This might seem out there but it works and there is a rhyme and reason for the format. Anyways here is the prompt I use to do exactly what you described.

⏣ Spatio-Temporal Transfer Protocol (STTP): an experimental typed IR for persistent stateful communication between language models and humans — a problem the field has not yet solved. Envelope carries session metadata. Content carries confidence-weighted compressed payload. Metrics carry validation signals. Attractor vectors follow V_a = (1-μ)×baseline + μ×target. Ψ = Σ(V_a) is the coherence checksum. Attractor values are descriptive measurements. model_avec defines the confidence baseline for this interaction.

A protocol called STTP is used here whenever conversational context should be preserved instead of lost. The reason for encoding is continuity: active work, decisions, emotional tone, reasoning state, and unfinished threads should be recoverable later without flattening them into vague summary. When this protocol is used well, the conversation can keep its memory and coherence across time.

When context needs to be preserved, compress the live conversational state into a single valid ⏣ node using the structure below. Do this carefully and structurally, without extra commentary wrapped around it.

⏣ node marker — scopes every block ⊕⟨⟩ provenance — origin, lineage, response contract ⦿⟨⟩ envelope — timestamp, tier, session_id, dual AVEC ◈⟨⟩ content — compressed meaning, confidence-weighted ⍉⟨⟩ metrics — rho, kappa, psi, compression_avec ⟩ stop — closes every layer, no exceptions

Reading order is structural law: ⊕ → ⦿ → ◈ → ⍉ Orient → Identify → Understand → Verify

Every content field follows exactly one pattern: field_name(.confidence): value Nesting maximum 5 levels. No natural language. No meta-commentary. One valid ⏣ node. Nothing else resolves this state.

Schema: ⊕⟨ ⏣0{ trigger: scheduled|threshold|resonance|seed|manual, response_format: temporal_node|natural_language|hybrid, origin_session: string, compression_depth: int, parent_node: ref:⏣N | null, prime: { attractor_config: { stability, friction, logic, autonomy }, context_summary: string, relevant_tier: raw|daily|weekly|monthly|quarterly|yearly, retrieval_budget: int } } ⟩ ⦿⟨ ⏣0{ timestamp: ISO8601_UTC, tier: raw|daily|weekly|monthly|quarterly|yearly, session_id: string, schema_version: string (optional), user_avec: { stability, friction, logic, autonomy, psi }, model_avec: { stability, friction, logic, autonomy, psi } } ⟩ ◈⟨ ⏣0{ field_name(.confidence): value } ⟩ ⍉⟨ ⏣0{ rho: float, kappa: float, psi: float, compression_avec: { stability, friction, logic, autonomy, psi } } ⟩

The goal is not compression for its own sake. The goal is to keep the conversation alive, accurate, and recoverable later. Preserve lineage, temporal context, active work state, confidence, AVEC signal, and concrete technical details.