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/modelsgit 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 | $0.00003 | $0.00938 |
| Opus 5 | $0.00002 | $0.00469 |
| Sonnet 5 | $0.00001 | $0.00188 |
| Haiku 4.5 | $0.00000 | $0.00094 |
Grade A, and why
models 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 and view available embedding models for vector search.
IMPORTANT: You must specify a subcommand (currently only list is available).
Subcommands (required):
list: List all available embedding models with details
Options:
- --json: Output in JSON format
Examples:
/models list
/models list --json
Execution:
arc models $ARGUMENTS
Available Models:
The list command shows:
- Model name (for --model flags)
- Dimensions (vector size)
- Backend (fastembed, sentence-transformers)
- Best use case (PDFs, code, general)
- Model ID (HuggingFace identifier)
Current Models:
For Documents/PDFs:
- arctic-m (768D): DEFAULT - stable FastEmbed retrieval model
- stella (1024D): Highest-quality opt-in document model, requires
arcaneum[sentence-transformers] - mxbai-large (1024D): High-quality FastEmbed document model
- bge-large (1024D): Legacy BGE document model
For Source Code:
- jina-code (768D): DEFAULT - stable FastEmbed code model
- jina-code-st (768D): Legacy SentenceTransformers code path, requires
arcaneum[sentence-transformers] - jina-code-0.5b (896D): Higher-quality opt-in code model, 32K context, requires
arcaneum[sentence-transformers] - jina-code-1.5b (1536D): SOTA Sept 2025, 32K context, highest quality, requires
arcaneum[sentence-transformers] - codesage-large (1024D): CodeSage V2, Dec 2024, 9 languages, requires
arcaneum[sentence-transformers] - nomic-code (3584D): 7B params, 6 languages, slower but comprehensive, requires
arcaneum[sentence-transformers]
For General Use:
- bge (1024D): High-quality general embeddings
- bge-small (384D): Faster, smaller, lower quality
Model Selection Tips:
-
Match content type:
- PDFs/docs → arctic-m (stable default), stella (quality), or mxbai-large (FastEmbed quality)
- Source code → jina-code (stable FastEmbed default), jina-code-0.5b or jina-code-1.5b (quality extra)
- Mixed → arctic-m or mxbai-large
-
Consider dimensions:
- Higher dimensions (1024D) = better quality, more storage
- Lower dimensions (384D, 768D) = faster, less storage
-
Backend matters:
- fastembed: Faster, optimized, limited models
- sentence-transformers: More models, HuggingFace ecosystem
-
Collection consistency:
- Use same model for all documents in a collection
- Cannot mix dimensions in one vector space
Downloading Models:
Models auto-download on first use. FastEmbed defaults are part of the core install;
SentenceTransformers models require arcaneum[sentence-transformers].
- Cached in
~/.cache/arcaneum/models(XDG-compliant) - Reused across indexing operations
- Use --offline flag to require cached models
Pre-download for offline use:
# Download an opt-in higher-quality code model
python -c "from sentence_transformers import SentenceTransformer; SentenceTransformer('jinaai/jina-code-embeddings-0.5b')"
# Or the legacy v2 SentenceTransformers model
python -c "from sentence_transformers import SentenceTransformer; SentenceTransformer('jinaai/jina-embeddings-v2-base-code')"
Related Commands:
- /collection create - Create collection with specific model
- /index pdf - Index with model selection
- /index code - Index with model selection
Implementation:
- RDR-002: Embedding client architecture
- RDR-006: Model listing CLI
- arcaneum-142: Multi-backend support
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 · 113 lines · 3 tokens per session scan A 94df3c72e3d4
models is a command published in the GitHub repository cwensel/arcaneum (7 stars, last pushed 6d ago), licensed MIT. It adds 3 tokens to every session and 938 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
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.