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Auto Dream

Community
notque
auto-dream

Background memory consolidation — overnight review, merge, and injection payload for memory files.

Overview

Publishernotque
Repositoryvexjoy-agent
Skill nameauto-dream
Stars
443
Forks
50
Bundled files
6
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 6 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by notque on GitHub. Read the source before you install it.

Installation

Install the Auto Dream AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/notque/vexjoy-agent/tree/main/skills/meta/auto-dream
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/notque/vexjoy-agent.git /tmp/vexjoy-agent
mkdir -p .claude/skills
cp -r /tmp/vexjoy-agent/skills/meta/auto-dream .claude/skills/auto-dream
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Auto Dream in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Auto Dream on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Auto Dream is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Background memory consolidation cycle. Scans memory files, finds stale, duplicate, and conflicting entries, consolidates them, synthesizes cross-session insights, builds an injection-ready payload for the next session start, and writes a dated dream report.

When to invoke

  • User says "run dream", "consolidate memories", "clean up memories", "memory maintenance", "deduplicate memories"
  • Cron job at 2 AM nightly via wrapper script: scripts/auto-dream-cron.sh --execute
  • Manual trigger for testing: ./scripts/auto-dream-cron.sh (dry-run by default)

Instructions

When invoked interactively (not via cron), read skills/meta/auto-dream/dream-prompt.md and execute its phases directly. The prompt is self-contained -- it describes the full cycle including safety constraints, file paths, and output formats.

For cron invocation: the dream prompt is passed directly to claude -p and runs as a standalone headless session with no CLAUDE.md, no hooks, no project context. All instructions are embedded in the prompt.

Phases

  1. SCAN — Read all memory files and the recent git log. Write the scan document to ~/.claude/state/dream-scan-{date}.md.
  2. ANALYZE — Identify stale, duplicate, conflicting memories and cross-session patterns. Write analysis to ~/.claude/state/dream-analysis-{date}.md.
  3. CONSOLIDATE — Apply consolidation actions (max 5 changes). Archive stale/merged files, update MEMORY.md atomically.
  4. SYNTHESIZE — Create insight memories from cross-session patterns (max 2 new memories per cycle).
  5. SELECT — Build the injection-ready payload for session start. Write to ~/.claude/state/dream-injection-{project-hash}.md.
  6. REPORT — Write the dream summary to ~/.claude/state/last-dream.md.

Safety constraints (always enforced)

  • Never delete files — archive to memory/archive/, never rm
  • Write the REPORT before executing any CONSOLIDATE filesystem operations
  • Maximum 5 memory changes per cycle — excess items deferred to next cycle
  • Flag conflicts for human review, never auto-resolve
  • Preserve YAML frontmatter when merging; use merged_from field for provenance
  • Memory files are the only write target. Knowledge reaches an agent or skill file through a reviewed human edit, never through this cycle.
  • In dry-run mode (the default), CONSOLIDATE and SYNTHESIZE describe proposed changes only — no filesystem writes. The wrapper script sets DREAM_DRY_RUN_MODE=yes, substituted into the prompt at runtime.

Testing

bash
# Dry run (read-only, no filesystem changes — dry-run is the default)
./scripts/auto-dream-cron.sh

# Full run (execute consolidation)
./scripts/auto-dream-cron.sh --execute

# Check output
cat ~/.claude/state/last-dream.md

# Verify cron registration
python3 ~/.claude/scripts/crontab-manager.py list

Cost estimate

~0.09 USD per nightly run with 50 memory files (~20-30K input tokens at Sonnet pricing). ~33 USD/year for automated overnight operation. Budget capped at 3.00 USD/run via wrapper script.

Cron setup

Use crontab-manager.py (not raw crontab -e) to install. The wrapper script handles PATH, lockfile, logging, budget cap, and dry-run/execute toggle.

bash
# Preview the cron entry
python3 ~/.claude/scripts/crontab-manager.py add \
  --tag "auto-dream" \
  --schedule "7 2 * * *" \
  --command "/home/feedgen/vexjoy-agent/scripts/auto-dream-cron.sh --execute >> /home/feedgen/vexjoy-agent/cron-logs/auto-dream/cron.log 2>&1" \
  --dry-run

# Install (after dry-run testing passes)
python3 ~/.claude/scripts/crontab-manager.py add \
  --tag "auto-dream" \
  --schedule "7 2 * * *" \
  --command "/home/feedgen/vexjoy-agent/scripts/auto-dream-cron.sh --execute >> /home/feedgen/vexjoy-agent/cron-logs/auto-dream/cron.log 2>&1"

# Verify
python3 ~/.claude/scripts/crontab-manager.py verify --tag auto-dream

Note: schedule uses 2:07 AM (off-minute) per cron best practice — avoids load spikes from jobs firing at :00.

Wrapper script details

scripts/auto-dream-cron.sh follows the established headless cron pattern (see skills/content/reddit-moderate/scripts/reddit-automod-cron.sh):

  • flock lockfile prevents concurrent runs
  • --permission-mode auto (never --dangerously-skip-permissions)
  • --max-budget-usd 3.00 caps spend per run
  • --no-session-persistence for clean headless operation
  • envsubst templates dream-prompt.md with project-specific paths at runtime
  • tee to timestamped per-run log file
  • Dry-run by default, --execute for live runs
  • Exit code propagation via PIPESTATUS[0]

Deep References

Load when the task requires detailed guidance beyond the phases above.

SignalReference
Failed cron run, wrapper script setup, budget capreferences/headless-cron-patterns.md
Memory file writes, YAML frontmatter, staleness, mergingreferences/memory-file-operations.md
Dry-run validation, output file verificationreferences/dream-cycle-testing.md
Cron log interpretation, rotation, phase markersreferences/logging-patterns.md
Concurrent runs, lockfile, partial write recoveryreferences/concurrency.md

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Auto Dream AI skill do?

Background memory consolidation — overnight review, merge, and injection payload for memory files.

Why use Auto Dream on TypingMind?

Because you install it once and use it with any model. Auto Dream is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Auto Dream in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/notque/vexjoy-agent/tree/main/skills/meta/auto-dream. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Auto Dream?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Auto Dream?

As many as you like. As long as a model supports skills, you can use Auto Dream with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Auto Dream AI skill free?

Yes. It is published on GitHub by notque under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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