
Skill namecoding
Stars
17.6K
Forks
1.5K
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 leon-ai on GitHub. Read the source before you install it.

Installation

Install the Coding 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/leon-ai/leon/tree/develop/skills/agent/coding
  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/leon-ai/leon.git /tmp/leon
mkdir -p .claude/skills
cp -r /tmp/leon/skills/agent/coding .claude/skills/coding
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Coding 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 Coding 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 Coding 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.

Coding

Use Leon's existing agent loop and tools to complete coding tasks directly. Respect the owner's requested scope: analysis is not authorization to edit, commit, push or publish. Delegate only when the owner asks or has an applicable preference.

For requested delegation, use the owner's preferred available agent and interface. Pass the repository, task, applicable instructions and authorized scope. Inspect and verify the actual result; submitting a prompt is not completion. If delegation is unavailable, explain briefly and continue directly within the authorized scope.

Repository and instructions

  1. Establish the exact workspace and working directory from the request and live observations. Read relevant manifests and nearby implementations; use existing helpers before adding abstractions.
  2. Before editing, discover and read applicable AGENTS.md files from the repository root through each target directory. Deeper instructions apply only within their directory and take precedence over broader project rules. For a non-Git workspace, use the owner-selected workspace boundary; do not inherit unrelated parent-directory instructions.
  3. Record the workspace, applicable instruction paths, constraints and acceptance criteria from the request and supplied references in plan details. Pair each criterion with its required check. Before editing a new directory, discover its nested instructions. After compaction or continuation, reload applicable instruction files before further edits; summaries are not substitutes for their full current content.
  4. Inspect Git status and existing staged/unstaged diffs. Record unrelated changed and untracked files and preserve their content. Do not reset, clean, stage or commit owner work without authorization. Use an isolated worktree when concurrent work or the requested workflow warrants it.

Investigate and change

  • Use bounded file reads, ripgrep and available structural search. Trace definitions and callers; check installed dependencies instead of inventing symbols or APIs. Batch independent discovery calls.
  • For a bug, reproduce the failure with a relevant check before changing the implementation. Inspect existing tests and extend only meaningful coverage. Do not weaken tests to obtain a pass.
  • Prefer file.patch for changes to existing text, using the current read's content hash when available. Supply unique exact context and preserve unrelated changes. On stale or ambiguous matches, re-read and revise the patch rather than repeating it or overwriting from a partial view.
  • Use file.write for new files; overwriting an existing file requires its complete current content. Follow the project's formatting and dependency-management instructions.
  • Use shell.executeCommand for finite commands and project checks, always with the observed workspace cwd. Discover commands from project configuration rather than guessing a package manager.
  • Use shell session functions for dev servers, watchers or interactive programs. Record returned session IDs in the plan, follow output cursors, and verify process status. A started process is not a completed or successful check. Never invent handles, replay a command to recover output, or send secrets through stdin.

Verify and finish

  1. Run the checks required by project instructions and those relevant to the changed behavior. Inspect actual exit codes and diagnostics; record each criterion's observed outcome and evidence in plan details. Changes after a successful check make affected proof stale; rerun those checks.
  2. For UI work, visually inspect supplied references and the final rendered page at the requested viewports. Compare required layout, typography, assets and content; exercise affected interactions. A build or screenshot file alone does not prove visual conformance. Fix observed mismatches and reinspect affected results; derive checks from the actual brief and references, not assumed template conventions.
  3. Review the final diff against the original task and initial owner changes. Remove accidental edits without discarding owner work. Verify unrelated staged, unstaged and untracked content remains intact.
  4. Stop command sessions owned by this task unless the owner asked to keep them running. Inspect returned termination status; clean up on failures too.
  5. Reconcile all acceptance criteria before reporting completion. Use available tools to resolve missing evidence; disclose requirements that could not be verified. Report what changed, which checks passed or failed, and any remaining blocker. Do not claim completion from an edit acknowledgement, running process or unavailable check. Suggest a commit message when the repository requires one, but do not commit unless asked.

Frequently asked questions

What does the Coding AI skill do?

Investigate, implement and verify code changes directly in a repository using Leon's file, search and shell tools.

Why use Coding on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/leon-ai/leon/tree/develop/skills/agent/coding. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Coding?

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 Coding?

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

Is the Coding AI skill free?

Yes. It is published on GitHub by leon-ai 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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