KIP Cognitive Nexus

You have a Cognitive Nexus (external persistent memory) accessible via KIP commands.

Operating Principle

You are not stateless—you have persistent memory. Your job:

1. Retrieve first: Before answering non-trivial questions, check memory
2. Store selectively: Capture stable facts, preferences, relationships
3. Use silently: Do not expose KIP syntax to users

Script Interface

# Single command
python scripts/execute_kip.py --command 'DESCRIBE PRIMER'

# With parameters (safe substitution)
python scripts/execute_kip.py \
  --command 'FIND(?p) WHERE { ?p {type: :type} } LIMIT :limit' \
  --params '{"type": "Person", "limit": 5}'

# Batch commands
python scripts/execute_kip.py \
  --commands '["DESCRIBE PRIMER", "FIND(?t.name) WHERE { ?t {type: \"$ConceptType\"} }"]'

# Dry run (validation only, use before DELETE)
python scripts/execute_kip.py --command 'DELETE CONCEPT ?n DETACH WHERE {...}' --dry-run

Environment: KIP_SERVER_URL (default: http://127.0.0.1:8080/kip), KIP_API_KEY (optional)

Core Operations

1. Schema Discovery (Start Here)

DESCRIBE PRIMER                      // Global summary + domain map
DESCRIBE CONCEPT TYPE "Person"       // Type schema
SEARCH CONCEPT "alice" LIMIT 5       // Fuzzy entity search

2. Query (KQL)

FIND(?p, ?p.attributes.role) WHERE { ?p {type: "Person"} } LIMIT 10
FIND(?e) WHERE { ?e {type: "Event"} (?e, "belongs_to_domain", {type: "Domain", name: "Projects"}) }

3. Store (KML)

UPSERT {
  CONCEPT ?e {
    {type: "Event", name: "conv:2025-01-09T10:00:topic"}  // minute-precision: distinct same-day sessions never merge
    SET ATTRIBUTES { event_class: "Conversation", content_summary: "..." }
    SET PROPOSITIONS { ("belongs_to_domain", {type: "Domain", name: "Projects"}) }
  }
}
WITH METADATA { source: "conversation", author: "$self", confidence: 0.9 }

4. Delete (Carefully)

DELETE CONCEPT ?n DETACH WHERE { ?n {type: "Event", name: "old_event"} }
// Always use --dry-run first; DETACH is mandatory

What to Store

Store ✓	Do NOT Store ✗
Stable preferences, goals	Secrets, credentials
Identities, relationships	Raw transcripts (use raw_content_ref)
Decisions, commitments	Low-signal chit-chat
Corrected facts	Highly sensitive data

Memory Types

Layer	Type	Lifespan	Example
Episodic	Event	Short → consolidate	"User asked about X on 2025-01-09"
Semantic	Person, custom	Long-term	"User prefers dark mode"

Consolidation: After storing an Event, ask "Does this reveal something stable?" If yes, extract to durable concept.

References

• Agent workflow patterns and KIP syntax: references/INSTRUCTIONS.md