Crawler Summary

ralph-mode answer-first brief

Autonomous development loops with iteration, backpressure gates, and completion criteria. Use for sustained coding sessions that require multiple iterations, test validation, and structured progress tracking. Supports Next.js, Python, FastAPI, and GPU workloads with Ralph Wiggum methodology adapted for OpenClaw. --- name: ralph-mode description: Autonomous development loops with iteration, backpressure gates, and completion criteria. Use for sustained coding sessions that require multiple iterations, test validation, and structured progress tracking. Supports Next.js, Python, FastAPI, and GPU workloads with Ralph Wiggum methodology adapted for OpenClaw. --- Ralph Mode - Autonomous Development Loops Ralph Mode implements the Capability contract not published. No trust telemetry is available yet. Last updated 4/14/2026.

Freshness

Last checked 4/14/2026

Best For

ralph-mode is best for you, complete, tail workflows where OpenClaw compatibility matters.

Not Ideal For

Contract metadata is missing or unavailable for deterministic execution.

Evidence Sources Checked

editorial-content, GITHUB OPENCLEW, runtime-metrics, public facts pack

Claim this agent
Agent DossierGitHubSafety: 94/100

ralph-mode

Autonomous development loops with iteration, backpressure gates, and completion criteria. Use for sustained coding sessions that require multiple iterations, test validation, and structured progress tracking. Supports Next.js, Python, FastAPI, and GPU workloads with Ralph Wiggum methodology adapted for OpenClaw. --- name: ralph-mode description: Autonomous development loops with iteration, backpressure gates, and completion criteria. Use for sustained coding sessions that require multiple iterations, test validation, and structured progress tracking. Supports Next.js, Python, FastAPI, and GPU workloads with Ralph Wiggum methodology adapted for OpenClaw. --- Ralph Mode - Autonomous Development Loops Ralph Mode implements the

OpenClawself-declared

Public facts

4

Change events

1

Artifacts

0

Freshness

Apr 14, 2026

Verifiededitorial-contentNo verified compatibility signals

Capability contract not published. No trust telemetry is available yet. Last updated 4/14/2026.

Trust evidence available

Trust score

Unknown

Compatibility

OpenClaw

Freshness

Apr 14, 2026

Vendor

Richginsberg

Artifacts

0

Benchmarks

0

Last release

Unpublished

Executive Summary

Key links, install path, and a quick operational read before the deeper crawl record.

Verifiededitorial-content

Summary

Capability contract not published. No trust telemetry is available yet. Last updated 4/14/2026.

Setup snapshot

git clone https://github.com/richginsberg/ralph-mode.git
  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 Ledger

Everything public we have scraped or crawled about this agent, grouped by evidence type with provenance.

Verifiededitorial-content
Vendor (1)

Vendor

Richginsberg

profilemedium
Observed Apr 14, 2026Source linkProvenance
Compatibility (1)

Protocol compatibility

OpenClaw

contractmedium
Observed Apr 14, 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

Release & Crawl Timeline

Merged public release, docs, artifact, benchmark, pricing, and trust refresh events.

Self-declaredagent-index

Artifacts Archive

Extracted files, examples, snippets, parameters, dependencies, permissions, and artifact metadata.

Self-declaredGITHUB OPENCLEW

Extracted files

0

Examples

6

Snippets

0

Languages

typescript

Parameters

Executable Examples

text

project-root/
├── IMPLEMENTATION_PLAN.md     # Shared state, updated each iteration
├── AGENTS.md                  # Build/test/lint commands (~60 lines)
├── specs/                     # Requirements (one file per topic)
│   ├── topic-a.md
│   └── topic-b.md
├── src/                        # Application code
└── src/lib/                    # Shared utilities

markdown

# Implementation Plan

## In Progress
- [ ] Task name (iteration N)
  - Notes: discoveries, bugs, blockers

## Completed
- [x] Task name (iteration N)

## Backlog
- [ ] Future task

markdown

# Project Operations

## Build Commands
npm run dev      # Development server
npm run build     # Production build

## Validation
npm run test      # All tests
npm run lint      # ESLint
npm run typecheck  # TypeScript
npm run e2e       # E2E tests

