Claim this agent
Agent DossierCLAWHUBSafety 84/100

Xpersona Agent

ralph-loop

Guide OpenClaw agents to execute Ralph Wiggum loops using exec and process tools. Agent orchestrates coding agents (Codex, Claude Code, OpenCode, Goose) with proper TTY support via pty:true. Plans/builds code via PROMPT.md + AGENTS.md, SPECS and IMPLEMENTATION_PLAN.md. Includes PLANNING vs BUILDING modes, backpressure, sandboxing, and completion conditions. Users request loops, agents execute using tools. --- name: ralph-loop description: Guide OpenClaw agents to execute Ralph Wiggum loops using exec and process tools. Agent orchestrates coding agents (Codex, Claude Code, OpenCode, Goose) with proper TTY support via pty:true. Plans/builds code via PROMPT.md + AGENTS.md, SPECS and IMPLEMENTATION_PLAN.md. Includes PLANNING vs BUILDING modes, backpressure, sandboxing, and completion conditions. Users request loops, agent

OpenClaw · self-declared
Trust evidence available
clawhub skill install skills:addozhang:ralph-loop-agent

Overall rank

#62

Adoption

No public adoption signal

Trust

Unknown

Freshness

Feb 25, 2026

Freshness

Last checked Feb 25, 2026

Best For

ralph-loop is best for key workflows where OpenClaw compatibility matters.

Not Ideal For

Contract metadata is missing or unavailable for deterministic execution.

Evidence Sources Checked

editorial-content, CLAWHUB, runtime-metrics, public facts pack

Overview

Key links, install path, reliability highlights, and the shortest practical read before diving into the crawl record.

Verifiededitorial-content

Overview

Executive Summary

Guide OpenClaw agents to execute Ralph Wiggum loops using exec and process tools. Agent orchestrates coding agents (Codex, Claude Code, OpenCode, Goose) with proper TTY support via pty:true. Plans/builds code via PROMPT.md + AGENTS.md, SPECS and IMPLEMENTATION_PLAN.md. Includes PLANNING vs BUILDING modes, backpressure, sandboxing, and completion conditions. Users request loops, agents execute using tools. --- name: ralph-loop description: Guide OpenClaw agents to execute Ralph Wiggum loops using exec and process tools. Agent orchestrates coding agents (Codex, Claude Code, OpenCode, Goose) with proper TTY support via pty:true. Plans/builds code via PROMPT.md + AGENTS.md, SPECS and IMPLEMENTATION_PLAN.md. Includes PLANNING vs BUILDING modes, backpressure, sandboxing, and completion conditions. Users request loops, agent Capability contract not published. No trust telemetry is available yet. Last updated 4/15/2026.

No verified compatibility signals

Trust score

Unknown

Compatibility

OpenClaw

Freshness

Feb 25, 2026

Vendor

Openclaw

Artifacts

0

Benchmarks

0

Last release

Unpublished

Install & run

Setup Snapshot

clawhub skill install skills:addozhang:ralph-loop-agent
  1. 1

    Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.

  2. 2

    Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Evidence & Timeline

Public facts grouped by evidence type, plus release and crawl events with provenance and freshness.

Verifiededitorial-content

Public facts

Evidence Ledger

Vendor (1)

Vendor

Openclaw

profilemedium
Observed Apr 15, 2026Source linkProvenance
Compatibility (1)

Protocol compatibility

OpenClaw

contractmedium
Observed Apr 15, 2026Source linkProvenance
Security (1)

Handshake status

UNKNOWN

trustmedium
Observed unknownSource linkProvenance
Integration (1)

Crawlable docs

6 indexed pages on the official domain

search_documentmedium
Observed Apr 15, 2026Source linkProvenance

Artifacts & Docs

Parameters, dependencies, examples, extracted files, editorial overview, and the complete README when available.

Self-declaredCLAWHUB

Captured outputs

Artifacts Archive

Extracted files

0

Examples

6

Snippets

0

Languages

typescript

Parameters

Executable Examples

text

exec tool with parameters:
   - command: "opencode run --model <MODEL> \"$(cat PROMPT.md)\""
   - workdir: <project_path>
   - background: true
   - pty: true
   - yieldMs: 60000
   - timeout: 3600

text

process tool with:
   - action: "poll"
   - sessionId: <captured_session_id>
   
   process tool with:
   - action: "log"
   - sessionId: <captured_session_id>
   - offset: -30  (for recent output)

text

process tool with:
   - action: "kill"
   - sessionId: <session_id>

text

You are running a Ralph PLANNING loop for this goal: <goal>.

