grim:mem:dream-sequence
SkillDocs & knowledgeConsolidate and improve the memory system, the way sleep consolidates a day's experiences. Run this weekly, or after an intense stretch of work.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the grim:mem:dream-sequence skill
About this capability
Dream Memory Sequence: Run a periodic memory-consolidation review. Synthesize recent work and chat history, propose durable memory candidates, flag stale or duplicate memory for cleanup, and surface missing-context questions. Never edits memory without user confirmation.
What this skill tells your AI
The instructions your AI receives, as published by mindgoblinstudios/grim-tome in skills/mem/dream-sequence/SKILL.md and read by ahel’s review.
Purpose
Consolidate and improve the memory system, the way sleep consolidates a day's experiences. Run this weekly, or after an intense stretch of work.
Memory flows in one direction: memory/MEMORY.md is the fast-capture surface where corrections and preferences land mid-session, and the dream sequence is the consolidation pass. Durable items that have proven themselves get promoted out of memory into the right docs/ file (or AGENTS.md, per grim:dev:autodocs), then pruned from memory. Memory stays small; docs accumulate the distilled knowledge.
Inputs
Review before summarizing:
memory/MEMORY.md(and the project's memory system docs, if any)docs/README.mdand any recently changed docs- Recent chat or session history from the last 7 to 14 days, when the harness makes it available
Consolidation Pass
- Revisit the previous consolidation candidates using existing run notes. Check which were applied, superseded, declined, or still need a decision before proposing them again.
- Distill what changed in understanding: a durable lesson, a corrected belief, the intent behind a decision, or missing context that matters next time.
- Check candidates against their canonical home for contradictions and duplication. Preserve sources and uncertainty where they matter, and make the useful context easy to retrieve.
- Reuse relevant evidence and refresh changed or consequential facts. Keep broader cleanup and system experiments with their existing owner unless they affect memory's accuracy or usefulness.
- Keep declined ideas retired unless the user reopens them. Do not invent candidates to fill a report; stay quiet when there is no meaningful update or action.
Report
Return a report with:
- A synthesis of what changed since the previous pass, highlighting the most important things to remember
- Durable memory candidates, to be added to memory
- Promotion candidates: memory entries that have matured into doc material, with the target
docs/file for each (prune from memory once promoted) - Stale, duplicate, or low-signal memory cleanup suggestions
- The top 3 missing-context questions that would help the system better understand the user's life, goals, and projects
- Suggested next steps
Rules
- Do not silently edit any docs, skills, memory files, commits, or external systems until the user confirms.
- Distill, don't transcribe: one bullet per durable fact. Preserve intent and distinguish observations from decisions.
- Prefer correcting an existing memory entry over adding a near-duplicate.
Signals
- GitHub stars
- 20
- Last commit
- Sep 2026
Others that do the same job
Advanced
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grim-mem-dream-sequence- Source
- github.com/mindgoblinstudios/grim-tome