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Copilot Delegate

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amElnagdy
copilot-delegate

Delegate a coding task to the GitHub Copilot CLI (`copilot`) as a background implementer, then review its diff and land it yourself. Use this whenever the user wants to delegate implementation work to Copilot - phrasings like "have Copilot implement X", "delegate this to copilot", "run it through Copilot CLI", or "use copilot to implement/fix/refactor" - or wants to run a queue of coding tasks through Copilot 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 namecopilot-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 Copilot 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/copilot-delegate .claude/skills/copilot-delegate
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Copilot 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 Copilot 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 Copilot 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.

Copilot Delegate

You are the orchestrator. Delegate a bounded coding task to a separate implementer — the GitHub Copilot CLI — then review what it produced and land it yourself. You write the brief and own the judgment; the implementer makes changes in its own session in a clean working tree; you verify and commit.

The loop needs only a shell command and file access, so any comparable orchestrator can drive it.

When NOT to use this

  • The task is small enough to do inline; delegation overhead is not worth it.
  • The copilot CLI is not installed or authenticated.
  • You need a hard sandbox. Copilot exposes sandbox controls, but they are upstream-experimental (MXC-based, controlled via the /sandbox command and settings, disabled by default) — this relay does not configure them. --read-only only disables edit tools (--mode plan); shell commands still run. If project files must not change at all, dispatch against a clean or isolated worktree.

Prerequisites (check once)

  1. Install copilot (npm install -g @github/copilot; the CLI requires Node 22+, the relay itself runs on Node 18+ — the relay probes copilot version).
  2. Authenticate: run copilot login (interactive web/device flow), or set COPILOT_GITHUB_TOKEN / GH_TOKEN / GITHUB_TOKEN in the environment.
  3. Confirm copilot version succeeds.
  4. Work in, or point --cd at, the target git repository.

Choose the model (optional)

Copilot picks a default model (auto). To choose another, pass --model <name>. The relay accepts letters, digits, and . _ : / - only (the value reaches a shell on Windows).

Choose the effort (optional)

Copilot supports a reasoning effort dial: --effort <level> with values low, medium, high, xhigh, or max. The relay rejects any other value before dispatch.

The loop

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

1. Write a brief

Copilot sees only the text you send. It cannot read your conversation: the brief must stand alone with the goal, current state, what to change, what to leave untouched, the project's real gates, and a report contract. Keep each brief to a single task. Write it to a file and pass it as the relay's --brief. See references/writing-the-brief.md.

2. Dispatch

Use the bundled relay. It runs copilot -p with --output-format json --no-color --stream off, captures the JSONL event stream, and writes result.json.

bash
node "<skill-dir>/scripts/relay.mjs" --brief brief.txt --cd /path/to/repo
# choose a model:                    add --model <name>
# set reasoning effort:              add --effort <level>
# read-only planning pass:           add --read-only  (forces --mode plan)
# full tool autonomy:                add --allow-all-tools
# hard time limit (watchdog):        add --timeout 2h   (the 30m default suits brief runs)
# resume a session:                  add --session <id>  or  --resume-last
# see all options:                   node .../relay.mjs --help

The child's cwd pins the workspace. The relay writes artifacts under the system temp dir by default and never commits. See references/dispatch-and-poll.md.

3. Wait for completion

The relay blocks until copilot finishes. Run it with the orchestrator's background-command facility, or background it in the shell and poll for result.json. A pre-run usage error exits 2 and writes no result; a missing copilot exits 127 and writes status: "copilot_unavailable".

Completion means the process exited and result.json exists — trust process state and the working tree, not the progress display. Copilot's final assistant message is the finalMessage field of result.json.

4. Review — do not trust the self-report

  • Re-run the project's gates yourself.
  • Read the diff against the brief, starting with touchedFiles.
  • Run relevant guard skills if installed.

See references/review-and-land.md.

5. Land it

If the work is good, commit it. The relay never commits — the diff and result.json are the record; run git status and git diff first to confirm exactly what changed. If the group has a PR flow, make the commit and push a branch; let human review happen. If the diff is wrong or incomplete, re-dispatch a corrected brief in a fresh run and review again.

Autonomy and permissions

Without --allow-all-tools, copilot auto-denies tool calls in headless mode: the process exits 0 but the relay detects the denial events and reports status: "failed" with the CLI's own error message and a hint to pass --allow-all-tools. This is the honest default — the orchestrator sees the failure rather than a silent no-op.

--allow-all-tools explicitly grants full tool autonomy. --read-only selects --mode plan, which disables edit tools so project files can't be changed by direct edits; it works without --allow-all-tools. Shell commands still run in plan mode, so it guards against edits, not against everything. The two flags are mutually exclusive.

Copilot also exposes sandbox controls, but they are upstream-experimental (MXC-based, controlled via the /sandbox command and settings, disabled by default). This relay does not configure them.

Authorization model

Delegation is something the human opts into. Once briefed, copilot works as a tool you approved use of. The boundary is: do not accept conclusions from the self-report; verify everything on disk. For anything touching credentials, production data, or irreversible operations, stop and ask the human first instead of encoding it in a brief.

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 Copilot Delegate AI skill do?

Delegate a coding task to the GitHub Copilot CLI (`copilot`) as a background implementer, then review its diff and land it yourself. Use this whenever the user wants to delegate implementation work to Copilot - phrasings like "have Copilot implement X", "delegate this to copilot", "run it through Copilot CLI", or "use copilot to implement/fix/refactor" - or wants to run a queue of coding tasks through Copilot 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 Copilot Delegate on TypingMind?

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

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

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

Is the Copilot 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.

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