Knowledge Operations

Manage a multi-layered knowledge system for ingesting, organizing, syncing, vectorizing, and retrieving knowledge across multiple stores.

Usage Template

Prompt

Use knowledge-ops to organize this knowledge base. Deduplicate, classify, sync, and prepare semantic retrieval.

Use Case

• Scaling from a small wiki to a multi-layer knowledge system with search, memory, and vector storage.

Expected Result

• The agent proposes or runs organization, deduplication, vector sync, and retrieval steps.

Output Example

• A sync report listing indexed notes, duplicates, skipped files, merge candidates, and retrieval test queries.

Verification Case

• The output reports what was indexed, skipped, merged, or flagged for manual review.

Verified Effect

• A growing wiki becomes searchable, deduplicated, and ready for semantic retrieval.

When to Use

• User wants to "save this to my knowledge base"
• Syncing knowledge across systems (wiki, memory, vector DB, git repos)
• Deduplicating or organizing existing knowledge
• User asks "what do I know about X?" (semantic search)
• User says "sync", "organize", "deduplicate", "vectorize"
• Knowledge base is growing beyond ~300 nodes and manual navigation is slowing down

Knowledge Layers

Layer 1: Active Execution Truth

• GitHub issues, PRs, Linear tasks — current operational state
• Rule: if it affects an active plan or release, put it here first

Layer 2: Quick Access Memory

• Agent-specific memory files (e.g., ~/.claude/projects/*/memory/)
• Markdown with frontmatter — user preferences, feedback, project context
• Automatically loaded at session start

Layer 3: Durable Wiki

• Curated, interlinked wiki pages (concepts, entities, outputs)
• The canonical store for long-term knowledge
• Cross-referenced with [[wikilinks]]

Layer 4: Vector Store (RAG) ⭐

• ChromaDB (local, no API key needed) or any vector DB
• Enables semantic search across hundreds of nodes
• Embeddings generated locally via sentence-transformers (free)
• Automatic sync with wiki on every ingest

Vector Store Setup (Local, Free)

Recommended Stack

Quick Start

pip install chromadb sentence-transformers watchdog

Sync Script Structure

# sync_wiki_to_vector.py — run on ingest or periodically

from sentence_transformers import SentenceTransformer
import chromadb
import os, glob, hashlib

# Initialize
model = SentenceTransformer('all-MiniLM-L6-v2')
client = chromadb.PersistentClient(path="./vector_store")
collection = client.get_or_create_collection("wiki")

def sync_wiki():
    files = glob.glob("wiki/**/*.md", recursive=True)
    for f in files:
        with open(f, "r") as fh:
            content = fh.read()
        doc_id = hashlib.sha256(f.encode()).hexdigest()[:16]
        # Check if already indexed (by hash)
        existing = collection.get(ids=[doc_id])
        if existing["ids"]:
            continue
        # Embed and store
        embedding = model.encode(content[:5000]).tolist()
        collection.add(
            documents=[content[:5000]],
            embeddings=[embedding],
            ids=[doc_id],
            metadatas=[{"path": f, "updated": os.path.getmtime(f)}]
        )

def semantic_search(query, k=5):
    q_embedding = model.encode(query).tolist()
    results = collection.query(query_embeddings=[q_embedding], n_results=k)
    return results

Auto-Sync with Watchdog

# auto_watch.py — runs in background
from watchdog.observers import Observer
from watchdog.events import FileSystemEventHandler

class WikiSyncHandler(FileSystemEventHandler):
    def on_modified(self, event):
        if event.src_path.endswith(".md"):
            sync_wiki()  # re-index changed file

observer = Observer()
observer.schedule(WikiSyncHandler(), path="./wiki", recursive=True)
observer.start()

Ingestion Workflow

1. Classify

2. Deduplicate

• Search existing knowledge before creating
• Check wiki concepts, entities, and memory for duplicates
• Vector search across knowledge base for semantic duplicates

3. Store

• Write to the appropriate layer
• If wiki page created/updated → trigger vector sync (auto or manual)
• Update summaries and indexes
• Cross-reference with [[wikilinks]]

4. Retrieve (Semantic Search)

When user asks "what do I know about X":

1. Encode query with sentence-transformers
2. Search ChromaDB for top-5 matches
3. Return matched wiki paths + relevance scores
4. Load top-3 files into context (respecting token budget)

5. Verify

• Confirm stored knowledge can be retrieved
• Check that links resolve correctly
• Verify vector search returns relevant results

Quality Gates

• Knowledge classified to correct layer
• No duplicates created (searched first + vector checked)
• Vector store in sync with wiki (auto or batch)
• Index/summary updated
• Cross-references added
• Verification: knowledge is retrievable (file + vector)