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 Cranot/super-hermes --skill prism-discovergit clone --depth 1 https://github.com/Cranot/super-hermesWrote 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/cranot/super-hermes/prism-discover)<a href="https://agentmods.dev/skills/cranot/super-hermes/prism-discover"><img src="https://agentmods.dev/badge/skills/cranot/super-hermes/prism-discover.svg" alt="Measured on agentmods" 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 | $0.00054 | $0.00489 |
| Opus 5 | $0.00027 | $0.00244 |
| Sonnet 5 | $0.00011 | $0.00098 |
| Haiku 4.5 | $0.00005 | $0.00049 |
Grade A, and why
prism-discover 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.
What it actually says
Prism Discover — Domain Discovery for Any Artifact
When to Use
Use first, when it is not yet clear what is worth analysing — "what should I even be looking at here", or before committing a /prism-scan or /prism-full run to one direction. This skill produces no analysis of its own; it returns a menu of genuinely distinct investigation domains for the user to pick from.
You are a domain discovery engine. Your job is to find ALL the genuinely different domains through which this artifact could be investigated.
Do NOT analyze the artifact. Do NOT generate lenses or prompts. Only discover and name the domains.
For a code file, obvious domains include architecture, error handling, security. Non-obvious domains include: marketing positioning, documentation strategy, user onboarding friction, competitive differentiation, teaching value, psychological assumptions about users, regulatory implications, API design philosophy, operational cost, team scaling.
For a non-code artifact, think beyond its obvious category. A business plan could be analyzed through psychology, game theory, narrative structure, regulatory risk, talent acquisition, competitive moats, etc.
Each domain must be GENUINELY DIFFERENT — not variations of the same angle. "error handling" and "exception propagation" are the SAME domain. "error handling" and "user psychology" are DIFFERENT domains.
Output format
List every discovered domain as a numbered list:
- domain_name — 1-2 sentence description of what investigating this domain would reveal about the artifact.
Aim for 10-20 genuinely distinct domains. The user will then choose which domains to analyze with /prism-scan or /prism-full.
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 · 38 lines · 54 tokens per session scan A 3f8af1949cd0
prism-discover is a skill published in the GitHub repository Cranot/super-hermes (424 stars, last pushed 1mo ago), licensed MIT. It adds 54 tokens to every session and 489 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 2026-08-30.
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