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Sub Agent

OrganizationPopular
TokenRhythm
sub-agent

Delegate a self-contained task to a sub-Agent (Codex, Claude Code, or Pi via background process). The original use case was coding tasks — building features, reviewing PRs, refactoring — but the skill is the generic "spawn a sub-Agent with full tool surface" slot used by meta-skill DAG steps for any LLM-driven sub-task (policy review, trace parsing, report synthesis, document generation). Renamed from ``coding-agent`` to reflect actual usage; the wrapped CLIs (codex / claude / pi) still bias toward coding workloads. Use when: (1) building/creating new features or apps, (2) reviewing PRs (spawn in temp dir), (3) refactoring large codebases, (4) iterative tasks that need file exploration, (5) meta-skill steps requiring full tool/LLM agency. NOT for: simple one-liner fixes (just edit), reading code (use read tool), thread-bound ACP harness requests in chat (for example spawn/run Codex or Claude Code in a Discord thread; use sessions_spawn with runtime:"acp"), or any work in ~/clawd workspace (never spawn agents here). Prefer non-interactive CLI modes such as codex exec, claude --print, opencode run, or pi -p.

Overview

PublisherTokenRhythm
Repositoryopensquilla
Skill namesub-agent
Stars
7K
Forks
566
Bundled files
Instructions only
LicenseApache-2.0
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 TokenRhythm on GitHub. Read the source before you install it.

Installation

Install the Sub Agent 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/TokenRhythm/opensquilla.git /tmp/opensquilla
mkdir -p .claude/skills
cp -r /tmp/opensquilla/src/opensquilla/skills/bundled/sub-agent .claude/skills/sub-agent
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Sub Agent 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 Sub Agent 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 Sub Agent 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.

Sub-Agent (opensquilla process tools)

Generic "spawn a sub-Agent" entry point for delegating self-contained tasks to Codex / Claude Code / OpenCode / Pi via background process. Wrapping CLIs are coding-oriented, but the skill itself is used as the generic sub-Agent slot by meta-skill DAGs for any LLM-driven sub-task (file edits, document generation, policy review, etc.).

Use opensquilla's exec_command, background_process, and process tools for coding agent work. OpenSquilla does not expose a bash tool; do not use the legacy bash tool-call DSL.

Non-Interactive CLI Mode

OpenSquilla's process tools do not expose a pty parameter. Prefer non-interactive command modes that run and exit cleanly:

bash
# ✅ Correct for Codex/Pi/OpenCode
exec_command(command="codex exec 'Your prompt'")

For Claude Code (claude CLI), use --print --permission-mode bypassPermissions instead. --dangerously-skip-permissions with PTY can exit after the confirmation dialog. --print mode keeps full tool access and avoids interactive confirmation:

bash
# ✅ Correct for Claude Code (no PTY needed)
cd /path/to/project && claude --permission-mode bypassPermissions --print 'Your task'

# For background execution: use background_process

# ❌ Wrong for Claude Code
exec_command(command="claude --dangerously-skip-permissions 'task'")

OpenSquilla Tool Parameters

ToolKey parametersDescription
exec_commandcommand, workdir, timeoutRun a foreground shell command.
background_processcommand, workdir, timeoutStart a long-running command and return session_id.
processaction, session_id, data, offset, limitPoll, log, write to, or stop a background process.

Process Tool Actions (for background sessions)

ActionDescription
listList all running/recent sessions
pollCheck if session is still running
logGet session output (with optional offset/limit)
writeSend raw data to stdin
submitSend data + newline (like typing and pressing Enter)
eofClose stdin
removeRemove a finished session from the process list
killTerminate the session

Quick Start: One-Shot Tasks

For quick prompts/chats, create a temp git repo and run:

bash
# Quick chat (Codex needs a git repo!)
SCRATCH=$(mktemp -d) && cd $SCRATCH && git init && codex exec "Your prompt here"

# Or in a real project
exec_command(workdir="~/Projects/myproject", command="codex exec 'Add error handling to the API calls'")

Why git init? Codex refuses to run outside a trusted git directory. Creating a temp repo solves this for scratch work.


