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 Hmbown/Wizards-of-the-Ghosts --skill arcanagit clone --depth 1 https://github.com/Hmbown/Wizards-of-the-GhostsWrote 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/hmbown/wizards-of-the-ghosts/arcana)<a href="https://agentmods.dev/skills/hmbown/wizards-of-the-ghosts/arcana"><img src="https://agentmods.dev/badge/skills/hmbown/wizards-of-the-ghosts/arcana/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/hmbown/wizards-of-the-ghosts/arcana"><img src="https://agentmods.dev/badge/skills/hmbown/wizards-of-the-ghosts/arcana.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00068 | $0.00673 |
| Opus 5 | $0.00034 | $0.00336 |
| Sonnet 5 | $0.00014 | $0.00135 |
| Haiku 4.5 | $0.00007 | $0.00067 |
Grade A, and why
arcana 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 — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Arcana
Apply deep technical knowledge to understand how systems work under the hood.
What This Skill Does
In D&D, Arcana is knowledge of magic, its traditions, symbols, and mechanisms. The real-world version is deep technical literacy: understanding how software architectures work, what protocols do at the wire level, how APIs behave beyond their documentation, and what the actual mechanisms are behind the abstractions everyone else takes on faith. In this grimoire, Arcana is treated as a metaphorical skill with a shipping-now delivery profile. Canonical reference input: Arcana (skill).
When To Use
- A technical system needs to be understood at a level deeper than its documentation provides.
- You need to explain how something actually works — not what it claims to do, but what it does.
- A debugging or architecture question requires knowledge of underlying mechanisms, not just surface APIs.
Prerequisites
- No extra runtime dependencies beyond Hermes Agent and the normal toolset for this session.
Procedure
- Restate the target, the success condition, and any no-touch boundaries before taking action.
- Identify the system, protocol, or technology to analyze.
- Explain the mechanism at the appropriate depth: not the marketing version, but how it actually works.
- Surface non-obvious implications, edge cases, or failure modes that follow from the mechanism.
- Deliver the explanation with a clear note on what is documented fact vs. observed behavior vs. inference.
- Package the result as the deliverables below, with confidence, assumptions, and unresolved risk called out explicitly.
Deliverables
- A mechanistic explanation of how the system actually works under the hood.
- Non-obvious implications or edge cases that follow from the mechanism.
- A confidence note: what is documented, what is observed, and what is inferred.
Pitfalls / Guardrails
- Keep the metaphor anchored to a real mechanism instead of drifting into lore.
- Do not confuse documentation with behavior. The docs say what it should do; arcana reveals what it does.
- Clearly separate established knowledge from speculation when reasoning about undocumented behavior.
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 · 67 lines · 68 tokens per session scan A 8dca1a039302
arcana is a skill published in the GitHub repository Hmbown/Wizards-of-the-Ghosts (107 stars, last pushed 5mo ago), licensed CC0-1.0. It adds 68 tokens to every session and 673 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 2026-09-03.
Other skills, from other repositories
linter-agent
Use when detecting and fixing code style violations, enforcing project conventions, ensuring consistent formatting.
perf-agent
Use when measure before optimizing, target actual bottlenecks proven by profiling, verify with benchmarks.
n8n-workflows
Use when n8n workflow automation — nodes, triggers, expressions, credentials, webhooks, error handling. Use when working with n8n workflows.
pipedream-workflows
Use when pipedream serverless workflows — triggers, code steps, pre-built actions, data stores, HTTP. Use when working with pipedream workflows.
error-handling-patterns
Master error handling patterns across languages including exceptions, Result types, error propagation, and graceful degradation to build resilient applications. Use when implementing error handling, designing APIs, or improving application reliability.
backend-implementation-review
Use when you need to review server-side implementation for correctness, maintainability, validation, and observability.