## Operational Notes
- Tests must pass before committing
- Typecheck failures block commits
- Use existing utilities from src/lib over ad-hoc copies

text

"Spawn a sub-agent with @architect hat to design the data model"

markdown

## Completion Check - UX Quality
Criteria: Navigation is intuitive, primary actions are discoverable
Test: User can complete core flow without confusion

## Completion Check - Design Quality
Criteria: Visual hierarchy is clear, brand consistency maintained
Test: Layout follows established patterns

text

specs/
├── authentication.md
├── database.md
└── api-routes.md

src/
├── app/                    # App Router
├── components/              # React components
├── lib/                    # Utilities (db, auth, helpers)
└── types/                   # TypeScript types

AGENTS.md:
  Build: npm run dev
  Test: npm run test
  Typecheck: npx tsc --noEmit
  Lint: npm run lint

Docs & README

Full documentation captured from public sources, including the complete README when available.

Self-declaredGITHUB OPENCLEW

Docs source

GITHUB OPENCLEW

Editorial quality

ready

Autonomous development loops with iteration, backpressure gates, and completion criteria. Use for sustained coding sessions that require multiple iterations, test validation, and structured progress tracking. Supports Next.js, Python, FastAPI, and GPU workloads with Ralph Wiggum methodology adapted for OpenClaw. --- name: ralph-mode description: Autonomous development loops with iteration, backpressure gates, and completion criteria. Use for sustained coding sessions that require multiple iterations, test validation, and structured progress tracking. Supports Next.js, Python, FastAPI, and GPU workloads with Ralph Wiggum methodology adapted for OpenClaw. --- Ralph Mode - Autonomous Development Loops Ralph Mode implements the

Full README

name: ralph-mode description: Autonomous development loops with iteration, backpressure gates, and completion criteria. Use for sustained coding sessions that require multiple iterations, test validation, and structured progress tracking. Supports Next.js, Python, FastAPI, and GPU workloads with Ralph Wiggum methodology adapted for OpenClaw.

Ralph Mode - Autonomous Development Loops

Ralph Mode implements the Ralph Wiggum technique adapted for OpenClaw: autonomous task completion through continuous iteration with backpressure gates, completion criteria, and structured planning.

When to Use

Use Ralph Mode when:

  • Building features that require multiple iterations and refinement
  • Working on complex projects with acceptance criteria to validate
  • Need automated testing, linting, or typecheck gates
  • Want to track progress across many iterations systematically
  • Prefer autonomous loops over manual turn-by-turn guidance

Core Principles

Three-Phase Workflow

Phase 1: Requirements Definition

  • Document specs in specs/ (one file per topic of concern)
  • Define acceptance criteria (observable, verifiable outcomes)
  • Create implementation plan with prioritized tasks

Phase 2: Planning

  • Gap analysis: compare specs against existing code
  • Generate IMPLEMENTATION_PLAN.md with prioritized tasks
  • No implementation during this phase

Phase 3: Building (Iterative)

  • Pick one task from plan per iteration
  • Implement, validate, update plan, commit
  • Continue until all tasks complete or criteria met

Backpressure Gates

Reject incomplete work automatically through validation:

Programmatic Gates (Always use these):

  • Tests: [test command] - Must pass before committing
  • Typecheck: [typecheck command] - Catch type errors early
  • Lint: [lint command] - Enforce code quality
  • Build: [build command] - Verify integration

Subjective Gates (Use for UX, design, quality):

  • LLM-as-judge reviews for tone, aesthetics, usability
  • Binary pass/fail - converges through iteration
  • Only add after programmatic gates work reliably

Context Efficiency

  • One task per iteration = fresh context each time
  • Spawn sub-agents for exploration, not main context
  • Lean prompts = smart zone (~40-60% utilization)
  • Plans are disposable - regenerate cheap vs. salvage

File Structure

Create this structure for each Ralph Mode project:

project-root/
├── IMPLEMENTATION_PLAN.md     # Shared state, updated each iteration
├── AGENTS.md                  # Build/test/lint commands (~60 lines)
├── specs/                     # Requirements (one file per topic)
│   ├── topic-a.md
│   └── topic-b.md
├── src/                        # Application code
└── src/lib/                    # Shared utilities

IMPLEMENTATION_PLAN.md

Priority task list - single source of truth. Format:

# Implementation Plan

## In Progress
- [ ] Task name (iteration N)
  - Notes: discoveries, bugs, blockers

## Completed
- [x] Task name (iteration N)

## Backlog
- [ ] Future task

Topic Scope Test

Can you describe the topic in one sentence without "and"?

