Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Run Codex CLI, Claude Code, Kiro CLI, OpenCode, or Pi Coding Agent via background process for programmatic control.
Run Codex CLI, Claude Code, Kiro CLI, OpenCode, or Pi Coding Agent via background process for programmatic control.
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete.
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run.
Launch and manage AI coding agents (Codex, Claude Code, Kiro CLI, OpenCode, Pi) from OpenClaw using bash with background process control.
Coding agents are interactive terminal applications that need a pseudo-terminal (PTY). Without it, output breaks or the agent hangs. Always set pty:true: # Correct โ with PTY bash pty:true command:"codex exec 'Your prompt'" # Wrong โ agent may break or hang bash command:"codex exec 'Your prompt'"
ParameterTypeDescriptioncommandstringShell command to runptybooleanAllocate a pseudo-terminal (required for coding agents)workdirstringWorking directory (agent sees only this folder)backgroundbooleanRun in background; returns sessionId for monitoringtimeoutnumberTimeout in seconds (kills process on expiry)elevatedbooleanRun on host instead of sandbox (if allowed)
ActionDescriptionlistList all running/recent sessionspollCheck if a session is still runninglogGet session output (optional offset/limit)writeSend raw data to stdin (no newline)submitSend data + newline (like typing and pressing Enter)send-keysSend key tokens or hex bytespastePaste text (optional bracketed mode)killTerminate the session
# Codex needs a git repo โ create a temp one for scratch work SCRATCH=$(mktemp -d) && cd $SCRATCH && git init && codex exec "Your prompt" # Run inside an existing project (with PTY) bash pty:true workdir:~/project command:"codex exec 'Add error handling to the API calls'"
For longer tasks, combine all three: # 1. Start the agent bash pty:true workdir:~/project background:true command:"codex exec --full-auto 'Build a snake game'" # โ returns sessionId # 2. Monitor progress process action:log sessionId:XXX # 3. Check completion process action:poll sessionId:XXX # 4. Send input if the agent asks a question process action:submit sessionId:XXX data:"yes" # text + Enter process action:write sessionId:XXX data:"y" # raw keystroke # 5. Kill if stuck process action:kill sessionId:XXX Why workdir? The agent starts in a focused directory and won't wander into unrelated files.
Default model: gpt-5.2-codex (set in ~/.codex/config.toml)
FlagEffectexec "prompt"One-shot execution, exits when done--full-autoSandboxed, auto-approves within workspace--yoloNo sandbox, no approvals (fastest, most dangerous)
# One-shot with auto-approve bash pty:true workdir:~/project command:"codex exec --full-auto 'Build a dark mode toggle'" # Background for longer work bash pty:true workdir:~/project background:true command:"codex --yolo 'Refactor the auth module'"
Never review PRs in OpenClaw's own project folder. Clone to a temp directory or use a git worktree. # Clone to temp REVIEW_DIR=$(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" # 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"
# Fetch all PR refs git fetch origin '+refs/pull/*/head:refs/remotes/origin/pr/*' # Launch one Codex per PR (all background + 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 process action:list # Post results gh pr comment <PR#> --body "<review content>"
# One-shot bash pty:true workdir:~/project command:"claude 'Your task'" # Background bash pty:true workdir:~/project background:true command:"claude 'Your task'"
AWS AI coding assistant with session persistence, custom agents, skills, hooks, steering, subagents, planning mode, and MCP integration. Install: https://kiro.dev/docs/cli/installation
kiro-cli # Interactive chat (default) kiro-cli chat "Your question" # Direct question kiro-cli --agent my-agent # Use a 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
For scripting and automation โ outputs a single response to STDOUT, then exits. # Single response 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 kiro-cli chat --no-interactive --trust-tools "fs_read,fs_write" "Read package.json" Tool trust: --trust-all-tools for full automation. For untrusted input, use --trust-tools "fs_read,fs_write,shell" to limit scope.
