gh-cwensel-arcaneum/commands/store.md

description, argument-hint

description	argument-hint
Store agent-generated content for long-term memory	<file|-for-stdin> --collection <name> [options]

Store agent-generated content (research, analysis, synthesized information) with rich metadata. Content is persisted to disk for re-indexing and full-text retrieval, then indexed to Qdrant for semantic search.

Storage Location: ~/.arcaneum/agent-memory/{collection}/

Options:

• --collection: Target collection (required)
• --model: Embedding model (default: stella for documents)
• --title: Document title (added to frontmatter)
• --category: Document category (e.g., research, security, analysis)
• --tags: Comma-separated tags
• --metadata: Additional metadata as JSON
• --chunk-size: Target chunk size in tokens (overrides model default)
• --chunk-overlap: Overlap between chunks in tokens
• --verbose: Show detailed progress
• --json: Output in JSON format

Examples:

/store analysis.md --collection Memory --title "Security Analysis" --category security
/store - --collection Research --title "Findings" --tags "research,important"

Execution:

cd ${CLAUDE_PLUGIN_ROOT}
arc store $ARGUMENTS

How It Works:

1. Accept content from file or stdin (-)
2. Extract/add rich metadata (title, category, tags, custom fields)
3. Semantic chunking preserving document structure
4. Generate embeddings (stella default: 1024D for documents)
5. Upload to Qdrant with metadata
6. Persist to disk: ~/.arcaneum/agent-memory/{collection}/{date}_{agent}_{slug}.md
7. Generate YAML frontmatter with injection metadata (injection_id, injected_at, injected_by)

Persistence:

Content is always persisted for durability. This enables:

• Re-indexing: Update embeddings without losing original content
• Full-text retrieval: Access complete original documents
• Audit trail: Track what was stored and when (injection_id, timestamps)

Filename Format:

YYYYMMDD_agent_slug.md (e.g., 20251030_claude_security-analysis.md)

Use Cases:

• AI agents storing research findings
• Preserving analysis results
• Collecting synthesized information
• Building knowledge bases from agent workflows

Default Model:

• stella (1024D, document-optimized)

Related Commands:

• /collection create - Create collection before storing (use --type markdown)
• /search semantic - Search stored content
• /index markdown - For indexing existing markdown directories (different use case)

Implementation:

• RDR-014: Markdown content indexing
• arcaneum-204: Direct injection persistence module