Xpersona

Crawler Summary

rem-sleep answer-first brief

Memory consolidation and defragmentation for long-term memory maintenance. Use when asked to consolidate memories, defrag memory, run REM sleep, clean up memory files, or process session logs into durable memory. Also use periodically during heartbeats for memory maintenance. --- name: rem-sleep description: Memory consolidation and defragmentation for long-term memory maintenance. Use when asked to consolidate memories, defrag memory, run REM sleep, clean up memory files, or process session logs into durable memory. Also use periodically during heartbeats for memory maintenance. homepage: https://github.com/stewnight/rem-sleep-skill --- REM Sleep - Memory Consolidation for AI Agents Like Published capability contract available. No trust telemetry is available yet. 3 GitHub stars reported by the source. Last updated 4/15/2026.

Freshness

Last checked 4/15/2026

Best For

Contract is available with explicit auth and schema references.

Not Ideal For

rem-sleep is not ideal for teams that need stronger public trust telemetry, lower setup complexity, or more explicit contract coverage before production rollout.

Evidence Sources Checked

editorial-content, capability-contract, runtime-metrics, public facts pack

CardFactsSnapshotContractTrust
Claim this agent
Agent DossierGitHubSafety: 94/100

rem-sleep

Memory consolidation and defragmentation for long-term memory maintenance. Use when asked to consolidate memories, defrag memory, run REM sleep, clean up memory files, or process session logs into durable memory. Also use periodically during heartbeats for memory maintenance. --- name: rem-sleep description: Memory consolidation and defragmentation for long-term memory maintenance. Use when asked to consolidate memories, defrag memory, run REM sleep, clean up memory files, or process session logs into durable memory. Also use periodically during heartbeats for memory maintenance. homepage: https://github.com/stewnight/rem-sleep-skill --- REM Sleep - Memory Consolidation for AI Agents Like

OpenClawself-declared

Public facts

7

Change events

1

Artifacts

0

Freshness

Apr 15, 2026

Verifiededitorial-contentNo verified compatibility signals3 GitHub stars

Published capability contract available. No trust telemetry is available yet. 3 GitHub stars reported by the source. Last updated 4/15/2026.

3 GitHub starsSchema refs publishedTrust evidence available

Trust score

Unknown

Compatibility

OpenClaw

Freshness

Apr 15, 2026

Vendor

Stewnight

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

Published capability contract available. No trust telemetry is available yet. 3 GitHub stars reported by the source. Last updated 4/15/2026.

View Source

Setup snapshot

git clone https://github.com/stewnight/rem-sleep-skill.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

Stewnight

profilemedium
Observed Apr 15, 2026Source linkProvenance
Compatibility (2)

Protocol compatibility

OpenClaw

contractmedium
Observed Feb 24, 2026Source linkProvenance

Auth modes

api_key

contracthigh
Observed Feb 24, 2026Source linkProvenance
Artifact (1)

Machine-readable schemas

OpenAPI or schema references published

contracthigh
Observed Feb 24, 2026Source linkProvenance
Adoption (1)

Adoption signal

3 GitHub stars

profilemedium
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

Release & Crawl Timeline

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

Self-declaredagent-index

Docs Update

Docs refreshed: Sign in to GitHub · GitHub

search_documentmedium

Fresh crawlable documentation was indexed for the official domain.

Observed Apr 15, 2026

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

bash

# OpenClaw session logs location
SESSIONS_DIR="$HOME/.openclaw/agents/main/sessions"

# Search for patterns in recent sessions
grep -r "decision\|learned\|important\|remember\|TODO" "$SESSIONS_DIR" --include="*.jsonl" | head -100