# 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 a 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"
Skills are portable instruction packages that extend what Kiro knows. When a request matches a skill's description, Kiro automatically loads and follows its instructions โ no slash command needed. Skill Locations LocationScopeNotes.kiro/skills/<name>/Workspace (project)Shared via version control~/.kiro/skills/<name>/Global (all projects)Personal workflows Workspace skills take priority when names collide. Creating a Skill A skill is a folder with a SKILL.md file: my-skill/ โโโ SKILL.md # Required โ frontmatter + instructions โโโ references/ # Optional โ detailed docs loaded on demand โโโ guide.md SKILL.md format: --- name: pr-review description: Review pull requests for code quality, security issues, and test coverage. --- ## Review checklist 1. Check for vulnerabilities, injection risks, exposed secrets 2. Verify edge cases and failure modes are handled 3. Confirm new code has appropriate tests For detailed patterns, see `references/guide.md`. name โ Unique identifier for the skill. description โ Determines when Kiro activates the skill. Be specific; include keywords that match how you'd phrase requests. Reference files โ Stored in references/. Kiro loads them only when the instructions direct it to. Skills in Custom Agents The default agent auto-discovers skills. Custom agents need explicit resource declarations: { "name": "my-agent", "resources": [ "skill://.kiro/skills/*/SKILL.md", "skill://~/.kiro/skills/*/SKILL.md" ] } Skill Best Practices Precise descriptions โ "Review pull requests for security vulnerabilities and test coverage" activates reliably; "Helps with code review" does not. Keep SKILL.md actionable โ Put lengthy reference material in references/ files. Right scope โ Global skills for personal workflows; workspace skills for team/project conventions. Version control โ Commit .kiro/skills/ so the team shares the same workflows. Check availability โ Use /context show to see which skills are loaded in the current session.
Plan Agent is a built-in read-only agent for structured planning before execution. It transforms ideas into detailed implementation plans through an interactive workflow. When to Use Complex multi-step features (e.g., "build a user authentication system") Unclear or evolving requirements that need refinement Large features that benefit from task breakdown before coding When NOT to Use Simple queries or single-step tasks User already has clear, specific instructions Quick fixes or small changes How to Enter Planning Mode # Slash command > /plan # With an immediate prompt > /plan Build a REST API for user authentication # Keyboard shortcut (toggles plan โ execution) Shift + Tab When active, the prompt shows a [plan] indicator. Plan Workflow (4 phases) Requirements gathering โ Structured multiple-choice questions to refine your idea. Answer with 1=a, 2=b syntax or free-text. Research & analysis โ Explores your codebase using code intelligence, grep, and glob tools. Implementation plan โ Produces a task breakdown with clear objectives, implementation guidance, and demo descriptions for each task. Approval & handoff โ You review the plan. On approval (y), the plan transfers automatically to the execution agent. Plan Agent is read-only: it can read files, search code, and research, but cannot write files or execute commands until handoff. OpenClaw Integration for Planning Mode For interactive planning sessions, run Kiro in background mode and relay the /plan command: # Start interactive Kiro session bash pty:true workdir:~/project background:true command:"kiro-cli chat --trust-all-tools" # Enter planning mode process action:submit sessionId:XXX data:"/plan Build a REST API for user authentication" # Relay the user's answers to requirement questions process action:submit sessionId:XXX data:"1=a, 2=d I'm using Rust with Axum" # Approve the plan process action:submit sessionId:XXX data:"y" # Monitor output process action:log sessionId:XXX Example Planning Session > /plan Add user authentication to my web app [plan] > I understand you want to add user authentication. [1]: What authentication method? a. Email/Password b. OAuth c. Magic Links d. Multi-factor > 1=a [plan] > Great! Email/password it is. [2]: What's your tech stack? a. React + Node.js b. Next.js c. Django/Flask d. Other > 2=d, I'm using Rust with Axum [plan] > Researching Axum authentication patterns... **Implementation Plan โ User Authentication System** [Detailed task breakdown...] Does this plan look good? Ready to exit [plan] agent? [y/n]: y [default] > Implement this plan: [Plan transferred]
Hooks execute custom commands at specific points during agent lifecycle and tool execution. Defined in the agent configuration file. Hook Types HookTriggerCan Block?AgentSpawnAgent startsNoUserPromptSubmitUser sends a promptNoPreToolUseBefore a tool runsYes (exit code 2)PostToolUseAfter a tool runsNoStopAgent finishes a turnNo Exit Codes 0 โ Success. STDOUT captured (added to context for AgentSpawn/UserPromptSubmit). 2 โ (PreToolUse only) Block tool execution; STDERR returned to the LLM. Other โ Failure. STDERR shown as warning to user. Tool Matching Use the matcher field to target specific tools: MatcherMatches"fs_write" or "write"Write tool"execute_bash" or "shell"Shell execution"@git"All tools from git MCP server"@git/status"Specific MCP tool"*"All tools (built-in + MCP)"@builtin"Built-in tools only Configuration timeout_ms โ Default 30,000ms (30s). cache_ttl_seconds โ 0 = no caching (default); > 0 = cache successful results. AgentSpawn hooks are never cached. See Agent Configuration Reference for full syntax.
