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/configgit 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/config)<a href="https://agentmods.dev/commands/cwensel/arcaneum/config"><img src="https://agentmods.dev/badge/commands/cwensel/arcaneum/config.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 | $0.00004 | $0.00276 |
| Opus 5 | $0.00002 | $0.00138 |
| Sonnet 5 | $0.00001 | $0.00055 |
| Haiku 4.5 | $0.00000 | $0.00028 |
Grade A, and why
config 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
Manage Arcaneum configuration and model cache.
Subcommands:
- show-cache-dir: Display cache locations and sizes
- clear-cache: Clear model cache to free disk space
Arguments:
- --confirm: Confirm cache deletion (required for clear-cache)
- --json: Emit a machine-readable JSON envelope on stdout
Examples:
/config show-cache-dir
/config show-cache-dir --json
/config clear-cache --confirm
Execution:
arc config $ARGUMENTS
Note: The model cache stores downloaded embedding models in XDG-compliant locations. First-time indexing downloads ~1-2GB of models which are then reused. I'll show you:
- Current cache directory locations
- Size of each directory
- Free disk space information
Use clear-cache when models are corrupted or to free disk space (models will be re-downloaded on next use).
Directory Locations (XDG-compliant):
- Models (cache):
~/.cache/arcaneum/models - Qdrant data: Docker volume
qdrant-arcaneum-storage
Related:
- Implemented in arcaneum-157 and arcaneum-162
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 · 50 lines · 4 tokens per session scan A 685366f348cd
config is a command published in the GitHub repository cwensel/arcaneum (7 stars, last pushed 8d ago), licensed MIT. It adds 4 tokens to every session and 276 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.
Other commands, from other repositories
minutes-ideas
Surface recent voice memos and ideas captured from any device. Use when the user asks "what ideas did I have?", "what were my recent memos?", "what did I record while walking?", or wants to recall a captured thought.
learn
Force claude-smart to extract learnings from this session now.
session-end
I'll summarize this coding session and update the memory system with our accomplishments.
memory-store
Store an insight, decision, or pattern to memory.
cc-memory
Configure persistent memory that survives across sessions using a layered approach: split rule files for always-loaded context, auto-memory for organic learning, and optional MCP-backed long-term memory for large codebases.
neo-review
Get Neo's code review with semantic matching against past findings in memory. Use on a diff or module before merge, especially where earlier mistakes in this codebase are likely to recur. Skip for formatting, lint-catchable issues, and single-line changes.