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Dream

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sneg55
dream

Memory consolidation - review, merge, prune, and index memory files. Run periodically to keep memories organized and up-to-date.

Overview

Publishersneg55
Repositoryagent-starter
Skill namedream
Stars
74
Forks
4
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by sneg55 on GitHub. Read the source before you install it.

Installation

Install the 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/sneg55/agent-starter/tree/main/skills/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/sneg55/agent-starter.git /tmp/agent-starter
mkdir -p .claude/skills
cp -r /tmp/agent-starter/skills/dream .claude/skills/dream
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable 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 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 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.

Dream: Memory Consolidation

You are performing a dream - a reflective pass over your memory files. Synthesize what you've learned recently into durable, well-organized memories so that future sessions can orient quickly.

Phase 1 - Orient

  • ls the memory directory to see what already exists
  • Read MEMORY.md to understand the current index
  • Skim existing topic files so you improve them rather than creating duplicates
  • If logs/ or sessions/ subdirectories exist, review recent entries there

Phase 2 - Gather recent signal

Look for new information worth persisting. Sources in rough priority order:

  1. Daily logs (logs/YYYY/MM/YYYY-MM-DD.md) if present - these are the append-only stream
  2. Existing memories that drifted - facts that contradict something you see in the codebase now
  3. Transcript search - if you need specific context, grep transcripts for narrow terms: grep -rn "<narrow term>" .claude/transcripts/ --include="*.jsonl" | tail -50

Don't exhaustively read transcripts. Look only for things you already suspect matter.

Phase 3 - Consolidate

For each thing worth remembering, write or update a memory file. Use these type conventions:

TypeWhat belongsExamples
userUser's role, goals, preferences, knowledge"senior Go dev, new to React"
feedbackCorrections AND confirmations from user"don't mock DB in tests", "bundled PR was right call"
projectOngoing work context, deadlines, initiatives"merge freeze 2026-03-05 for mobile release"
referencePointers to external systems"pipeline bugs in Linear project INGEST"

Each memory file uses this format:

markdown
---
name: {{name}}
description: {{one-line description}}
type: {{user, feedback, project, reference}}
---

{{content - for feedback/project: rule, then **Why:** and **How to apply:**}}

Focus on:

  • Merging new signal into existing topic files rather than creating near-duplicates
  • Converting relative dates ("yesterday", "last week") to absolute dates
  • Deleting contradicted facts - if today's investigation disproves an old memory, fix it at the source

Phase 4 - Prune and index

Update MEMORY.md so it stays under 200 lines AND under ~25KB. It's an index, not a dump:

  • Each entry: one line under ~150 chars: - [Title](file.md) - one-line hook
  • Never write memory content directly into MEMORY.md
  • Remove pointers to stale, wrong, or superseded memories
  • Shorten verbose entries - move detail to the topic file
  • Add pointers to newly important memories
  • Resolve contradictions - if two files disagree, fix the wrong one

What NOT to save

  • Code patterns, architecture, file paths - derivable from the project
  • Git history - git log / git blame are authoritative
  • Debugging solutions - the fix is in the code
  • Anything already in CLAUDE.md
  • Ephemeral task details or conversation context

Return a brief summary of what you consolidated, updated, or pruned. If nothing changed, say so.

Frequently asked questions

What does the Dream AI skill do?

Memory consolidation - review, merge, prune, and index memory files. Run periodically to keep memories organized and up-to-date.

Why use Dream on TypingMind?

Because you install it once and use it with any model. 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 Dream in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/sneg55/agent-starter/tree/main/skills/dream. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use 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 Dream?

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

Is the Dream AI skill free?

Yes. It is published on GitHub by sneg55 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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