Kiro can delegate tasks to subagents โ independent agents with their own context that run autonomously and return results. > Use the backend agent to refactor the payment module Key capabilities: Autonomous execution with isolated context Live progress tracking Parallel execution for multiple tasks Custom agent configurations for specialized workflows Available tools in subagents: read, write, shell, code intelligence, MCP tools. Not available: web_search, web_fetch, use_aws, grep, glob, thinking, todo_list.
Pre-define tool permissions, context resources, and behaviors: 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 Benefits: Pre-approved tool trust, limited tool access, auto-loaded project docs, shareable team configs.
Provide persistent project knowledge via markdown files: PathScope.kiro/steering/Workspace โ this project only~/.kiro/steering/Global โ all projects Example structure: .kiro/steering/ โโโ product.md # Product overview โโโ tech.md # Tech stack โโโ structure.md # Project structure โโโ api-standards.md # API conventions Also supports AGENTS.md in the project root or ~/.kiro/steering/. In custom agents: Add "resources": ["file://.kiro/steering/**/*.md"] to config.
Connect external tools and data sources via Model Context Protocol: 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
bash pty:true workdir:~/project command:"opencode run 'Your task'"
# Install: npm install -g @mariozechner/pi-coding-agent # Interactive bash pty:true workdir:~/project command:"pi 'Your task'" # Non-interactive (single response) 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'"
Fix multiple issues simultaneously using isolated worktrees: # 1. Create worktrees git worktree add -b fix/issue-78 /tmp/issue-78 main git worktree add -b fix/issue-99 /tmp/issue-99 main # 2. Launch agents (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 process action:list process action:log sessionId:XXX # 4. Create PRs 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. Clean up git worktree remove /tmp/issue-78 git worktree remove /tmp/issue-99
Always use pty:true โ Coding agents need a pseudo-terminal. Respect tool choice โ If the user asks for Kiro, use Kiro; for Codex, use Codex. Do NOT hand-code patches yourself when orchestrating agents. If an agent fails or hangs, respawn it or ask the user โ don't silently take over. Be patient โ Don't kill sessions just because they seem slow. Monitor with process:log โ Check progress without interfering. Codex auto-approve flags โ Use --full-auto (sandboxed) or --yolo (no sandbox) for building tasks. Kiro tool trust โ Use --trust-all-tools for automation; --trust-tools for restricted scope. Kiro one-shots โ Use --no-interactive for single-response queries. Parallel is OK โ Run multiple agent processes concurrently for batch work. Never start agents in ~/clawd/ โ Agents will read system docs and behave unpredictably. Never checkout branches in ~/Projects/openclaw/ โ That's the live OpenClaw instance. Suggest Kiro /plan for complex tasks โ When requirements are unclear or multi-step, suggest Plan Agent and let the user decide. Leverage Kiro skills โ If the project has .kiro/skills/ or the user has ~/.kiro/skills/, relevant skills activate automatically. No extra flags needed.
When you spawn coding agents in the background, keep the user informed: On start โ 1 short message: what's running, where, and which agent. On change โ Update only when something happens: A milestone completes (build finished, tests passed) The agent asks a question or needs input An error occurs or user action is needed The agent finishes (include what changed and where) On kill โ Immediately say you killed it and why.
For long-running tasks, append a wake trigger so OpenClaw is notified immediately when the agent finishes: ... your task here. When completely finished, run this command to notify me: openclaw gateway wake --text "Done: [brief summary]" --mode now Example: 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 instead of waiting for the next heartbeat.
PTY is essential โ Without pty:true, output breaks or the agent hangs. Git repo required for Codex โ Use mktemp -d && git init for scratch work. exec for one-shots โ codex exec "prompt" runs and exits cleanly. submit vs write โ submit sends input + Enter; write sends raw data without newline. Skills activate automatically โ No slash command needed; Kiro matches your request against skill descriptions. Plan before complex builds โ /plan saves time on multi-step features by clarifying requirements upfront.
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Largest current source with strong distribution and engagement signals.