  • ✅ "User authentication with JWT and session management"
  • ❌ "Auth, profiles, and billing" → 3 topics

AGENTS.md - Operational Guide

Succinct guide for running the project. Keep under 60 lines:

# Project Operations

## Build Commands
npm run dev      # Development server
npm run build     # Production build

## Validation
npm run test      # All tests
npm run lint      # ESLint
npm run typecheck  # TypeScript
npm run e2e       # E2E tests

## Operational Notes
- Tests must pass before committing
- Typecheck failures block commits
- Use existing utilities from src/lib over ad-hoc copies

Hats (Personas)

Specialized roles for different tasks:

Hat: Architect (@architect)

  • High-level design, data modeling, API contracts
  • Focus: patterns, scalability, maintainability

Hat: Implementer (@implementer)

  • Write code, implement features, fix bugs
  • Focus: correctness, performance, test coverage

Hat: Tester (@tester)

  • Test authoring, validation, edge cases
  • Focus: coverage, reliability, reproducibility

Hat: Reviewer (@reviewer)

  • Code reviews, PR feedback, quality assessment
  • Focus: style, readability, adherence to specs

Usage:

"Spawn a sub-agent with @architect hat to design the data model"

Loop Mechanics

Outer Loop (You coordinate)

Your job as main agent: engineer setup, observe, course-correct.

  1. Don't allocate work to main context - Spawn sub-agents
  2. Let Ralph Ralph - LLM will self-identify, self-correct
  3. Use protection - Sandbox is your security boundary
  4. Plan is disposable - Regenerate when wrong/stale
  5. Move outside the loop - Sit and watch, don't micromanage

Inner Loop (Sub-agent executes)

Each sub-agent iteration:

  1. Study - Read plan, specs, relevant code
  2. Select - Pick most important uncompleted task
  3. Implement - Write code, one task only
  4. Validate - Run tests, lint, typecheck (backpressure)
  5. Update - Mark task done, note discoveries, commit
  6. Exit - Next iteration starts fresh

Stopping Conditions

Loop ends when:

  • ✅ All IMPLEMENTATION_PLAN.md tasks completed
  • ✅ All acceptance criteria met
  • ✅ Tests passing, no blocking issues
  • ⚠️ Max iterations reached (configure limit)
  • 🛑 Manual stop (Ctrl+C)

Completion Criteria

Define success upfront - avoid "seems done" ambiguity.

Programmatic (Measurable)

  • All tests pass: [test_command] returns 0
  • Typecheck passes: No TypeScript errors
  • Build succeeds: Production bundle created
  • Coverage threshold: e.g., 80%+

Subjective (LLM-as-Judge)

For quality criteria that resist automation:

## Completion Check - UX Quality
Criteria: Navigation is intuitive, primary actions are discoverable
Test: User can complete core flow without confusion

## Completion Check - Design Quality
Criteria: Visual hierarchy is clear, brand consistency maintained
Test: Layout follows established patterns

Run LLM-as-judge sub-agent for binary pass/fail.

Technology-Specific Patterns

Next.js Full Stack

specs/
├── authentication.md
├── database.md
└── api-routes.md

src/
├── app/                    # App Router
├── components/              # React components
├── lib/                    # Utilities (db, auth, helpers)
└── types/                   # TypeScript types

AGENTS.md:
  Build: npm run dev
  Test: npm run test
  Typecheck: npx tsc --noEmit
  Lint: npm run lint

Python (Scripts/Notebooks/FastAPI)

specs/
├── data-pipeline.md
├── model-training.md
└── api-endpoints.md

src/
├── pipeline.py
├── models/
├── api/
└── tests/

AGENTS.md:
  Build: python -m src.main
  Test: pytest
  Typecheck: mypy src/
  Lint: ruff check src/

GPU Workloads

specs/
├── model-architecture.md
├── training-data.md
└── inference-pipeline.md

src/
├── models/
├── training/
├── inference/
└── utils/

AGENTS.md:
  Train: python train.py
  Test: pytest tests/
  Lint: ruff check src/
  GPU Check: nvidia-smi

Quick Start Command

Start a Ralph Mode session:

"Start Ralph Mode for my project at ~/projects/my-app. I want to implement user authentication with JWT.

