Commandcode Delegate logo

Commandcode Delegate

CommunityPopular
amElnagdy
commandcode-delegate

Delegate a coding task to the Command Code CLI (`cmd`) as a background implementer, then review its diff and land it yourself. Use this whenever the user wants to hand implementation work to Command Code — phrasings like "have Command Code do X", "delegate this to commandcode", "run it through cmd", or "use Command Code to implement/fix/refactor" — or to run a queue of coding tasks through Command Code while staying the reviewer. DO NOT USE for tasks small enough to do inline, or when the user wants the code written directly without delegating.

Overview

PublisheramElnagdy
Repositorydelegate-skills
Skill namecommandcode-delegate
Stars
2.1K
Forks
167
Bundled files
5
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.

  • 5 bundled files

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

  • Open source

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

Installation

Install the Commandcode Delegate 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/amElnagdy/delegate-skills.git /tmp/delegate-skills
mkdir -p .claude/skills
cp -r /tmp/delegate-skills/skills/commandcode-delegate .claude/skills/commandcode-delegate
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Commandcode Delegate 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 Commandcode Delegate 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 Commandcode Delegate 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.

Command Code Delegate

You are the orchestrator. This skill lets you hand a bounded coding task to a separate implementer — the Command Code CLI (cmd) — then review what it produced and land it yourself. You write the brief and own the judgment; Command Code does the typing in your working tree; you verify and commit.

Nothing here is specific to one orchestrating agent. The loop needs only the ability to run a shell command and read a file, so it works the same whether you are Claude Code, OpenCode with a selected model, or any comparable agent. (It is designed for and run on Claude Code; treat other orchestrators as designed-for, not yet proven.)

When NOT to use this

  • The task is small enough to just do inline — delegation overhead is not worth it.
  • The cmd CLI is not installed or not authenticated (run cmd login).
  • You want to write the code yourself, or you only need a review (Command Code has its own /review).
  • You are on native Windows and cmdc --version does not work. Upstream recommends WSL for stable Windows use.

Read this before the first dispatch: the autonomy model

Command Code's headless mode has exactly two states, with nothing in between:

  • Default (-p with no --yolo): read, grep, and glob work. Every write, edit, and shell call is refused by the CLI's permission layer, and headless mode has no prompt to grant them mid-run. This is the relay's --read-only.
  • --yolo (alias --dangerously-skip-permissions): every tool is allowed, anywhere the process can reach. There is no filesystem sandbox and no path restriction. This is what an implementation run needs, so the relay passes it by default.

--permission-mode auto-accept and --tools-all do not lift the headless write gate. Direct CLI probes refused write, edit, and shell with both. So an implementation run through Command Code is a full-trust run: scope it with a tight brief and a clean working tree, not with a sandbox. The brief is guidance, and a git worktree isolates a checkout without containing the process. If writes outside the target tree are unacceptable, use an OS-enforced sandbox such as codex-delegate or run this one inside a container.

Prerequisites (check once)

  1. cmd --version succeeds and cmd status reports authenticated. If not, install Command Code and run cmd login.
  2. Confirm the CLI on PATH. On macOS/Linux, command -v cmd shows the active cmd. On native Windows, use cmdc --version; cmd is the system shell. The relay uses cmdc there and launches its npm .cmd shim through cmd.exe. COMMANDCODE_BIN remains an absolute-path override and must never point to the system command interpreter. The relay records the version it ran in result.json, so a wrong binary is visible after the fact.
  3. You are in (or will point --cd at) the target git repository, and its tree is clean before you dispatch — a full-trust run is much easier to review against a clean baseline.

The loop

Run these five steps per task. Steps 1, 4, and 5 are your judgment; 2 and 3 are mechanical.

1. Write the brief

Command Code sees only the text you send — no repo memory, no chat history, no shared context (beyond the repo's own AGENTS.md, which it reads automatically). Everything the task needs goes in the brief: the goal, the current state, what to change, what to leave untouched, the project's actual gate commands (discover them from the repo's AGENTS.md/CLAUDE.md/Makefile — do not assume), and a report contract. Tell it that it will not commit (you will). Keep one task per brief. Full guidance and a template: references/writing-the-brief.md.

