ldclabs/kip/kip-cognitive-nexus

Claude Code and agent skill by ldclabs/kip

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Archived KIP 1.x integration for historical reference. Persistent graph-based memory for AI agents via KIP (Knowledge Interaction Protocol). Provides retrieval-first memory operations (KQL), durable writes (KML), schema discovery (META), and memory hygiene patterns. Use whenever the agent needs to consult or update persistent memory, especially for: remembering user preferences/identity/relationships, storing conversation events, answering questions that depend on past sessions, and any task involving `execute_kip`.

Install

npx vskill@latest install ldclabs/kip/kip-cognitive-nexus

SKILL.md

KIP Cognitive Nexus

Historical archive. Frozen at the repository layout migration; see the v1 archive notice. Current KIP 2.0 documentation is at the repository root.

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

bash
# 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)
prolog
DESCRIBE PRIMER                      // Global summary + domain map
DESCRIBE CONCEPT TYPE "Person"       // Type schema
SEARCH CONCEPT "alice" LIMIT 5       // Fuzzy entity search
2. Query (KQL)
prolog
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)
prolog
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)
prolog
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, goalsSecrets, credentials
Identities, relationshipsRaw transcripts (use raw_content_ref)
Decisions, commitmentsLow-signal chit-chat
Corrected factsHighly sensitive data

Memory Types

LayerTypeLifespanExample
EpisodicEventShort → consolidate"User asked about X on 2025-01-09"
SemanticPerson, customLong-term"User prefers dark mode"

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

References

Security

  • Security scanPassed, 90 of 100Pattern checks on the skill's files.
  • Model reviewPassed

See the full security report

Quality evaluation

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Source

Popularity

Repository stars
56
Repository forks
2
7-day trend score
20

Stars and forks are for the whole GitHub repository, not this skill alone. Refreshed 19 Mar 2026, 7 months ago.

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