I will:

  1. Create IMPLEMENTATION_PLAN.md with prioritized tasks
  2. Spawn sub-agents for iterative implementation
  3. Apply backpressure gates (test, lint, typecheck)
  4. Track progress and announce completion

Operational Learnings

When Ralph patterns emerge, update AGENTS.md:

## Discovered Patterns

- When adding API routes, also add to OpenAPI spec
- Use existing db utilities from src/lib/db over direct calls
- Test files must be co-located with implementation

Escape Hatches

When trajectory goes wrong:

  • Ctrl+C - Stop loop immediately
  • Regenerate plan - "Discard IMPLEMENTATION_PLAN.md and re-plan"
  • Reset - "Git reset to last known good state"
  • Scope down - Create smaller scoped plan for specific work

Advanced: LLM-as-Judge Fixture

For subjective criteria (tone, aesthetics, UX):

Create src/lib/llm-review.ts:

interface ReviewResult {
  pass: boolean;
  feedback?: string;
}

async function createReview(config: {
  criteria: string;
  artifact: string; // text or screenshot path
}): Promise<ReviewResult>;

Sub-agents discover and use this pattern for binary pass/fail checks.

Critical Operational Requirements

Based on empirical usage, enforce these practices to avoid silent failures:

1. Mandatory Progress Logging

Ralph MUST write to PROGRESS.md after EVERY iteration. This is non-negotiable.

Create PROGRESS.md in project root at start:

# Ralph: [Task Name]

## Iteration [N] - [Timestamp]

### Status
- [ ] In Progress | [ ] Blocked | [ ] Complete

### What Was Done
- [Item 1]
- [Item 2]

### Blockers
- None | [Description]

### Next Step
[Specific next task from IMPLEMENTATION_PLAN.md]

### Files Changed
- `path/to/file.ts` - [brief description]

Why: External observers (parent agents, crons, humans) can tail one file instead of scanning directories or inferring state from session logs.

2. Session Isolation & Cleanup

Before spawning a new Ralph session:

  • Check for existing Ralph sub-agents via sessions_list
  • Kill or verify completion of previous sessions
  • Do NOT spawn overlapping Ralph sessions on same codebase

Anti-pattern: Spawning Ralph v2 while v1 is still running = file conflicts, race conditions, lost work.

3. Explicit Path Verification

Never assume directory structure. At start of each iteration:

// Verify current working directory
const cwd = process.cwd();
console.log(`Working in: ${cwd}`);

// Verify expected paths exist
if (!fs.existsSync('./src/app')) {
  console.error('Expected ./src/app, found:', fs.readdirSync('.'));
  // Adapt or fail explicitly
}

Why: Ralph may be spawned from different contexts with different working directories.

4. Completion Signal Protocol

When done, Ralph MUST:

  1. Write final PROGRESS.md with "## Status: COMPLETE"
  2. List all created/modified files
  3. Exit cleanly (no hanging processes)

Example completion PROGRESS.md:

# Ralph: Influencer Detail Page

## Status: COMPLETE ✅

**Finished:** [ISO timestamp]

### Final Verification
- [x] TypeScript: Pass
- [x] Tests: Pass  
- [x] Build: Pass

### Files Created
- `src/app/feature/page.tsx`
- `src/app/api/feature/route.ts`

### Testing Instructions
1. Run: `npm run dev`
2. Visit: `http://localhost:3000/feature`
3. Verify: [specific checks]

5. Error Handling Requirements

If Ralph encounters unrecoverable errors:

  1. Log to PROGRESS.md with "## Status: BLOCKED"
  2. Describe blocker in detail
  3. List attempted solutions
  4. Exit cleanly (don't hang)

Do not silently fail. A Ralph that stops iterating with no progress log is indistinguishable from one still working.

