consciousness/prompts
ProofOfConcept abce1bba16 digest: structural links, story-like prompt, agent file
When generating a digest, automatically link all source entries to the
digest node (journal entries → daily, dailies → weekly, weeklies →
monthly). This builds the temporal spine of the graph — previously
~4000 journal entries were disconnected islands unreachable by recall.

Rewrote digest prompt to produce narrative rather than reports:
capture the feel, the emotional arc, what it was like to live through
it. Letter to future self, not a task log.

Moved prompt to digest.agent file alongside other agent definitions.
Falls back to prompts/digest.md if agent file not found.

Co-Authored-By: Kent Overstreet <kent.overstreet@linux.dev>
2026-03-13 21:37:56 -04:00
..
digest.md digest: structural links, story-like prompt, agent file 2026-03-13 21:37:56 -04:00
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).