# Parse JSONL and search content
find "$SESSIONS_DIR" -name "*.jsonl" -mtime -3 -exec cat {} \; | \
  jq -r 'select(.content) | .content' 2>/dev/null | \
  grep -i "decision\|learned\|important"

bash

# More powerful semantic search
rp -e 'search "decision" --context-lines 2'
rp -e 'search "learned" --context-lines 2'
rp -e 'search "important" --context-lines 2'

text

memory_search("decisions made this week")
memory_search("lessons learned")

text

STALE: [entry] — reason it's outdated
DUPLICATE: [entry A] ≈ [entry B]
INCONSISTENT: [entry A] vs [entry B]
BLOAT: [verbose entry] → [compressed version]

bash

# Native search (no dependencies)
grep -r "pattern" ~/.openclaw/agents/main/sessions --include="*.jsonl"

# With Repo Prompt
rp -e 'search "PATTERN" --context-lines 2'

# Helper script (if using Repo Prompt)
./scripts/gather-sessions.sh [days_back]

text

rem-sleep/
├── SKILL.md          # This file
├── README.md         # GitHub readme
└── scripts/
    └── gather-sessions.sh   # Helper script (requires Repo Prompt)

Docs & README

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

Self-declaredGITHUB OPENCLEW

Docs source

GITHUB OPENCLEW

Editorial quality

ready

Memory consolidation and defragmentation for long-term memory maintenance. Use when asked to consolidate memories, defrag memory, run REM sleep, clean up memory files, or process session logs into durable memory. Also use periodically during heartbeats for memory maintenance. --- name: rem-sleep description: Memory consolidation and defragmentation for long-term memory maintenance. Use when asked to consolidate memories, defrag memory, run REM sleep, clean up memory files, or process session logs into durable memory. Also use periodically during heartbeats for memory maintenance. homepage: https://github.com/stewnight/rem-sleep-skill --- REM Sleep - Memory Consolidation for AI Agents Like

Full README

name: rem-sleep description: Memory consolidation and defragmentation for long-term memory maintenance. Use when asked to consolidate memories, defrag memory, run REM sleep, clean up memory files, or process session logs into durable memory. Also use periodically during heartbeats for memory maintenance. homepage: https://github.com/stewnight/rem-sleep-skill

REM Sleep - Memory Consolidation for AI Agents

Like biological REM sleep, this skill processes raw experience (session logs) into consolidated long-term memory.

Works with: OpenClaw, Claude Code, or any agent with session logs and memory files.

The Problem

  • Session logs accumulate but are expensive to re-read
  • Important insights get buried in noise
  • "Mental notes" don't survive context compaction
  • After a restart, you're starting from scratch unless you wrote it down

The Solution

Periodic "sleep cycles" that:

  1. Search session logs for significant patterns
  2. Extract what's worth remembering
  3. Consolidate into durable memory files

Modes

1. Consolidate

Process recent session logs → extract significant events → update MEMORY.md

2. Defrag

Review MEMORY.md → remove stale/outdated entries → merge duplicates → compress

3. Full

Run both consolidate then defrag.

Consolidation Workflow

Step 1: Gather Recent Sessions

Option A: Using grep/jq (no extra software)

# OpenClaw session logs location
SESSIONS_DIR="$HOME/.openclaw/agents/main/sessions"

# Search for patterns in recent sessions
grep -r "decision\|learned\|important\|remember\|TODO" "$SESSIONS_DIR" --include="*.jsonl" | head -100

# Parse JSONL and search content
find "$SESSIONS_DIR" -name "*.jsonl" -mtime -3 -exec cat {} \; | \
  jq -r 'select(.content) | .content' 2>/dev/null | \
  grep -i "decision\|learned\|important"

Option B: Using Repo Prompt (if installed)

# More powerful semantic search
rp -e 'search "decision" --context-lines 2'
rp -e 'search "learned" --context-lines 2'
rp -e 'search "important" --context-lines 2'

Option C: Using memory_search (OpenClaw built-in)

If your agent has the memory_search tool, use it to semantically search memory files:

memory_search("decisions made this week")
memory_search("lessons learned")

Step 2: Identify Consolidation Candidates

From search results, look for:

  • Decisions made — choices, preferences, conclusions
  • Facts learned — new info about people, projects, systems
  • Lessons — things that worked/didn't, mistakes to avoid
  • TODOs/commitments — things promised or planned
  • Relationship context — interactions with people, their preferences

Step 3: Update Memory Files

Two-tier system:

  1. Daily file (memory/YYYY-MM-DD.md): Raw events, specific details
  2. MEMORY.md: Distilled, durable knowledge worth keeping long-term

Consolidation prompt:

Review these session excerpts. Extract significant information that should be remembered long-term. Focus on: decisions, facts about people/projects, lessons learned, and preferences. Format as bullet points suitable for MEMORY.md.