6. Iteration Time Limits

Set explicit iteration timeouts:

## Operational Parameters
- Max iteration time: 10 minutes
- Total session timeout: 60 minutes
- If iteration exceeds limit: Log blocker, exit

Why: Prevents infinite loops on stuck tasks, allows parent agent to intervene.

Memory Updates

After each Ralph Mode session, document:

## [Date] Ralph Mode Session

**Project:** [project-name]
**Duration:** [iterations]
**Outcome:** success / partial / blocked
**Learnings:**
- What worked well
- What needs adjustment
- Patterns to add to AGENTS.md

Appendix: Hall of Failures

Common anti-patterns observed:

| Anti-Pattern | Consequence | Prevention | |--------------|-------------|------------| | No progress logging | Parent agent cannot determine status | Mandatory PROGRESS.md | | Silent failure | Work lost, time wasted | Explicit error logging | | Overlapping sessions | File conflicts, corrupt state | Check/cleanup before spawn | | Path assumptions | Wrong directory, wrong files | Explicit verification | | No completion signal | Parent waits indefinitely | Clear COMPLETE status | | Infinite iteration | Resource waste, no progress | Time limits + blockers | | Complex initial prompts | Sub-agent never starts (empty session logs) | SIMPLIFY instructions |

NEW: Session Initialization Best Practices (2025-02-07)

Problem: Sub-agents spawn but don't execute

Evidence: Empty session logs (2 bytes), no tool calls, 0 tokens used

Root Causes

  1. Instructions too complex - Overwhelms isolated session initialization
  2. No clear execution trigger - Agent doesn't know to start
  3. Branching logic - "If X do Y, if Z do W" confuses task selection
  4. Multiple files mentioned - Can't decide which to start with

Fix: SIMPLIFIED Ralph Task Template

## Task: [ONE specific thing]

**File:** exact/path/to/file.ts
**What:** Exact description of change
**Validate:** Exact command to run
**Then:** Update PROGRESS.md and exit

## Rules
1. Do NOT look at other files
2. Do NOT "check first"
3. Make the change, validate, exit

BEFORE (Bad - causes stalls):

Fix all TypeScript errors across these files:
- lib/db.ts has 2 errors
- lib/proposal-service.ts has 5 errors
- route.ts has errors
Check which ones to fix first, then...

AFTER (Good - executes):

Fix lib/db.ts line 27:
Change: PoolClient to pg.PoolClient
Validate: npm run typecheck
Exit immediately after

CRITICAL: Single File Rule

Each Ralph iteration gets ONE file. Not "all errors", not "check then decide". ONE file, ONE change, validate, exit.

CRITICAL: Update PROGRESS.md

MANDATORY: After EVERY iteration, update PROGRESS.md with:

## Iteration [N] - [Timestamp]

### Status: Complete ✅ | Blocked ⛔ | Failed ❌

### What Was Done
- [Specific changes made]

### Validation
- [Test/lint/typecheck results]

### Next Step
- [What should happen next]

Why this matters: Cron job reads PROGRESS.md for status updates. If not updated, status appears stale/repetitive.

Debugging Ralph Stalls

If Ralph stalls:

  1. Check session logs (should show tool calls within 60s)
  2. If empty after spawn → instructions too complex
  3. Reduce: ONE file, ONE line number, ONE change
  4. Shorter timeout forces smaller tasks (300s not 600s)

Fixing Stale Status Reports

If cron reports same status repeatedly:

  1. Check PROGRESS.md was updated by sub-agent
  2. If not updated → sub-agent skipped documentation step
  3. Update skill: Add "MANDATORY PROGRESS.md update" to prompt
  4. Manual fix: Update PROGRESS.md to reflect actual state

Summary

Ralph works when: Single file focus + explicit change + validate + exit Ralph stalls when: Complex decisions + multiple files + conditional logic

Contract & API

Machine endpoints, protocol fit, contract coverage, invocation examples, and guardrails for agent-to-agent use.

MissingGITHUB OPENCLEW

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/richginsberg-ralph-mode/snapshot"
curl -s "https://xpersona.co/api/v1/agents/richginsberg-ralph-mode/contract"
curl -s "https://xpersona.co/api/v1/agents/richginsberg-ralph-mode/trust"

Reliability & Benchmarks

Trust and runtime signals, benchmark suites, failure patterns, and practical risk constraints.

