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Coding Agent

Community
smallnest
coding-agent

Run Codex CLI, Claude Code, Kiro CLI, OpenCode, or Pi Coding Agent via background process for programmatic control.

Overview

Publishersmallnest
Repositorygoclaw
Skill namecoding-agent
Stars
599
Forks
119
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

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

Installation

Install the Coding 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.
    https://github.com/smallnest/goclaw/tree/master/internal/builtin_skills/coding-agent
  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/smallnest/goclaw.git /tmp/goclaw
mkdir -p .claude/skills
cp -r /tmp/goclaw/internal/builtin_skills/coding-agent .claude/skills/coding-agent
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Coding 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 Coding 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 Coding 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.

Coding Agent (bash-first)

Use bash (with optional background mode) for all coding agent work. Simple and effective.

⚠️ PTY Mode Required!

Coding agents (Codex, Claude Code, Kiro, Pi) are interactive terminal applications that need a pseudo-terminal (PTY) to work correctly. Without PTY, you'll get broken output, missing colors, or the agent may hang.

Always use pty:true when running coding agents:

bash
# ✅ Correct - with PTY
bash pty:true command:"codex exec 'Your prompt'"

# ❌ Wrong - no PTY, agent may break
bash command:"codex exec 'Your prompt'"

Bash Tool Parameters

ParameterTypeDescription
commandstringThe shell command to run
ptybooleanUse for coding agents! Allocates a pseudo-terminal for interactive CLIs
workdirstringWorking directory (agent sees only this folder's context)
backgroundbooleanRun in background, returns sessionId for monitoring
timeoutnumberTimeout in seconds (kills process on expiry)
elevatedbooleanRun on host instead of sandbox (if allowed)

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)
send-keysSend key tokens or hex bytes
pastePaste text (with optional bracketed mode)
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 - with PTY!
bash pty:true 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 + pty

For longer tasks, use background mode with PTY:

bash
# Start agent in target directory (with PTY!)
bash pty:true workdir:~/project background:true command:"codex exec --full-auto 'Build a snake game'"
# Returns sessionId for tracking

# Monitor progress
process action:log sessionId:XXX

# Check if done
process action:poll sessionId:XXX

# Send input (if agent asks a question)
process action:write sessionId:XXX data:"y"

# Submit with Enter (like typing "yes" and pressing Enter)
process action:submit sessionId:XXX data:"yes"

# Kill if needed
process action:kill sessionId: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 (auto-approves) - remember PTY!
bash pty:true workdir:~/project command:"codex exec --full-auto 'Build a dark mode toggle'"

# Background for longer work
bash pty:true workdir:~/project background:true command:"codex --yolo 'Refactor the auth module'"

Reviewing PRs

⚠️ CRITICAL: Never review PRs in OpenClaw'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 $REVIEW_DIR && gh pr checkout 130
bash pty:true workdir:$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
bash pty:true 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 (all with PTY!)
bash pty:true workdir:~/project background:true command:"codex exec 'Review PR #86. git diff origin/main...origin/pr/86'"
bash pty:true workdir:~/project background:true 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
# With PTY for proper terminal output
bash pty:true workdir:~/project command:"claude 'Your task'"

# Background
bash pty:true workdir:~/project background:true command:"claude 'Your task'"

Kiro CLI (AWS)

AWS AI coding assistant with session persistence, custom agents, steering, and MCP integration.

Install: https://kiro.dev/docs/cli/installation

Basic Usage

bash
kiro-cli                           # Start interactive chat (default)
kiro-cli chat "Your question"      # Direct question
kiro-cli --agent my-agent          # Use specific agent
kiro-cli chat --resume             # Resume last session (per-directory)
kiro-cli chat --resume-picker      # Pick from saved sessions
kiro-cli chat --list-sessions      # List all sessions

Non-Interactive Mode (scripting/automation)

bash
# Single response to STDOUT, then exit
kiro-cli chat --no-interactive "Show current directory"

# Trust all tools (no confirmation prompts)
kiro-cli chat --no-interactive --trust-all-tools "Create hello.py"

# Trust specific tools only (comma-separated)
kiro-cli chat --no-interactive --trust-tools "fs_read,fs_write" "Read package.json"

🔐 Tool Trust: Use --trust-all-tools for automation (default). For untrusted input or sensitive systems, consider --trust-tools "fs_read,fs_write,shell" to limit scope.

OpenClaw Integration

bash
# Interactive session (background)
bash pty:true workdir:~/project background:true command:"kiro-cli"

# One-shot query (non-interactive)
bash pty:true workdir:~/project command:"kiro-cli chat --no-interactive --trust-all-tools 'List all TODO comments in src/'"

# With specific agent
bash pty:true workdir:~/project background:true command:"kiro-cli --agent aws-expert 'Set up Lambda'"

# Resume previous session
bash pty:true workdir:~/project command:"kiro-cli chat --resume"

Custom Agents

Pre-define tool permissions, context resources, and behaviors:

bash
kiro-cli agent list              # List available agents
kiro-cli agent create my-agent   # Create new agent
kiro-cli agent edit my-agent     # Edit agent config
kiro-cli agent validate ./a.json # Validate config file
kiro-cli agent set-default my-agent  # Set default

Benefits: Pre-approve trusted tools, limit tool access, auto-load project docs, share configs across team.