Defrag Workflow

Step 1: Analyze Current Memory

Read MEMORY.md and identify:

  • Stale entries — outdated info, completed TODOs, old dates
  • Duplicates — same info repeated in different sections
  • Inconsistencies — conflicting information
  • Bloat — overly verbose entries that could be compressed

Step 2: Categorize Issues

STALE: [entry] — reason it's outdated
DUPLICATE: [entry A] ≈ [entry B]
INCONSISTENT: [entry A] vs [entry B]
BLOAT: [verbose entry] → [compressed version]

Step 3: Apply Fixes

  • Remove stale entries (or move to an archive section if uncertain)
  • Merge duplicates into single authoritative entry
  • Resolve inconsistencies (check session logs if needed)
  • Compress verbose entries

Step 4: Reorganize

Ensure MEMORY.md has logical sections:

  • About [User]
  • My Setup
  • Projects
  • People
  • Preferences
  • Lessons Learned

Scheduling

Recommended cadence:

  • Consolidate: Every few days, or after busy periods
  • Defrag: Weekly or bi-weekly
  • Full: Monthly deep clean

Trigger options:

  • Manually: "Run REM sleep" / "Consolidate my memories"
  • Heartbeat: Add to HEARTBEAT.md for periodic runs
  • Cron: Schedule isolated job for off-hours

Quick Reference

# Native search (no dependencies)
grep -r "pattern" ~/.openclaw/agents/main/sessions --include="*.jsonl"

# With Repo Prompt
rp -e 'search "PATTERN" --context-lines 2'

# Helper script (if using Repo Prompt)
./scripts/gather-sessions.sh [days_back]

File Structure

rem-sleep/
├── SKILL.md          # This file
├── README.md         # GitHub readme
└── scripts/
    └── gather-sessions.sh   # Helper script (requires Repo Prompt)

Notes

  • Session logs are JSONL format — content is wrapped in JSON
  • When uncertain if something is stale, keep it (conservative approach)
  • MEMORY.md is loaded in main sessions — keep it focused and relevant
  • The skill is a workflow, not a binary — adapt to your setup

Contributing

PRs welcome! Ideas for improvement:

  • Better heuristics for "what's worth remembering"
  • Alternative search methods
  • Automation scripts for different platforms
  • Integration with vector DBs for semantic search

GitHub: https://github.com/stewnight/rem-sleep-skill

Contract & API

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

Verifiedcapability-contract

Endpoints

Dossier APISnapshot APIContract APITrust API

Contract coverage

Status

ready

Auth

api_key

Streaming

No

Data region

global

Protocol support

OpenClaw: self-declared

Requires: openclew, lang:typescript

Forbidden: none

Guardrails

Operational confidence: medium

Contract is available with explicit auth and schema references.
Trust confidence is not low and verification freshness is acceptable.
Invocation examples
curl -s "https://xpersona.co/api/v1/agents/stewnight-rem-sleep-skill/snapshot"
curl -s "https://xpersona.co/api/v1/agents/stewnight-rem-sleep-skill/contract"
curl -s "https://xpersona.co/api/v1/agents/stewnight-rem-sleep-skill/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

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
Agent hubMore from this sourceOpenClaw agents
Machine Appendix

Contract JSON

{
  "contractStatus": "ready",
  "authModes": [
    "api_key"
  ],
  "requires": [
    "openclew",
    "lang:typescript"
  ],
  "forbidden": [],
  "supportsMcp": false,
  "supportsA2a": false,
  "supportsStreaming": false,
  "inputSchemaRef": "https://github.com/stewnight/rem-sleep-skill#input",
  "outputSchemaRef": "https://github.com/stewnight/rem-sleep-skill#output",
  "dataRegion": "global",
  "contractUpdatedAt": "2026-02-24T19:45:00.286Z",
  "sourceUpdatedAt": "2026-02-24T19:45:00.286Z",
  "freshnessSeconds": 4420525
}

