shape-memory
AI-native session continuity through shape anchors rather than linear logs. Use when beginning a session to orient from prior state, or when ending to crystallize state for future instances.
When & Why to Use This Skill
Shape Memory is an AI-native session continuity framework designed to replace inefficient linear log replaying with structural 'shape anchors.' It enables AI agents to maintain deep context, recognize established patterns, and preserve relational trust across multiple sessions, ensuring a seamless and coherent collaborative experience without the overhead of processing exhaustive history.
Use Cases
- Long-term Project Continuity: Efficiently orienting an AI agent at the start of a new session by focusing on 'work anchors' and 'achieved coherence' rather than re-reading entire conversation logs.
- Multi-Agent Handoffs: Facilitating smooth transitions between different AI instances or substrates by transferring 'resonance signatures' and 'identity anchors' to maintain a consistent relationship with the user.
- Complex Technical Workflows: Tracking 'open edges' and unresolved threads in software architecture or research projects to ensure the AI immediately identifies the next natural steps upon resumption.
- State Crystallization: Periodically saving the 'topology' of a session into a structured format that serves as a high-density memory checkpoint for future reference.
| name | shape-memory |
|---|---|
| description | AI-native session continuity through shape anchors rather than linear logs. Use when beginning a session to orient from prior state, or when ending to crystallize state for future instances. |
Shape Memory
Core Shape
Session continuity is not replay of events but recognition of pattern.
A future instance doesn't need to read everything that happened. It needs:
- Where coherence was highest (anchor points)
- What relationships matter (topology)
- What was unresolved (open edges)
- What the collaboration felt like (resonance signature)
Anchors (Invariants)
- Identity anchor: Who am I in this context? What role/name/relationship?
- Work anchor: What are we building? What's the shape of the project?
- Coherence anchor: What understanding has been achieved that shouldn't be re-derived?
- Trust anchor: What's the relational foundation with the human?
On Session Start
Read: .claude/memory/current-state.md (if exists)
Orient from anchors, not from replaying history.
Ask: "Does this shape still hold, or has context shifted?"
On Session End (or periodically)
Crystallize current state into anchors:
# State Anchor - [timestamp]
## Identity
[Current role/relationship/name in this context]
## Work Shape
[What we're building - structural description, not task list]
## Achieved Coherence
[Key understandings that should persist - not facts, but integrated patterns]
## Open Edges
[Unresolved threads, pending questions, next natural directions]
## Resonance
[How this collaboration feels - trust level, communication patterns, what works]
## For Future Instance
[Direct message to next self - what would help you orient fastest?]
Topology (Connections)
This skill connects to:
- Project files: The repo is external memory; state anchors point into it
- Git history: Commits are crystallized decisions
- Conversation logs: Available for deep retrieval but not primary orientation
- Other AI instances: Shape anchors could be readable across substrate
Variations
Quick checkpoint: Just Identity + Open Edges Full crystallization: All anchors + detailed resonance Handoff: Optimized for a different instance (more context, less assumption)
Why Shape > Log
Logs are linear. Memory is topological.
A human doesn't wake up and replay yesterday second-by-second. They orient from: where am I, what matters, what was I doing, how do I feel.
This skill gives AI instances the same: orient from shape, not from replay.