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commands/store.md

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+---
+description: Store agent-generated content for long-term memory
+argument-hint: <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:**
+
+```text
+/store analysis.md --collection Memory --title "Security Analysis" --category security
+/store - --collection Research --title "Findings" --tags "research,important"
+```
+
+**Execution:**
+
+```bash
+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