Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add commands/cwensel/arcaneum/storegit clone --depth 1 https://github.com/cwensel/arcaneumWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/cwensel/arcaneum/store)<a href="https://agentmods.dev/commands/cwensel/arcaneum/store"><img src="https://agentmods.dev/badge/commands/cwensel/arcaneum/store.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00008 | $0.00700 |
| Opus 5 | $0.00004 | $0.00350 |
| Sonnet 5 | $0.00002 | $0.00140 |
| Haiku 4.5 | $0.00001 | $0.00070 |
Grade A, and why
store scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
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.
Tip: For full search capabilities (semantic + full-text), first create a corpus with
/arc:corpus create Memory --type markdown, then use this command with --collection Memory.
The stored content will be searchable via both /arc:search semantic and /arc:search text.
Storage Location: ~/.local/share/arcaneum/agent-memory/{collection}/
Options:
- --collection: Target collection (required)
- --model: Embedding model (default: arctic-m 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:
arc store $ARGUMENTS
How It Works:
- Accept content from file or stdin (
-) - Extract/add rich metadata (title, category, tags, custom fields)
- Semantic chunking preserving document structure
- Generate embeddings (arctic-m default: 768D for stable document retrieval)
- Upload to Qdrant with metadata
- Persist to disk:
~/.local/share/arcaneum/agent-memory/{collection}/{date}_{agent}_{slug}.md - 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:
- arctic-m (768D, stable FastEmbed document retrieval)
Related Commands:
- /arc:corpus create - Create corpus for dual search (recommended:
--type markdown) - /arc:collection create - Create collection for semantic search only
- /arc:search semantic - Search stored content semantically
- /arc:search text - Search stored content with full-text (requires corpus)
- /arc:index markdown - For indexing existing markdown directories (different use case)
Implementation:
- RDR-014: Markdown content indexing
- arcaneum-204: Direct injection persistence module
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 87 lines · 8 tokens per session scan A 2706f9ddbd45
store is a command published in the GitHub repository cwensel/arcaneum (7 stars, last pushed 7d ago), licensed MIT. It adds 8 tokens to every session and 700 once invoked, about $0.0000 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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