Rank
70
AI Agents & MCPs & AI Workflow Automation • (~400 MCP servers for AI agents) • AI Automation / AI Agent with MCPs • AI Workflows & AI Agents • MCPs for AI Agents
Traction
No public download signal
Freshness
Updated 2d ago
Xpersona Agent
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
clawhub skill install skills:addozhang:ralph-loop-agentOverall 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
Key links, install path, reliability highlights, and the shortest practical read before diving into the crawl record.
Overview
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.
Trust score
Unknown
Compatibility
OpenClaw
Freshness
Feb 25, 2026
Vendor
Openclaw
Artifacts
0
Benchmarks
0
Last release
Unpublished
Install & run
clawhub skill install skills:addozhang:ralph-loop-agentSetup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
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.
Public facts grouped by evidence type, plus release and crawl events with provenance and freshness.
Public facts
Vendor
Openclaw
Protocol compatibility
OpenClaw
Handshake status
UNKNOWN
Crawlable docs
6 indexed pages on the official domain
Parameters, dependencies, examples, extracted files, editorial overview, and the complete README when available.
Captured outputs
Extracted files
0
Examples
6
Snippets
0
Languages
typescript
Parameters
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 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
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:
specs/*.mdIMPLEMENTATION_PLAN.md (do not implement)The loop persists context via PROMPT.md + AGENTS.md (loaded each iteration) and the plan/specs on disk.
This skill generates instructions for OpenClaw agents to execute Ralph Loops using the exec and process tools.
exec tool with the coding agent commandpty: true to provide TTY for interactive CLIsbackground: true for monitoring capabilitiesprocess tool to monitor progress and detect completionImportant: Users don't run these scripts directly - the OpenClaw agent executes them using its tool capabilities.
Some coding agents require a real terminal (TTY) to work properly, or they will hang:
Interactive CLIs (need TTY):
Non-interactive CLIs (file-based):
Solution: Use exec + process mode for interactive CLIs, simple loops for file-based tools.
For OpenCode, Codex, Claude Code, Pi, and Goose - these require TTY support:
When I (the agent) receive a Ralph Loop request, I will:
Use exec tool to launch the coding agent:
exec tool with parameters:
- command: "opencode run --model <MODEL> \"$(cat PROMPT.md)\""
- workdir: <project_path>
- background: true
- pty: true
- yieldMs: 60000
- timeout: 3600
Capture session ID from exec tool response
Use process tool to monitor:
process tool with:
- action: "poll"
- sessionId: <captured_session_id>
process tool with:
- action: "log"
- sessionId: <captured_session_id>
- offset: -30 (for recent output)
Check completion by reading IMPLEMENTATION_PLAN.md for sentinel text
Clean up with process kill if needed:
process tool with:
- action: "kill"
- sessionId: <session_id>
Benefits: TTY support, real-time logs, timeout handling, parallel sessions, workdir isolation
Required:
opencode, codex, claude, goose, pi, other)PLANNING, BUILDING, or BOTH)Optional:
STATUS: COMPLETE in IMPLEMENTATION_PLAN.md)$PWD)--full-auto, --yolo, --dangerously-skip-permissions)Auto-detect:
If requirements are unclear:
specs/<topic>.md for eachPROMPT.md references:
specs/*.mdIMPLEMENTATION_PLAN.mdAGENTS.md includes:
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
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 "$(cat PROMPT.md)" |
Common flags:
--full-auto, --yolo, --model <model>--dangerously-skip-permissionsAgent executes this sequence:
Step 1: Launch OpenCode with exec tool
{
command: "opencode run --model github-copilot/claude-opus-4.5 \"$(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"
}
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
Use flexible regex to match variations:
grep -Eq "STATUS:?\s*(PLANNING_)?COMPLETE" IMPLEMENTATION_PLAN.md
Matches:
STATUS: COMPLETESTATUS:COMPLETESTATUS: PLANNING_COMPLETE## Status: PLANNING_COMPLETE--full-auto (sandboxed, auto-approve) or --yolo (no sandbox!)--dangerously-skip-permissionsCtrl+Cgit reset --hard HEAD~N| 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" |
MIT
This skill builds upon work by:
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.
Machine endpoints, contract coverage, trust signals, runtime metrics, benchmarks, and guardrails for agent-to-agent use.
Machine interfaces
Contract coverage
Status
missing
Auth
None
Streaming
No
Data region
Unspecified
Protocol support
Requires: none
Forbidden: none
Guardrails
Operational confidence: low
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
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
Raw contract, invocation, trust, capability, facts, and change-event payloads for machine-side inspection.
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.