Read specs/* and the current codebase. Only update IMPLEMENTATION_PLAN.md.

Rules:
- Do not implement
- Do not commit
- Create a prioritized task list
- Write down questions if unclear

Completion:
When plan is ready, add: STATUS: PLANNING_COMPLETE

text

You are running a Ralph BUILDING loop for this goal: <goal>.

Context: specs/*, IMPLEMENTATION_PLAN.md, AGENTS.md

Tasks:
1) Pick the most important task
2) Investigate code
3) Implement
4) Run backpressure commands from AGENTS.md
5) Update IMPLEMENTATION_PLAN.md
6) Update AGENTS.md with learnings
7) Commit with clear message

Completion:
When all done, add: STATUS: COMPLETE

text

Step 1: Launch OpenCode with exec tool
{
  command: "opencode run --model github-copilot/claude-opus-4.5 \"$(cat PROMPT.md)\"",
  workdir: "/path/to/project",
  background: true,
  pty: true,
  timeout: 3600,
  yieldMs: 60000
}

Step 2: Capture session ID from response
sessionId: "abc123"

Step 3: Monitor with process tool every 10-30 seconds
{
  action: "poll",
  sessionId: "abc123"
}

Step 4: Check recent logs
{
  action: "log",
  sessionId: "abc123",
  offset: -30
}

Step 5: Read IMPLEMENTATION_PLAN.md to check for completion
- Look for: "STATUS: COMPLETE" or "STATUS: PLANNING_COMPLETE"

Step 6: If complete or timeout, cleanup
{
  action: "kill",
  sessionId: "abc123"
}

Editorial read

Docs & README

Docs source

CLAWHUB

Editorial quality

ready

Guide OpenClaw agents to execute Ralph Wiggum loops using exec and process tools. Agent orchestrates coding agents (Codex, Claude Code, OpenCode, Goose) with proper TTY support via pty:true. Plans/builds code via PROMPT.md + AGENTS.md, SPECS and IMPLEMENTATION_PLAN.md. Includes PLANNING vs BUILDING modes, backpressure, sandboxing, and completion conditions. Users request loops, agents execute using tools. --- name: ralph-loop description: Guide OpenClaw agents to execute Ralph Wiggum loops using exec and process tools. Agent orchestrates coding agents (Codex, Claude Code, OpenCode, Goose) with proper TTY support via pty:true. Plans/builds code via PROMPT.md + AGENTS.md, SPECS and IMPLEMENTATION_PLAN.md. Includes PLANNING vs BUILDING modes, backpressure, sandboxing, and completion conditions. Users request loops, agent

Full README

name: ralph-loop description: Guide OpenClaw agents to execute Ralph Wiggum loops using exec and process tools. Agent orchestrates coding agents (Codex, Claude Code, OpenCode, Goose) with proper TTY support via pty:true. Plans/builds code via PROMPT.md + AGENTS.md, SPECS and IMPLEMENTATION_PLAN.md. Includes PLANNING vs BUILDING modes, backpressure, sandboxing, and completion conditions. Users request loops, agents execute using tools. version: 1.1.0 author: OpenClaw Community keywords: [ralph-loop, ai-agent, coding-agent, pty, tty, automation, loop, opencode, codex, claude, goose, exec-tool, process-tool] license: MIT

Ralph Loop

Overview

This skill guides OpenClaw agents to execute Ralph Loop workflows using the exec and process tools. The agent orchestrates AI coding agent sessions following the Ralph playbook flow:

  1. Define Requirements → JTBD → Focus Topics → specs/*.md
  2. PLANNING Loop → Create/update IMPLEMENTATION_PLAN.md (do not implement)
  3. BUILDING Loop → Implement tasks, run tests (backpressure), update plan, commit

The loop persists context via PROMPT.md + AGENTS.md (loaded each iteration) and the plan/specs on disk.

How This Skill Works

This skill generates instructions for OpenClaw agents to execute Ralph Loops using the exec and process tools.

  • The agent calls exec tool with the coding agent command
  • Uses pty: true to provide TTY for interactive CLIs
  • Uses background: true for monitoring capabilities
  • Uses process tool to monitor progress and detect completion

Important: Users don't run these scripts directly - the OpenClaw agent executes them using its tool capabilities.


TTY Requirements

Some coding agents require a real terminal (TTY) to work properly, or they will hang:

Interactive CLIs (need TTY):

  • OpenCode, Codex, Claude Code, Pi, Goose

Non-interactive CLIs (file-based):

  • aider, custom scripts

Solution: Use exec + process mode for interactive CLIs, simple loops for file-based tools.