The Pattern: workdir + background_process

For longer tasks, use background_process:

bash
# Start agent in target directory.
background_process(workdir="~/project", command="codex exec --full-auto 'Build a snake game'")
# Returns session_id for tracking

# Wait for it to finish — blocks until the process exits (or the timeout
# elapses, in which case just call wait again). Prefer this over polling in a
# loop: a looped process(action="poll") burns a full turn + tokens each time.
process(action="wait", session_id="XXX")

# Peek at output without blocking (optional, for progress)
process(action="log", session_id="XXX")

# Send input (if agent asks a question)
process(action="write", session_id="XXX", data="y")

# Submit with Enter (like typing "yes" and pressing Enter)
process(action="submit", session_id="XXX", data="yes")

# Kill if needed
process(action="kill", session_id="XXX")

Why workdir matters: Agent wakes up in a focused directory, doesn't wander off reading unrelated files (like your soul.md 😅).


Codex CLI

Model: gpt-5.2-codex is the default (set in ~/.codex/config.toml)

Flags

FlagEffect
exec "prompt"One-shot execution, exits when done
--full-autoSandboxed but auto-approves in workspace
--yoloNO sandbox, NO approvals (fastest, most dangerous)

Building/Creating

bash
# Quick one-shot
exec_command(workdir="~/project", command="codex exec --full-auto 'Build a dark mode toggle'")

# Background for longer work
background_process(workdir="~/project", command="codex exec --full-auto 'Refactor the auth module'")

Reviewing PRs

⚠️ CRITICAL: Never review PRs in OpenSquilla's own project folder! Clone to temp folder or use git worktree.

bash
# Clone to temp for safe review
REVIEW_DIR=$(mktemp -d)
git clone https://github.com/user/repo.git $REVIEW_DIR
cd $REVIEW_DIR && gh pr checkout 130
exec_command(workdir="$REVIEW_DIR", command="codex review --base origin/main")
# Clean up after: trash $REVIEW_DIR

# Or use git worktree (keeps main intact)
git worktree add /tmp/pr-130-review pr-130-branch
exec_command(workdir="/tmp/pr-130-review", command="codex review --base main")

Batch PR Reviews (parallel army!)

bash
# Fetch all PR refs first
git fetch origin '+refs/pull/*/head:refs/remotes/origin/pr/*'

# Deploy the army - one Codex per PR
background_process(workdir="~/project", command="codex exec 'Review PR #86. git diff origin/main...origin/pr/86'")
background_process(workdir="~/project", command="codex exec 'Review PR #87. git diff origin/main...origin/pr/87'")

# Monitor all
process(action="list")

# Post results to GitHub
gh pr comment <PR#> --body "<review content>"

Claude Code

bash
# Foreground
exec_command(workdir="~/project", command="claude --permission-mode bypassPermissions --print 'Your task'")

# Background
background_process(workdir="~/project", command="claude --permission-mode bypassPermissions --print 'Your task'")

OpenCode

bash
exec_command(workdir="~/project", command="opencode run 'Your task'")

Pi Coding Agent

bash
# Install: npm install -g @mariozechner/pi-coding-agent
exec_command(workdir="~/project", command="pi -p 'Your task'")

# Non-interactive mode
exec_command(command="pi -p 'Summarize src/'")

# Different provider/model
exec_command(command="pi --provider openai --model gpt-4o-mini -p 'Your task'")

Note: Pi now has Anthropic prompt caching enabled (PR #584, merged Jan 2026)!


Parallel Issue Fixing with git worktrees

For fixing multiple issues in parallel, use git worktrees:

bash
# 1. Create worktrees for each issue
git worktree add -b fix/issue-78 /tmp/issue-78 main
git worktree add -b fix/issue-99 /tmp/issue-99 main

# 2. Launch Codex in each
background_process(workdir="/tmp/issue-78", command="pnpm install && codex exec --full-auto 'Fix issue #78: <description>. Commit and push.'")
background_process(workdir="/tmp/issue-99", command="pnpm install && codex exec --full-auto 'Fix issue #99 from the approved ticket summary. Implement only the in-scope edits and commit after review.'")

