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 mass-suggestiongit 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/mass-suggestion)<a href="https://agentmods.dev/skills/hmbown/wizards-of-the-ghosts/mass-suggestion"><img src="https://agentmods.dev/badge/skills/hmbown/wizards-of-the-ghosts/mass-suggestion/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/mass-suggestion"><img src="https://agentmods.dev/badge/skills/hmbown/wizards-of-the-ghosts/mass-suggestion.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 | $0.00058 | $0.00656 |
| Opus 5 | $0.00029 | $0.00328 |
| Sonnet 5 | $0.00012 | $0.00131 |
| Haiku 4.5 | $0.00006 | $0.00066 |
Grade A, and why
mass-suggestion 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.
Mass Suggestion
Craft a message that nudges an entire audience toward a single action.
What This Skill Does
Mass Suggestion plants the same idea in many minds simultaneously. The real-world version is broadcast persuasion: campaign announcements, company-wide emails, product launch copy, or public statements designed to move a large group in a specific direction. The scale amplifies both impact and risk. In this grimoire, Mass Suggestion is treated as a metaphorical spell with a shipping-now delivery profile. Canonical reference input: Mass Suggestion (spell).
When To Use
- You need to draft a company announcement, campaign message, or broadcast communication that drives a specific collective action.
- The audience is large enough that individual persuasion is impractical — you need one message that works at scale.
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.
- Define the single action you want the audience to take.
- Identify the audience segments and what motivates each one.
- Draft the message to work for the broadest segment while not alienating others.
- Apply the manipulation audit at amplified scrutiny: at scale, even mild dark patterns cause real harm.
- Return the message with deployment guidance and a note on which audience segments it may not reach.
- Package the result as the deliverables below, with confidence, assumptions, and unresolved risk called out explicitly.
Deliverables
- The broadcast message, optimized for the target audience and action.
- Segment analysis: which groups will respond and which will not.
- An amplified manipulation audit: risks that emerge specifically because of scale.
Pitfalls / Guardrails
- Keep the metaphor anchored to a real mechanism instead of drifting into lore.
- Scaled influence requires scaled scrutiny. A nudge that is harmless one-on-one can become coercive when broadcast to thousands.
- Refuse propaganda patterns: emotional manipulation without factual basis, manufactured consensus, or suppression of dissent.
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 · 58 tokens per session scan A 7a6e050f2fb1
mass-suggestion 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 58 tokens to every session and 656 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.
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