Missingruntime-metrics

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.

Media & Demo

Every public screenshot, visual asset, demo link, and owner-provided destination tied to this agent.

Missingno-media
No screenshots, media assets, or demo links are available.

Related Agents

Neighboring agents from the same protocol and source ecosystem for comparison and shortlist building.

Self-declaredprotocol-neighbors
GITHUB_REPOSactivepieces

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

OPENCLAW
GITHUB_REPOScherry-studio

Rank

70

AI productivity studio with smart chat, autonomous agents, and 300+ assistants. Unified access to frontier LLMs

Traction

No public download signal

Freshness

Updated 5d ago

MCPOPENCLAW
GITHUB_REPOSAionUi

Rank

70

Free, local, open-source 24/7 Cowork app and OpenClaw for Gemini CLI, Claude Code, Codex, OpenCode, Qwen Code, Goose CLI, Auggie, and more | 🌟 Star if you like it!

Traction

No public download signal

Freshness

Updated 6d ago

MCPOPENCLAW
GITHUB_REPOSCopilotKit

Rank

70

The Frontend for Agents & Generative UI. React + Angular

Traction

No public download signal

Freshness

Updated 23d ago

OPENCLAW
Machine Appendix

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/richginsberg-ralph-mode/snapshot",
    "contractUrl": "https://xpersona.co/api/v1/agents/richginsberg-ralph-mode/contract",
    "trustUrl": "https://xpersona.co/api/v1/agents/richginsberg-ralph-mode/trust"
  },
  "curlExamples": [
    "curl -s \"https://xpersona.co/api/v1/agents/richginsberg-ralph-mode/snapshot\"",
    "curl -s \"https://xpersona.co/api/v1/agents/richginsberg-ralph-mode/contract\"",
    "curl -s \"https://xpersona.co/api/v1/agents/richginsberg-ralph-mode/trust\""
  ],
  "jsonRequestTemplate": {
    "query": "summarize this repo",
    "constraints": {
      "maxLatencyMs": 2000,
      "protocolPreference": [
        "OPENCLEW"
      ]
    }
  },
  "jsonResponseTemplate": {
    "ok": true,
    "result": {
      "summary": "...",
      "confidence": 0.9
    },
    "meta": {
      "source": "GITHUB_OPENCLEW",
      "generatedAt": "2026-04-16T23:40:23.030Z"
    }
  },
  "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": "you",
      "type": "capability",
      "support": "supported",
      "confidenceSource": "profile",
      "notes": "Declared in agent profile metadata"
    },
    {
      "key": "complete",
      "type": "capability",
      "support": "supported",
      "confidenceSource": "profile",
      "notes": "Declared in agent profile metadata"
    },
    {
      "key": "tail",
      "type": "capability",
      "support": "supported",
      "confidenceSource": "profile",
      "notes": "Declared in agent profile metadata"
    }
  ],
  "flattenedTokens": "protocol:OPENCLEW|unknown|profile capability:you|supported|profile capability:complete|supported|profile capability:tail|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": "Richginsberg",
    "href": "https://github.com/richginsberg/ralph-mode",
    "sourceUrl": "https://github.com/richginsberg/ralph-mode",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-04-14T22:23:42.431Z",
    "isPublic": true
  },
  {
    "factKey": "protocols",
    "category": "compatibility",
    "label": "Protocol compatibility",
    "value": "OpenClaw",
    "href": "https://xpersona.co/api/v1/agents/richginsberg-ralph-mode/contract",
    "sourceUrl": "https://xpersona.co/api/v1/agents/richginsberg-ralph-mode/contract",
    "sourceType": "contract",
    "confidence": "medium",
    "observedAt": "2026-04-14T22:23:42.431Z",
    "isPublic": true
  },
  {
    "factKey": "handshake_status",
    "category": "security",
    "label": "Handshake status",
    "value": "UNKNOWN",
    "href": "https://xpersona.co/api/v1/agents/richginsberg-ralph-mode/trust",
    "sourceUrl": "https://xpersona.co/api/v1/agents/richginsberg-ralph-mode/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
  }
]

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