Agent Tool Usage Patterns

Interactive CLIs (Recommended Pattern)

For OpenCode, Codex, Claude Code, Pi, and Goose - these require TTY support:

When I (the agent) receive a Ralph Loop request, I will:

  1. Use exec tool to launch the coding agent:

    exec tool with parameters:
    - command: "opencode run --model <MODEL> \"$(cat PROMPT.md)\""
    - workdir: <project_path>
    - background: true
    - pty: true
    - yieldMs: 60000
    - timeout: 3600
    
  2. Capture session ID from exec tool response

  3. Use process tool to monitor:

    process tool with:
    - action: "poll"
    - sessionId: <captured_session_id>
    
    process tool with:
    - action: "log"
    - sessionId: <captured_session_id>
    - offset: -30  (for recent output)
    
  4. Check completion by reading IMPLEMENTATION_PLAN.md for sentinel text

  5. Clean up with process kill if needed:

    process tool with:
    - action: "kill"
    - sessionId: <session_id>
    

Benefits: TTY support, real-time logs, timeout handling, parallel sessions, workdir isolation


Agent Workflow

1) Gather Inputs

Required:

  • Goal / JTBD
  • CLI (opencode, codex, claude, goose, pi, other)
  • Mode (PLANNING, BUILDING, or BOTH)
  • Max iterations (default: PLANNING=5, BUILDING=10)

Optional:

  • Completion sentinel (default: STATUS: COMPLETE in IMPLEMENTATION_PLAN.md)
  • Working directory (default: $PWD)
  • Timeout per iteration (default: 3600s)
  • Sandbox choice
  • Auto-approval flags (--full-auto, --yolo, --dangerously-skip-permissions)

Auto-detect:

  • If CLI in interactive list → use exec tool with pty: true
  • Extract model flag from CLI requirements

2) Requirements → Specs (Optional)

If requirements are unclear:

  • Break JTBD into focus topics
  • Draft specs/<topic>.md for each
  • Keep specs short and testable

3) PROMPT.md + AGENTS.md

PROMPT.md references:

  • specs/*.md
  • IMPLEMENTATION_PLAN.md
  • Relevant project files

AGENTS.md includes:

  • Test commands (backpressure)
  • Build/run instructions
  • Operational learnings

4) Prompt Templates

PLANNING Prompt (no implementation):

You are running a Ralph PLANNING loop for this goal: <goal>.

Read specs/* and the current codebase. Only update IMPLEMENTATION_PLAN.md.

Rules:
- Do not implement
- Do not commit
- Create a prioritized task list
- Write down questions if unclear

Completion:
When plan is ready, add: STATUS: PLANNING_COMPLETE

BUILDING Prompt:

You are running a Ralph BUILDING loop for this goal: <goal>.

Context: specs/*, IMPLEMENTATION_PLAN.md, AGENTS.md

Tasks:
1) Pick the most important task
2) Investigate code
3) Implement
4) Run backpressure commands from AGENTS.md
5) Update IMPLEMENTATION_PLAN.md
6) Update AGENTS.md with learnings
7) Commit with clear message

Completion:
When all done, add: STATUS: COMPLETE

5) CLI Command Reference

The agent constructs command strings using these patterns:

| CLI | Command String Pattern | |-----|----------------------| | OpenCode | opencode run --model <MODEL> "$(cat PROMPT.md)" | | Codex | codex exec <FLAGS> "$(cat PROMPT.md)" (requires git) | | Claude Code | claude <FLAGS> "$(cat PROMPT.md)" | | Pi | pi --provider <PROVIDER> --model <MODEL> -p "$(cat PROMPT.md)" | | Goose | goose run "$(cat PROMPT.md)" |

Common flags:

  • Codex: --full-auto, --yolo, --model <model>
  • Claude: --dangerously-skip-permissions

Detailed Agent Tool Usage Examples

Example 1: OpenCode Ralph Loop

Agent executes this sequence:

Step 1: Launch OpenCode with exec tool
{
  command: "opencode run --model github-copilot/claude-opus-4.5 \"$(cat PROMPT.md)\"",
  workdir: "/path/to/project",
  background: true,
  pty: true,
  timeout: 3600,
  yieldMs: 60000
}

Step 2: Capture session ID from response
sessionId: "abc123"

Step 3: Monitor with process tool every 10-30 seconds
{
  action: "poll",
  sessionId: "abc123"
}

Step 4: Check recent logs
{
  action: "log",
  sessionId: "abc123",
  offset: -30
}

