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Pi Delegate

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amElnagdy
pi-delegate

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

Use it in TypingMind

Enable Pi 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 Pi 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 Pi 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.

Pi Delegate

You are the orchestrator. Delegate a bounded coding task to a separate implementer - the Pi coding agent 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; 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 pi CLI is not installed or authenticated.
  • You need a sandboxed implementer. Pi has no sandbox and no permission modes; --read-only restricts the tool surface, but a write-capable run executes without prompts.

Prerequisites (check once)

  1. Install pi with npm install -g @earendil-works/pi-coding-agent.
  2. Authenticate: /login inside pi for a subscription provider, or an API-key environment variable / pi's auth file for an API-key provider.
  3. Confirm pi --version succeeds.
  4. Work in, or point --cd at, the target git repository.

Choose the model (optional)

Omit --model to use pi's configured default. To pick another, choose from pi --list-models and pass an explicit id or pattern like <provider>/<model-id> or sonnet:high. The relay accepts letters, digits, and . _ : / - only (the value reaches a shell on Windows), so glob patterns with * are not forwarded.

The loop

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

1. Write the brief

Pi sees only the text you send plus what it can inspect in the workspace - no chat history or shared context. Include the goal, current state, what to change, what to leave untouched, the project's actual gates, and a report contract. Tell pi not to commit. Keep one task per brief. Pi auto-loads AGENTS.md/CLAUDE.md context files from the workspace and its parents, so repo instructions reach it without inlining. See references/writing-the-brief.md.

2. Dispatch

Use the bundled relay. It pipes the brief to pi --mode json on stdin, captures the JSON event stream, and writes result.json. (<skill-dir> is the installed folder containing this SKILL.md.)

bash
node "<skill-dir>/scripts/relay.mjs" --brief brief.txt --cd /path/to/repo
# choose a model:                          add --model <id from pi --list-models>
# choose a provider:                       add --provider <name>
# read-only run (review/diagnosis):        add --read-only
# trust project .pi resources:             add --approve
# resume the most recent session:          add --resume-last  (delta brief only)
# resume a specific session:               add --session <id> (delta brief only)
# hard time limit (watchdog):              add --timeout 2h  (the 30m default suits short runs; implementation briefs routinely need 1-2h)
# see all options:                         node .../relay.mjs --help

The child process'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 pi 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 pi exits 127 and writes status: "pi_unavailable".

Trust process state and the working tree over a progress display. Completion means the process exited and result.json exists. Pi'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

Treat pi's final message and gate claims as claims:

  • Re-run the project's gates yourself.
  • Read the diff against the brief, starting with touchedFiles.
  • Run relevant guard skills if installed.
  • Round-trip migrations and grep for dangling references after removals or renames.

See references/review-and-land.md.

5. Land it

The implementer edits the working tree; the orchestrator commits. Commit only after the gates pass and the diff holds. If rework is needed, send a delta brief with --resume-last or --session <id>, then review again.

Autonomy and permissions

Pi has no sandbox and no permission modes. A default headless run reads, writes, edits, and executes shell commands with no prompts - the controls are:

  1. --read-only restricts pi's callable tools to --tools read,grep,find,ls across built-in, extension, and custom tools. Installed extension code still runs with the user's host permissions.
  2. The relay passes --no-approve by default, so project .pi settings, extensions, and skills stay untrusted. --approve is the explicit opt-in for a repository the user trusts.
  3. touchedFiles and the diff are the record of what changed. Inspect them after every run.

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. Two limits remain: surface, don't absorb (report pi's design decisions, defensible-but-unasked turns, and non-blocking nitpicks) and stop for scope changes (if correct completion needs going beyond the brief, ask instead of expanding the mandate). See 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 Pi Delegate AI skill do?

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

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

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

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

Is the Pi 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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