Steering (Project Context)

Provide persistent project knowledge via markdown files in .kiro/steering/:

.kiro/steering/
├── product.md       # Product overview
├── tech.md          # Tech stack
├── structure.md     # Project structure
└── api-standards.md # API conventions
  • Workspace steering: .kiro/steering/ — applies to current project only
  • Global steering: ~/.kiro/steering/ — applies to all projects
  • AGENTS.md support: Place in project root or ~/.kiro/steering/

In custom agents: Add "resources": ["file://.kiro/steering/**/*.md"] to config.

MCP Integration

Connect external tools and data sources via Model Context Protocol:

bash
kiro-cli mcp add --name my-server --command "node server.js" --scope workspace
kiro-cli mcp list [workspace|global]
kiro-cli mcp status --name my-server
kiro-cli mcp remove --name my-server --scope workspace

Plan Agent

Plan Agent is a built-in agent for structured planning before execution. It helps transform ideas into detailed implementation plans.

When to suggest Plan Agent to user:

  • Complex multi-step features (e.g., "build a user authentication system")
  • Requirements are unclear or need clarification
  • Large features that benefit from task breakdown

When NOT to use Plan Agent:

  • Simple queries or single-step tasks
  • User already has clear, specific instructions
  • Quick fixes or small changes

How to use:

bash
# Start Plan mode
> /plan
# Or with immediate prompt
> /plan Build a REST API for user authentication
# Or toggle with keyboard shortcut
Shift + Tab

Plan workflow (4 phases):

  1. Requirements gathering — Structured questions with multiple choice options
  2. Research and analysis — Explores codebase, identifies patterns
  3. Implementation plan — Detailed task breakdown with clear objectives
  4. Handoff to execution — After approval (y), plan transfers to execution agent

Plan Agent limitations (read-only):

  • ✓ Can read files, search code, research documentation
  • ✗ Cannot write files or execute commands (until handoff)

OpenCode

bash
bash pty:true workdir:~/project command:"opencode run 'Your task'"

Pi Coding Agent

bash
# Install: npm install -g @mariozechner/pi-coding-agent
bash pty:true workdir:~/project command:"pi 'Your task'"

# Non-interactive mode (PTY still recommended)
bash pty:true command:"pi -p 'Summarize src/'"

# Different provider/model
bash pty:true 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 agent in each (background + PTY!)
bash pty:true workdir:/tmp/issue-78 background:true command:"pnpm install && codex --yolo 'Fix issue #78: <description>. Commit and push.'"
bash pty:true workdir:/tmp/issue-99 background:true command:"pnpm install && codex --yolo 'Fix issue #99: <description>. Commit and push.'"

# 3. Monitor progress
process action:list
process action:log sessionId: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. Always use pty:true - coding agents need a terminal!
  2. Respect tool choice - if user asks for Kiro, use Kiro; if they ask 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/--yolo for Codex building - auto-approves changes
  6. --trust-all-tools for Kiro automation - skips confirmation prompts; use --trust-tools for untrusted input
  7. --no-interactive for Kiro one-shots - single response mode
  8. Parallel is OK - run many agent processes at once for batch work
  9. NEVER start agents in ~/clawd/ - it'll read your soul docs and get weird ideas about the org chart!
  10. NEVER checkout branches in ~/Projects/openclaw/ - that's the LIVE OpenClaw instance!
  11. Suggest Kiro /plan for complex tasks - when requirements are unclear or task is multi-step, suggest Plan Agent to user and let them decide

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, append a wake trigger to your prompt so OpenClaw gets notified immediately when the agent finishes (instead of waiting for the next heartbeat):

... your task here.

When completely finished, run this command to notify me:
openclaw gateway wake --text "Done: [brief summary of what was built]" --mode now

Example (Codex):

bash
bash pty:true workdir:~/project background:true command:"codex --yolo exec 'Build a REST API for todos.

When completely finished, run: openclaw gateway wake --text \"Done: Built todos REST API with CRUD endpoints\" --mode now'"

This triggers an immediate wake event — gets pinged in seconds, not 10 minutes.


Learnings (Jan 2026)

  • PTY is essential: Coding agents are interactive terminal apps. Without pty:true, output breaks or agent hangs.
  • 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" 🦞

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 Coding Agent AI skill do?

Run Codex CLI, Claude Code, Kiro CLI, OpenCode, or Pi Coding Agent via background process for programmatic control.

Why use Coding Agent on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/smallnest/goclaw/tree/master/internal/builtin_skills/coding-agent. 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 Coding 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 Coding Agent?

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

Is the Coding Agent AI skill free?

Yes. It is published on GitHub by smallnest 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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