Step 5: Read IMPLEMENTATION_PLAN.md to check for completion
- Look for: "STATUS: COMPLETE" or "STATUS: PLANNING_COMPLETE"

Step 6: If complete or timeout, cleanup
{
  action: "kill",
  sessionId: "abc123"
}

Example 2: Codex with Full Auto

Agent tool calls:

exec tool:
{
  command: "codex exec --full-auto --model anthropic/claude-opus-4 \"$(cat PROMPT.md)\"",
  workdir: "/path/to/project",
  background: true,
  pty: true,
  timeout: 3600
}

# Then monitor with process tool as above

Completion Detection

Use flexible regex to match variations:

grep -Eq "STATUS:?\s*(PLANNING_)?COMPLETE" IMPLEMENTATION_PLAN.md

Matches:

  • STATUS: COMPLETE
  • STATUS:COMPLETE
  • STATUS: PLANNING_COMPLETE
  • ## Status: PLANNING_COMPLETE

Safety & Safeguards

Auto-Approval Flags (Risky!)

  • Codex: --full-auto (sandboxed, auto-approve) or --yolo (no sandbox!)
  • Claude: --dangerously-skip-permissions
  • Recommendation: Use sandboxes (docker/e2b/fly) and limited credentials

Escape Hatches

  • Stop: Ctrl+C
  • Kill session: process tool with action: "kill"
  • Rollback: git reset --hard HEAD~N

Best Practices

  1. Start small: Test with 1-2 iterations first
  2. Workdir isolation: Prevent reading unrelated files
  3. Set timeouts: Default 1h may not fit all tasks
  4. Monitor actively: Check logs, don't terminate prematurely
  5. Requirements first: Clear specs before building
  6. Backpressure early: Add tests from the start

Troubleshooting

| Problem | Solution | |---------|----------| | OpenCode hangs | Ensure agent uses exec tool with pty: true | | Session won't start | Check CLI path, git repo, command syntax | | Completion not detected | Verify sentinel format in IMPLEMENTATION_PLAN.md | | Process timeout | Agent should increase timeout parameter or simplify tasks | | Parallel conflicts | Agent should use git worktrees for isolation | | Can't see progress | Agent should use process tool with action: "log" |


License

MIT

Credits

This skill builds upon work by:

  • @jordyvandomselaar - Original Ralph Loop concept and workflow design
  • @steipete - Coding agent patterns and exec/process tool usage with pty support

Key improvement: Uses OpenClaw's exec tool with pty: true to provide TTY for interactive CLIs, solving the hanging issue that occurs with simple background bash execution.

API & Reliability

Machine endpoints, contract coverage, trust signals, runtime metrics, benchmarks, and guardrails for agent-to-agent use.

MissingCLAWHUB

Machine interfaces

Contract & API

Contract coverage

Status

missing

Auth

None

Streaming

No

Data region

Unspecified

Protocol support

OpenClaw: self-declared

Requires: none

Forbidden: none

Guardrails

Operational confidence: low

No positive guardrails captured.
Invocation examples
curl -s "https://xpersona.co/api/v1/agents/clawhub-skills-addozhang-ralph-loop-agent/snapshot"
curl -s "https://xpersona.co/api/v1/agents/clawhub-skills-addozhang-ralph-loop-agent/contract"
curl -s "https://xpersona.co/api/v1/agents/clawhub-skills-addozhang-ralph-loop-agent/trust"

Operational fit

Reliability & Benchmarks

Trust signals

Handshake

UNKNOWN

Confidence

unknown

Attempts 30d

unknown

Fallback rate

unknown

Runtime metrics

Observed P50

unknown

Observed P95

unknown

Rate limit

unknown

Estimated cost

unknown

Do not use if

Contract metadata is missing or unavailable for deterministic execution.
No benchmark suites or observed failure patterns are available.

Machine Appendix

Raw contract, invocation, trust, capability, facts, and change-event payloads for machine-side inspection.

MissingCLAWHUB

Contract JSON

{
  "contractStatus": "missing",
  "authModes": [],
  "requires": [],
  "forbidden": [],
  "supportsMcp": false,
  "supportsA2a": false,
  "supportsStreaming": false,
  "inputSchemaRef": null,
  "outputSchemaRef": null,
  "dataRegion": null,
  "contractUpdatedAt": null,
  "sourceUpdatedAt": null,
  "freshnessSeconds": null
}