2. Dispatch

Send the brief to Command Code with the bundled helper. It wraps cmd -p, captures the run, and writes a structured result.json — so your only job is "run a command, read a file." (<skill-dir> below is this skill's installed directory — the folder containing this SKILL.md, i.e. the directory you loaded the skill from. Claude Code prints it as "Base directory for this skill" when the skill loads; on other orchestrators use that same directory — if unsure where it landed, run find ~ -name relay.mjs -path '*commandcode-delegate*' and substitute the directory above it.)

bash
node "<skill-dir>/scripts/relay.mjs" --brief brief.txt --cd /path/to/repo
# read-only (review/diagnosis, no edits):   add --read-only
# continue the exact session:               add --session <sessionId>  (from result.json; send only the delta brief)
# fallback when no session id is available: add --continue-last
# hard time limit (watchdog):               add --timeout 2h  (default: off; implementation runs routinely need 1-2h)
# see all options:                          node .../relay.mjs --help

The helper defaults to a write-capable (--yolo) run, which intentionally edits the target repository. Its temp directory keeps only relay artifacts out of that repository. The relay never commits — see step 5. Mechanics, flags, and the result.json shape: references/dispatch-and-poll.md.

3. Wait for completion

The helper blocks until Command Code finishes, so back it with whatever your orchestrator offers and resume when it returns:

  • Claude Code: run the Bash call with run_in_background: true; you are notified on completion.
  • Plain shell / other agents: run it in the foreground for short tasks, or background it and poll the result file — … & in bash/zsh, or your shell's equivalent. The run is done when result.json exists with a status. (A pre-run usage error — bad args or an empty brief — instead exits with code 2 and a stderr message and writes no result file, so check the exit code too. A missing cmd binary exits 127 but does write a result.json with status commandcode_unavailable.)

Do not trust progress trackers over reality: a run is finished when result.json is written and the process has exited. Read the working tree, not a status line. The implementer's full report is the finalMessage field in result.json (also printed in full on stdout between the report markers).

4. Review — do not trust the self-report

result.json includes Command Code's own summary and gate claims. Re-verify, don't accept:

  • Re-run the project's gates yourself (the test/lint/build commands from step 1). Never take "gates passed" on faith.
  • Read the diff against the brief: did it do what was asked, nothing more (scope creep) and nothing less? touchedFiles in the result is your starting point — and because the run was full-trust, check for edits outside the paths the brief named, not just inside them.
  • Run the relevant guard skills on the diff if you have them installed (clean-code-guard, test-guard, etc. from guard-skills) — this skill produces the work; those skills judge it.
  • For schema/migration changes, round-trip them; for removals, grep for dangling references.

Full checklist: references/review-and-land.md.

5. Land it

The relay never commits, but it cannot stop Command Code under --yolo from writing .git. The brief forbids implementer commits, and the reviewer compares HEAD with the recorded pre-dispatch baseline before landing anything. The orchestrator commits. Only after the gates pass and the diff holds:

  • Commit the verified work yourself, with a clear message.
  • If it needs changes, send a delta brief with --session <sessionId> from the prior result.json (use --continue-last only when no session id is available), and review again.

Read-only second opinions

The relay doubles as a clean way to get an adversarial second opinion: dispatch --read-only with a brief that lists the agreed points, then each contested point with both positions, and ask Command Code to defend or concede each — deliverable in its final message, touching no files. The read-only guarantee here is the CLI's own permission layer rather than an OS sandbox, so the relay also checks it after the fact: readOnlyViolation: false means the Git-visible detector saw no change (ignored or outside-repository paths are not covered); true means it saw one; null means git could not tell.

Authorization model

Delegation is something the human opts into. Once they have ("run this queue", "proceed"), committing verified, gate-passing work is the agreed contract — that is the whole point. Two limits on that mandate: surface, don't absorb (report Command Code's design decisions, defensible-but-unasked turns, and non-blocking nitpicks rather than silently keeping them) and stop for scope changes (if correct completion needs going beyond the brief, ask — don't expand the mandate yourself). The full treatment is in references/review-and-land.md.

References

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 Commandcode Delegate AI skill do?

Delegate a coding task to the Command Code CLI (`cmd`) as a background implementer, then review its diff and land it yourself. Use this whenever the user wants to hand implementation work to Command Code — phrasings like "have Command Code do X", "delegate this to commandcode", "run it through cmd", or "use Command Code to implement/fix/refactor" — or to run a queue of coding tasks through Command Code while staying the reviewer. DO NOT USE for tasks small enough to do inline, or when the user wants the code written directly without delegating.

Why use Commandcode Delegate on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/amElnagdy/delegate-skills/tree/master/skills/commandcode-delegate. 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 Commandcode Delegate?

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 Commandcode Delegate?

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

Is the Commandcode Delegate AI skill free?

Yes. It is published on GitHub by amElnagdy 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