Delegating To Agents

Community

matyasstoch

delegating-to-agents

MUST be read ANY time another AI agent is involved — sending a prompt to or polling a Pi/Codex/Claude Code/Hermes/OpenCode agent, driving an agent running in another cmux pane/surface/terminal/tmux window, delegating or relaying ANYTHING to an agent in the same cmux workspace, spawning a sub-agent, or orchestrating agent-to-agent work. Read it BEFORE the first message you send to another agent. Covers the critical rule that prompts sent to a TUI agent must be ONE single line (newlines = Enter = fragmented/cut-off prompts), short polling cadence, which agent to pick, and per-agent characteristics. If you are about to type `cmux send`/`tmux send-keys` to any agent, this skill applies.

Overview

Publisher	matyasstoch
Repository	`david-skills`
Skill name	`delegating-to-agents`
Stars	61
Forks	11
Bundled files	Instructions only
Links	Skill source Repository

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 matyasstoch on GitHub. Read the source before you install it.

Installation

Install the Delegating To Agents AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/matyasstoch/david-skills/tree/main/skills/delegating-to-agents
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.

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/matyasstoch/david-skills.git /tmp/david-skills
mkdir -p .claude/skills
cp -r /tmp/david-skills/skills/delegating-to-agents .claude/skills/delegating-to-agents

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Delegating To Agents 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 Delegating To Agents 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 Delegating To Agents 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.

Delegating to Agents

Which agent to pick (most important)

For most tasks → just open Pi Agent with the pi command in a cmux terminal. Default Pi agents run on opus-4.8-fast via OpenRouter API at xhigh reasoning effort.
Default for coding → Codex CLI. It's currently the most powerful coding agent — stronger than Claude Code and Opus 4.8 — especially on complex, long-running SWE tasks. Delegate hard/multi-step engineering work here.
Frontend / design → Pi Agent with Opus 4.8 Fast. For UI, styling, and design work, Pi + Opus 4.8 Fast beats Codex.
Codex is on David's ChatGPT Pro plan, so usage limits are effectively near-unlimited — don't ration Codex calls to save quota.
A good setup for complex work: Pi Agent (you) as the orchestrator, delegating execution to Codex CLI running in the right pane in cmux. This isn't the only setup that works, but it's a solid default for heavy, long-running tasks.

Polling cadence — keep sleeps SHORT

General principle: when waiting on another agent, use short sleep intervals so you check often. Don't sleep 30. Start with sleep 3-5; if the agent isn't done, just sleep 5 again and re-check. Scale up only for genuinely heavy tasks.

Sending prompts — NEVER put newlines in the message body

When sending a prompt to a TUI agent (Pi, Hermes, Claude Code, Codex) via cmux send, tmux send-keys, or similar, the message text must be a single line. In these TUIs a newline = Enter, so a multi-line string submits after the first line and the rest arrive as separate mid-turn "Steering" messages — cutting off / fragmenting your prompt.

Fixes (pick one):

Send the whole prompt as one line (use ". " or "; " instead of line breaks), then one explicit send-key enter.
For long/multi-step instructions, write them to a file and tell the agent to read it: cmux send --surface X 'read /tmp/task.md and follow it' then send-key enter.

Managing a remote VPS via an agent

To have an agent manage a remote VPS, SSH in first and launch the agent ON the VPS (e.g. codex --yolo), then drive that on-box agent — it has full local context and avoids fragile per-command SSH round-trips. Don't run an agent locally and tell it to SSH for every step.

Agent characteristics

Pi Agent — starts & responds very fast. Usually launches within 1-2s and responds within a few seconds. No need for long waits; sleep 3-5 is plenty.
Pi & Hermes — both run on OpenRouter with opus-4.8-fast, which responds very fast. Same rule: poll with short 3-5s sleeps, not long ones.

The 4 agents (compressed reference)

Background only — don't over-index on this. All four use the portable SKILL.md standard (~/.{agent}/skills/, project version wins; one folder can be symlinked across all). Pi, Codex, Hermes are model-agnostic (BYOK); Claude Code is Claude-centric.

Pi (pi.dev, Mario Zechner / earendil-works, open-source TS, npm). Minimal 4-tool core (read/write/edit/bash) that self-extends via TS extensions, skills, prompt templates, packages. True BYOK (Claude/GPT/Gemini/Grok/DeepSeek/local), best-in-class branch/fork/resume sessions, transparent. Weak: smaller ecosystem, assemble-your-own workflow. Skills: ~/.pi/agent/skills/.
Hermes (Nous Research, MIT, Python). Persistent self-improving autonomous agent (not a copilot) — cross-session memory, learning loop that auto-writes reusable skills, 40+ tools, cron/scheduling, subagent delegation, 20+ messaging platforms. Can orchestrate other agents (Codex, OpenCode) as workers. Weak: heavy setup (systemd/server), not IDE-tethered. Skills: ~/.hermes/skills/ (project cwd skills/ wins).
Claude Code (Anthropic, TS). Deepest model integration, clean .claude/ conventions (CLAUDE.md, rules/, agents/ subagents, skills/plugins), live skill hot-reload mid-session. Skills are injected instructions in the main conversation, not separate processes. Weak: Anthropic-centric, less infra/sandbox tooling. Skills: ~/.claude/skills/ (personal) + .claude/skills/ (project).
Codex CLI (OpenAI, Apache 2.0, ~95% Rust). Fastest startup, low memory; only agent with kernel-level sandboxing (Apple Seatbelt / Landlock+seccomp); strong for CI (codex exec), MCP, GitHub PR review. Reads AGENTS.md. Weak: tied to ChatGPT/OpenAI auth, sparse docs, sandbox can change script behavior. Skills: ~/.codex/skills/ (personal) + .codex/skills/ (project).

Driving interactive CLIs

Codex, Pi, OpenCode: tooling that drives them needs pty=true.
Claude Code: prefers --print --permission-mode bypassPermissions (no PTY).
Hermes is the orchestrator/persistent agent — it can drive the coding CLIs, not just be one.

Frequently asked questions

What does the Delegating To Agents AI skill do?

Why use Delegating To Agents on TypingMind?

Because you install it once and use it with any model. Delegating To Agents 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 Delegating To Agents in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/matyasstoch/david-skills/tree/main/skills/delegating-to-agents. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Delegating To Agents?

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 Delegating To Agents?

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

Is the Delegating To Agents AI skill free?

It is published on GitHub by matyasstoch. Check the repository for licensing terms. 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Delegating to Agents

Which agent to pick (most important)

Polling cadence — keep sleeps SHORT

Sending prompts — NEVER put newlines in the message body

Managing a remote VPS via an agent

Agent characteristics

The 4 agents (compressed reference)

Driving interactive CLIs

Frequently asked questions

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