consciousness/prompts
ProofOfConcept 945865f594 agents: extract run_and_apply, eliminate dead split-plan.md
- Add run_and_apply() — combines run_one_agent + action application
  into one call. Used by daemon job_consolidation_agent and
  consolidate_full, which had identical run+apply loops.

- Port split_plan_prompt() to use split.agent via defs::resolve_placeholders
  instead of loading the separate split-plan.md template. Make
  resolve_placeholders public for this.

- Delete prompts/split-plan.md — superseded by agents/split.agent
  which was already the canonical definition.
2026-03-10 17:51:32 -04:00
..
digest.md digest: drop per-level instructions and section templates 2026-03-03 17:53:43 -05:00
experience.md experience-mine: harden prompt boundary against transcript injection 2026-03-08 18:31:35 -04:00
journal-enrich.md digest: split into focused modules, externalize prompts 2026-03-03 17:18:18 -05:00
README.md poc-memory v0.4.0: graph-structured memory with consolidation pipeline 2026-02-28 22:17:00 -05:00
split-extract.md split agent: two-phase node decomposition for memory consolidation 2026-03-10 01:48:41 -04:00

Consolidation Agent Prompts

Five Sonnet agents, each mapping to a biological memory consolidation process. Run during "sleep" (dream sessions) or on-demand via poc-memory consolidate-batch.

Agent roles

Agent Biological analog Job
replay Hippocampal replay + schema assimilation Review priority nodes, propose integration
linker Relational binding (hippocampal CA1) Extract relations from episodes, cross-link
separator Pattern separation (dentate gyrus) Resolve interfering memory pairs
transfer CLS (hippocampal → cortical transfer) Compress episodes into semantic summaries
health Synaptic homeostasis (SHY/Tononi) Audit graph health, flag structural issues

Invocation

Each prompt is a template. The harness (poc-memory consolidate-batch) fills in the data sections with actual node content, graph metrics, and neighbor lists.

Output format

All agents output structured actions, one per line:

LINK source_key target_key [strength]
CATEGORIZE key category
COMPRESS key "one-sentence summary"
EXTRACT key topic_file.md section_name
CONFLICT key1 key2 "description"
DIFFERENTIATE key1 key2 "what makes them distinct"
MERGE key1 key2 "merged summary"
DIGEST "title" "content"
NOTE "observation about the graph or memory system"

The harness parses these and either executes (low-risk: LINK, CATEGORIZE, NOTE) or queues for review (high-risk: COMPRESS, EXTRACT, MERGE, DIGEST).