Invocation Guide

{
  "preferredApi": {
    "snapshotUrl": "https://xpersona.co/api/v1/agents/clawhub-skills-addozhang-ralph-loop-agent/snapshot",
    "contractUrl": "https://xpersona.co/api/v1/agents/clawhub-skills-addozhang-ralph-loop-agent/contract",
    "trustUrl": "https://xpersona.co/api/v1/agents/clawhub-skills-addozhang-ralph-loop-agent/trust"
  },
  "curlExamples": [
    "curl -s \"https://xpersona.co/api/v1/agents/clawhub-skills-addozhang-ralph-loop-agent/snapshot\"",
    "curl -s \"https://xpersona.co/api/v1/agents/clawhub-skills-addozhang-ralph-loop-agent/contract\"",
    "curl -s \"https://xpersona.co/api/v1/agents/clawhub-skills-addozhang-ralph-loop-agent/trust\""
  ],
  "jsonRequestTemplate": {
    "query": "summarize this repo",
    "constraints": {
      "maxLatencyMs": 2000,
      "protocolPreference": [
        "OPENCLEW"
      ]
    }
  },
  "jsonResponseTemplate": {
    "ok": true,
    "result": {
      "summary": "...",
      "confidence": 0.9
    },
    "meta": {
      "source": "CLAWHUB",
      "generatedAt": "2026-04-17T05:29:20.346Z"
    }
  },
  "retryPolicy": {
    "maxAttempts": 3,
    "backoffMs": [
      500,
      1500,
      3500
    ],
    "retryableConditions": [
      "HTTP_429",
      "HTTP_503",
      "NETWORK_TIMEOUT"
    ]
  }
}

Trust JSON

{
  "status": "unavailable",
  "handshakeStatus": "UNKNOWN",
  "verificationFreshnessHours": null,
  "reputationScore": null,
  "p95LatencyMs": null,
  "successRate30d": null,
  "fallbackRate": null,
  "attempts30d": null,
  "trustUpdatedAt": null,
  "trustConfidence": "unknown",
  "sourceUpdatedAt": null,
  "freshnessSeconds": null
}

Capability Matrix

{
  "rows": [
    {
      "key": "OPENCLEW",
      "type": "protocol",
      "support": "unknown",
      "confidenceSource": "profile",
      "notes": "Listed on profile"
    },
    {
      "key": "key",
      "type": "capability",
      "support": "supported",
      "confidenceSource": "profile",
      "notes": "Declared in agent profile metadata"
    }
  ],
  "flattenedTokens": "protocol:OPENCLEW|unknown|profile capability:key|supported|profile"
}

Facts JSON

[
  {
    "factKey": "docs_crawl",
    "category": "integration",
    "label": "Crawlable docs",
    "value": "6 indexed pages on the official domain",
    "href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceType": "search_document",
    "confidence": "medium",
    "observedAt": "2026-04-15T05:03:46.393Z",
    "isPublic": true
  },
  {
    "factKey": "vendor",
    "category": "vendor",
    "label": "Vendor",
    "value": "Openclaw",
    "href": "https://github.com/openclaw/skills/tree/main/skills/addozhang/ralph-loop-agent",
    "sourceUrl": "https://github.com/openclaw/skills/tree/main/skills/addozhang/ralph-loop-agent",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-04-15T00:45:39.800Z",
    "isPublic": true
  },
  {
    "factKey": "protocols",
    "category": "compatibility",
    "label": "Protocol compatibility",
    "value": "OpenClaw",
    "href": "https://xpersona.co/api/v1/agents/clawhub-skills-addozhang-ralph-loop-agent/contract",
    "sourceUrl": "https://xpersona.co/api/v1/agents/clawhub-skills-addozhang-ralph-loop-agent/contract",
    "sourceType": "contract",
    "confidence": "medium",
    "observedAt": "2026-04-15T00:45:39.800Z",
    "isPublic": true
  },
  {
    "factKey": "handshake_status",
    "category": "security",
    "label": "Handshake status",
    "value": "UNKNOWN",
    "href": "https://xpersona.co/api/v1/agents/clawhub-skills-addozhang-ralph-loop-agent/trust",
    "sourceUrl": "https://xpersona.co/api/v1/agents/clawhub-skills-addozhang-ralph-loop-agent/trust",
    "sourceType": "trust",
    "confidence": "medium",
    "observedAt": null,
    "isPublic": true
  }
]

Change Events JSON

[
  {
    "eventType": "docs_update",
    "title": "Docs refreshed: Sign in to GitHub · GitHub",
    "description": "Fresh crawlable documentation was indexed for the official domain.",
    "href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceType": "search_document",
    "confidence": "medium",
    "observedAt": "2026-04-15T05:03:46.393Z",
    "isPublic": true
  }
]

Sponsored

Ads related to ralph-loop and adjacent AI workflows.