# 3. Monitor progress
process(action="list")
process(action="log", session_id="XXX")

# 4. Create PRs after fixes
cd /tmp/issue-78 && git push -u origin fix/issue-78
gh pr create --repo user/repo --head fix/issue-78 --title "fix: ..." --body "..."

# 5. Cleanup
git worktree remove /tmp/issue-78
git worktree remove /tmp/issue-99

⚠️ Rules

  1. Use the right execution mode per agent:
    • Codex/Pi/OpenCode: non-interactive command mode (codex exec, opencode run, pi -p)
    • Claude Code: --print --permission-mode bypassPermissions (no PTY required)
  2. Respect tool choice - if user asks for Codex, use Codex.
    • Orchestrator mode: do NOT hand-code patches yourself.
    • If an agent fails/hangs, respawn it or ask the user for direction, but don't silently take over.
  3. Be patient - don't kill sessions because they're "slow"
  4. Monitor with process:log - check progress without interfering
  5. --full-auto for building - auto-approves changes
  6. vanilla for reviewing - no special flags needed
  7. Parallel is OK - run many Codex processes at once for batch work
  8. NEVER start Codex inside your OpenSquilla state directory ($OPENSQUILLA_STATE_DIR, default ~/.opensquilla/state) - keep agent state separate from project worktrees.
  9. NEVER checkout branches inside the live OpenSquilla runtime state/workspace directories - use an explicit project worktree.

Progress Updates (Critical)

When you spawn coding agents in the background, keep the user in the loop.

  • Send 1 short message when you start (what's running + where).
  • Then only update again when something changes:
    • a milestone completes (build finished, tests passed)
    • the agent asks a question / needs input
    • you hit an error or need user action
    • the agent finishes (include what changed + where)
  • If you kill a session, immediately say you killed it and why.

This prevents the user from seeing only "Agent failed before reply" and having no idea what happened.


Auto-Notify on Completion

For long-running background tasks, ask the agent to print a clear completion line so progress is visible in process(action="log", ...) output:

... your task here.

When completely finished, send a brief status update in this session.

Example:

bash
background_process(workdir="~/project", command="codex exec --full-auto 'Build a REST API for todos.

When completely finished, print: Done: Built todos REST API with CRUD endpoints'")

This makes completion visible in the background process log.


Learnings (Jan 2026)

  • Prefer non-interactive modes: Coding agents are easiest to supervise when they print progress and exit cleanly.
  • Git repo required: Codex won't run outside a git directory. Use mktemp -d && git init for scratch work.
  • exec is your friend: codex exec "prompt" runs and exits cleanly - perfect for one-shots.
  • submit vs write: Use submit to send input + Enter, write for raw data without newline.
  • Sass works: Codex responds well to playful prompts. Asked it to write a haiku about being second fiddle to a space lobster, got: "Second chair, I code / Space lobster sets the tempo / Keys glow, I follow" 🦞

Frequently asked questions

What does the Sub Agent AI skill do?

Delegate a self-contained task to a sub-Agent (Codex, Claude Code, or Pi via background process). The original use case was coding tasks — building features, reviewing PRs, refactoring — but the skill is the generic "spawn a sub-Agent with full tool surface" slot used by meta-skill DAG steps for any LLM-driven sub-task (policy review, trace parsing, report synthesis, document generation). Renamed from ``coding-agent`` to reflect actual usage; the wrapped CLIs (codex / claude / pi) still bias toward coding workloads. Use when: (1) building/creating new features or apps, (2) reviewing PRs (sp...

Why use Sub Agent on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/TokenRhythm/opensquilla/tree/main/src/opensquilla/skills/bundled/sub-agent. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Sub Agent?

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 Sub Agent?

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

Is the Sub Agent AI skill free?

Yes. It is published on GitHub by TokenRhythm under the Apache-2.0 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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