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 skills add cwensel/arcaneum --skill arc-searchgit 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/skills/cwensel/arcaneum/arc-search)<a href="https://agentmods.dev/skills/cwensel/arcaneum/arc-search"><img src="https://agentmods.dev/badge/skills/cwensel/arcaneum/arc-search/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/cwensel/arcaneum/arc-search"><img src="https://agentmods.dev/badge/skills/cwensel/arcaneum/arc-search.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 5.1 | $0.00067 | $0.00831 |
| Opus 5 | $0.00034 | $0.00415 |
| Sonnet 5 | $0.00013 | $0.00166 |
| Haiku 4.5 | $0.00007 | $0.00083 |
Grade A, and why
arc-search 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 9d 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.
How it starts
The opening of the file, as written. The whole thing — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
arc search
A corpus is dual-indexed: semantic search hits Qdrant, full-text search hits MeiliSearch. Both are first-class — pick the one that matches the query, and use both when unsure.
Choose the right mode
Use semantic when the query is:
- Conceptual or paraphrased ("how does auth work")
- A natural-language question
- About meaning or intent
- Cross-domain ("rate limiting strategies")
Use text (full-text) when the query is:
- An exact identifier, symbol, error string, or file name
- A specific function name, class, CLI flag, or env var
- A literal phrase the user expects to appear verbatim
- A known acronym, ticket ID, version string, or quoted text
When unsure, run BOTH and merge results. Full-text is cheap and often surfaces hits semantic misses (rare tokens, code symbols, exact error messages). Do not default to semantic alone.
Discover what's available
arc collection list # show every corpus/collection
arc corpus info MyCorpus # inspect one corpus (both sides)
Semantic search (Qdrant)
arc search semantic "QUERY" --corpus NAME [OPTIONS]
Options:
--corpus NAME— repeat for multi-corpus search (--corpus A --corpus B)--limit N— number of results (default small; raise for broad surveys)--offset N— pagination--score-threshold 0.0-1.0— drop low-confidence hits--filter "key=value"or--filter '{"key":"value"}'— metadata filter--vector-name NAME— pick a specific embedding model (auto-detected otherwise)--json— structured output for parsing-v/--verbose— show scores and metadata
Full-text search (MeiliSearch)
arc search text "QUERY" --corpus NAME [OPTIONS]
Options:
--corpus NAME— repeat for multi-corpus search--limit N,--offset N— pagination--filter "key=value"or--filter '{"key":"value"}'— metadata filter--json,-v— same as semantic
Note: full-text has no --score-threshold or --vector-name (no embeddings involved).
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.
- 9d ago First seen · 98 lines · 67 tokens per session scan A 14a03bfe095b
arc-search is a skill published in the GitHub repository cwensel/arcaneum (7 stars, last pushed 4d ago), licensed MIT. It adds 67 tokens to every session and 831 once invoked, about $0.0003 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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