Invocation Guide

{
  "preferredApi": {
    "snapshotUrl": "https://xpersona.co/api/v1/agents/stewnight-rem-sleep-skill/snapshot",
    "contractUrl": "https://xpersona.co/api/v1/agents/stewnight-rem-sleep-skill/contract",
    "trustUrl": "https://xpersona.co/api/v1/agents/stewnight-rem-sleep-skill/trust"
  },
  "curlExamples": [
    "curl -s \"https://xpersona.co/api/v1/agents/stewnight-rem-sleep-skill/snapshot\"",
    "curl -s \"https://xpersona.co/api/v1/agents/stewnight-rem-sleep-skill/contract\"",
    "curl -s \"https://xpersona.co/api/v1/agents/stewnight-rem-sleep-skill/trust\""
  ],
  "jsonRequestTemplate": {
    "query": "summarize this repo",
    "constraints": {
      "maxLatencyMs": 2000,
      "protocolPreference": [
        "OPENCLEW"
      ]
    }
  },
  "jsonResponseTemplate": {
    "ok": true,
    "result": {
      "summary": "...",
      "confidence": 0.9
    },
    "meta": {
      "source": "GITHUB_OPENCLEW",
      "generatedAt": "2026-04-16T23:40:25.774Z"
    }
  },
  "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"
    }
  ],
  "flattenedTokens": "protocol:OPENCLEW|unknown|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": "Stewnight",
    "href": "https://github.com/stewnight/rem-sleep-skill",
    "sourceUrl": "https://github.com/stewnight/rem-sleep-skill",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-04-15T03:12:56.425Z",
    "isPublic": true
  },
  {
    "factKey": "traction",
    "category": "adoption",
    "label": "Adoption signal",
    "value": "3 GitHub stars",
    "href": "https://github.com/stewnight/rem-sleep-skill",
    "sourceUrl": "https://github.com/stewnight/rem-sleep-skill",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-04-15T03:12:56.425Z",
    "isPublic": true
  },
  {
    "factKey": "protocols",
    "category": "compatibility",
    "label": "Protocol compatibility",
    "value": "OpenClaw",
    "href": "https://xpersona.co/api/v1/agents/stewnight-rem-sleep-skill/contract",
    "sourceUrl": "https://xpersona.co/api/v1/agents/stewnight-rem-sleep-skill/contract",
    "sourceType": "contract",
    "confidence": "medium",
    "observedAt": "2026-02-24T19:45:00.286Z",
    "isPublic": true
  },
  {
    "factKey": "auth_modes",
    "category": "compatibility",
    "label": "Auth modes",
    "value": "api_key",
    "href": "https://xpersona.co/api/v1/agents/stewnight-rem-sleep-skill/contract",
    "sourceUrl": "https://xpersona.co/api/v1/agents/stewnight-rem-sleep-skill/contract",
    "sourceType": "contract",
    "confidence": "high",
    "observedAt": "2026-02-24T19:45:00.286Z",
    "isPublic": true
  },
  {
    "factKey": "schema_refs",
    "category": "artifact",
    "label": "Machine-readable schemas",
    "value": "OpenAPI or schema references published",
    "href": "https://github.com/stewnight/rem-sleep-skill#input",
    "sourceUrl": "https://xpersona.co/api/v1/agents/stewnight-rem-sleep-skill/contract",
    "sourceType": "contract",
    "confidence": "high",
    "observedAt": "2026-02-24T19:45:00.286Z",
    "isPublic": true
  },
  {
    "factKey": "handshake_status",
    "category": "security",
    "label": "Handshake status",
    "value": "UNKNOWN",
    "href": "https://xpersona.co/api/v1/agents/stewnight-rem-sleep-skill/trust",
    "sourceUrl": "https://xpersona.co/api/v1/agents/stewnight-rem-sleep-skill/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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Ads related to rem-sleep and adjacent AI workflows.