add-knowledge

name	add-knowledge
description	Add knowledge directly to the graph from free-form text input without requiring a document file. Supports Note, WebSource, Conversation, or any custom source type.
disable-model-invocation	false
argument-hint	["free-form text or URL"]
allowed-tools	Bash, Read

Add Knowledge

Add knowledge directly to the graph without requiring a document file. Claude analyzes the input, determines the appropriate source node type, extracts entities, and saves everything to the graph.

Usage

/add-knowledge "Acme Corp is a software company with 500 employees"
/add-knowledge "https://example.com/about — Acme Corp company overview"
/add-knowledge "Heard from a colleague that Acme Corp's revenue is 10B JPY"

Workflow

1. Analyze Input and Determine Source Type

Read the user's input $ARGUMENTS and classify into one of these source types:

Label	When to use	Key Properties
Note	Short memo, fact, research note, general knowledge	text, author (optional)
WebSource	URL-based information, web reference	url, title, reliability (optional: high/medium/low)
Conversation	Info heard from someone, meeting context	text, speaker, date (optional), context (optional)

Classification rules:

• Contains a URL (http:// or https://) → WebSource
• Mentions hearing from someone, meeting, discussion → Conversation (extract speaker name if present)
• Everything else → Note
• If none of the above fit well, propose a new label and confirm with the user

2. Extract Entities

Analyze the input text and extract entities and relationships, same as the /ingest entity extraction:

Entity types: PERSON, ORGANIZATION, TECHNOLOGY, REQUIREMENT, SCHEDULE, BUDGET, RISK, PROPOSAL_PATTERN, EVALUATION_CRITERIA, DELIVERABLE, SECURITY, DOMAIN, CONCEPT

Extraction rules:

• Extract concrete, specific entities (not generic terms)
• Normalize entity names (consistent casing, match existing graph entities)
• Each entity needs a brief description (1-2 sentences)

3. Check for Conflicts

Before saving, query existing entities for potential conflicts:

python -c "
import sys, json
sys.path.insert(0, '.')
from neo4j import GraphDatabase
from config import get_config
from scripts.save_entities import query_existing_entities
cfg = get_config()
driver = GraphDatabase.driver(cfg.neo4j_uri, auth=cfg.neo4j_auth)
names = [ENTITY_NAMES_LIST]
result = query_existing_entities(driver, names)
driver.close()
for name, info in result.items():
    print(json.dumps({'name': name, 'type': info['type'], 'description': info['description'], 'sources': info['sources']}, ensure_ascii=False))
"

If conflicts exist, present them to the user and resolve before saving.

4. Save to Graph

Pipe the structured JSON to add_knowledge.py:

cat <<'KNOWLEDGE_JSON' | python ${CLAUDE_SKILL_DIR}/../../../scripts/add_knowledge.py -p $1
{
  "source": {
    "label": "Note",
    "properties": {
      "text": "the input text",
      "author": "optional author"
    }
  },
  "entities": [
    {"name": "Entity Name", "type": "ORGANIZATION", "description": "Brief description"}
  ],
  "relationships": [
    {"source": "Entity A", "target": "Entity B", "type": "related", "description": "..."}
  ]
}
KNOWLEDGE_JSON

Property examples per type:

Note:

{"label": "Note", "properties": {"text": "...", "author": "user"}}

WebSource:

{"label": "WebSource", "properties": {"url": "https://...", "title": "Page title", "reliability": "high"}}

Conversation:

{"label": "Conversation", "properties": {"text": "...", "speaker": "John", "date": "2026-03-20", "context": "Weekly meeting"}}

5. Report Results

Report: source type created, entity count, relationship count, and any conflicts resolved.