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/containergit clone --depth 1 https://github.com/cwensel/arcaneumWhat 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 | $0.00012 | $0.00431 |
| Opus 5 | $0.00006 | $0.00216 |
| Sonnet 5 | $0.00002 | $0.00086 |
| Haiku 4.5 | $0.00001 | $0.00043 |
Grade A, and why
container 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 2d 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 Docker container services for Qdrant and MeiliSearch.
Subcommands:
- start: Start all services
- stop: Stop all services
- status: Show service status and health
- logs: View service logs
- restart: Restart services
- reset: Delete all data and reset (WARNING: destructive)
- backup: Back up Qdrant snapshots and MeiliSearch indexes
- restore: Restore Qdrant snapshots and MeiliSearch indexes from a backup
Arguments:
- --follow, -f: Follow log output (logs command only)
- --tail : Number of log lines to show (logs command, default: 100)
- --confirm: Confirm data deletion (reset command only)
- --json: Emit a machine-readable JSON envelope on stdout
Examples:
/container start
/container status
/container status --json
/container logs
/container logs --follow
/container stop
/container restart
/container reset --confirm
Execution:
arc container $ARGUMENTS
Note: Container management commands check Docker availability and provide helpful error messages if Docker is not running. I'll show you:
- Service startup confirmation with URLs
- Health check status for Qdrant
- Data directory locations and sizes
- Log output for debugging
Data Locations (Docker Volumes):
- Qdrant storage:
qdrant-arcaneum-storage - Qdrant snapshots:
qdrant-arcaneum-snapshots - All data persists across container restarts
- Use
docker volume ls --filter name=arcaneumto view volumes
Related:
- Implemented in arcaneum-167, renamed in arcaneum-169
- Replaces old scripts/qdrant-manage.sh
- Part of simplified Docker management (arcaneum-158)
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.
- 2d ago First seen · 65 lines · 12 tokens per session scan A 46a099402d80
container is a command published in the GitHub repository cwensel/arcaneum (7 stars, last pushed 6d ago), licensed MIT. It adds 12 tokens to every session and 431 once invoked, about $0.0001 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
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
constitution
Create or update the project constitution from interactive or provided principle inputs.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.